Music Appreciation ch. 35-41 Essay Example

Music Appreciation ch. 35-41 Paper Which of the following choral genres was NOT developed during the Baroque? part song A musical setting of the Mass for the Dead is called: a Requiem Oratorios primarily drew their stories from: the Bible Mozarts Requiem was: his last work, incomplete at his death Who completed Mozarts Requiem? Sà ¼ssmayr The Dies irae text from the Requiem Mass describes: Judgement Day Which of the following correctly describes the musical forces for Mozarts Requiem? winds, brass, strings, timpani, choir, and four soloists The ________ accompanies the baritone voice in the Tuba mirum section of Mozarts Requiem. trombone Which of the following best describes the mood of the Dies irae from Mozarts Requiem? fearful and then wondering The text of Mozarts Requiem is sung in: Latin The German term for the art song is: Lied A song whose text is a short lyric poem in German with piano accompaniment is called a: Lied _______ was NOT an important composer of nineteenth-century Lieder. Heinrich Heine Which of the following was NOT a typical theme of Romantic poetry? praise of the Virgin Mary The favorite subjects of the Romantic poets were: love, longing, and nature A group of Lieder unified by a narrative thread or by a descriptive or expressive theme is called a(n): song cycle A song form in which the same melody is repeated for every stanza of text is called: strophic A song that is composed from beginning to end without repetition of whole sections is called: through composed A song form in which the main melody is repeated for two or three stanzas but introduces new or significantly varied material when the text requires it is called: modified strophic Schubert was born in: Vienna Schubert and his friends organized evening gatherings of artists, writers, and musicians, called: Schubertiads Schubert lived a tragically short life but was a remarkably prolific composer of: Lieder, chamber music, piano music (all of the above) In which genre was Schubert NOT indebted to Classical traditions? Lied Approximately how many songs did Schubert compose? more than 600 Schubert wrote several song cycles, including: Winters Journey Schuberts song Elfking is a setting of a ballad written by: Geothe Schuberts Lied Elfking is in ________ form. through-composed Which of the following is true of Schuberts Elfking? It is the masterpiece of his youth, It is based on a legend that whoever is touched by the king of the elves must die, It presents four characters who are differentiated in the music (all of the above) In Schuberts Elfking, the obsessive triplet rhythm of the piano accompaniment represents: the galloping of the horse Which musical devices does Schubert use to portray the childs terror in Elfking? high range and dissonance The composer who founded the New Journal of Music was: Robert Schumann Robert Schumanns wife, Clara, was: the daughter of his piano teacher, one of the foremost pianists of her day, the inspiration for A Poets Love (all of the above) Robert Schumann ended his career and life: in an asylum, the result of a mental illness Robert Schumanns A Poets Love is a: song cycle Robert Schumanns A Poets Love is set to texts by: Heinrich Heine Which of the following does NOT describe Schumanns A Poets Love? it tells a detailed story of a lost love Schumanns In the lovely month of May is from which song cycle? A Poets Love What is the form of In the lovely month of May? strophic Which of the following does NOT describe Schumanns In the lovely month of May? it ends with harmonic resolution Which of the following does NOT describe American popular music of the nineteenth century? the composers were always well known Which of the following describes music in America during the early nineteenth century? music was largely imported through Europe What is vernacular music? popular songs sung in a countrys native language What nationality was Stephen Foster? American Which nineteenth-century American composer is best remembered for his parlor songs and minstrel show tunes? Stephen Foster Which of the following best describes minstrelsy? shows that featured performers in blackface Stephen Foster composed all of the following songs EXCEPT: When Johnny Comes Marching Home Jeanie with the Light Brown Hair is: a parlor song The form of Fosters Jeanie with the Light Brown Hair is: strophic Fosters Jeanie with the Light Brown Hair is based on a poem by: Foster himself The most important keyboard instrument of the Romantic period was the: piano Which of the following does NOT characterize the piano? it is capable of only one dynamic level Which of the following instruments is capable of playing both melody and harmony? piano Which of the following was NOT a technical improvement to the nineteenth-century piano? a second keyboard was added The short, lyric piano piece is the instrumental equivalent of: the song During the nineteenth century, Prelude, Impromptu, and Intermezzo were common titles for: character pieces Nineteenth-century composers of the short, lyric piano piece included: Johannes Brahms, Frà ©dà ©ric Chopin, Robert Schumann (all of the above) Chopin is credited with developing the: modern piano style Which nineteenth-century composers entire output centered around the piano? Chopin Chopin spent his early years in: Poland Chopin spent most of his productive life in: Paris With which famous novelist did Chopin become romantically involved? George Sand Chopin composed works in all of the following genres EXCEPT the: symphony Which of the following does NOT characterize the music of Chopin? reserved emotions What is the origin of the mazurka? a Polish peasant dance Which of the following does NOT characterize Chopins Mazurka in B-flat Minor, Op. 24, No. 4? simple A-B-A form In connection with Chopins music, the term rubato means that the performer should: take liberties with the tempo Which composer is known as the poet of the piano? Frà ©dà ©ric Chopin Which of the following best describes the role of women in nineteenth-century music? the piano provided women with a socially acceptable performance outfit Which of the following was a noted woman composer of the Romantic era? Clara Schumann Which of the following women organized salons featuring music by her brother? Fanny Mendelssohn Hensel Fanny Mendelssohn Hensel was discouraged from pursuing a career as a composer because: she was a woman Fanny Mendelssohn Hensels output is dominated by: Lieder and piano music Which of the following composed the piano cycle The Year? Fanny Mendelssohn Hensel Fanny Mendelssohn Hensel wrote her cycle The Year for: piano The manuscript for Fanny Mendelssohn Hensels September: At the River, from The Year, has poetic lines by: Johann Wolfgang von Geothe Fanny Mendelssohn Hensels September: At the River, from The Year, is in ________ form. A-B-A How does Fanny Mendelssohn Hensels The Year reach a level of achievement beyond that of her brother Felix? it is a large-scale work unified by musical and extramusical links In which country was Franz Liszt born? Hungary Which composer is generally considered the greatest pianist and showman of the Romantic era? Liszt Liszt was inspired by the virtuoso violinist: Paganini Which of the following was the first internationally acclaimed American composer of classical music? Louis Moreau Gottschalk Louis Moreau Gottschalk was born in: New Orleans Louis Moreau Gottschalk is best known for his ________. solo piano music Which of the following statements about Louis Moreau Gottschalk is NOT true? he spent most of his creative life in Europe Louis Moreau Gottschalk based many of his works on: South American and Caribbean songs Which of the following does NOT characterize Gottschalks The Banjo? limited range The familiar tune quoted near the end of Gottschalks work The Banjo is: Camptown Races Instrumental music endowed with literary, philosophical, or pictorial associations is called: program music Which of the following compositions is LEAST likely to be an example of program music? string quartet in B-flat major Music composed without literary or pictorial meanings is called absolute music A multimovement, programmatic work for orchestra is called a: program symphony Which of the following composers is considered the first great exponent of musical Romanticism in France? Berlioz Hector Berlioz was born and spent most of his career in: France Which of the following is NOT characteristic of the music of Berlioz? as is typical of French music, emotions are restrained Which of the following is NOT a work by Berlioz? Italian Symphony Berliozs Symphonie fantastique is an example of a: program symphony How many movements are in Berliozs Symphonie fantastique? five Which of the following inspired Berliozs Symphonie fantastique? the actress Harriet Smithson Which of the following is NOT true of Berliozs Symphonie fantastique? the program deals entirely with nature In Berliozs Symphonie fantastique, the idà ©e fixe: symbolizes the beloved, recurs as required by the literary program, unifies the five movements, which are diverse in character and mood (all of the above) In Berliozs Symphonie fantastique, what is the idà ©e fixe? the basic theme of the symphony, heard in the march movement The technique of altering a theme to give it a different character is often called: thematic transformation Which of the following does NOT characterize the March to the Scaffold from Berliozs Symphonie fantastique? dominance of the string instruments The Dies irae is: a chant from the Mass for the Dead The piano manufacturer in New York that made major improvements to the instrument was: Steinway Through which innovation did Theobald Boehm improve musical instruments? key mechanism for woodwinds What new instrument was developed in the nineteenth century? saxophone

Giving Back to the Environment essays

Giving Back to the Environment essays It is not simple to reduce the environmental impact of automobiles worldwide. It is hard to find a straightforward solution for making automobiles better for the environment without drastically increasing costs or cutting down on performance. the best way to deal with this problem is the Life-cycle approach. The goal of the life cycle approach at is to make vehicles that are more efficient and inexpensive. It embraces environmental performance and cost factors. The life cycle of an automobile begins with material production and concludes with retirement. The public is now conscious of environmental issues that have increased. However, the automobile industry as a whole must undertake this issue immediately. To paraphrase a segment of Richard Porters book Economics at the wheel, International automotive manufacturing is dominated by a fairly small number of large producers. The automobile industry is the leading manufacturing enterprise in the world. It is also one of the major industrial systems that use many resources. The automobile industries in Europe and the U.S. use approximately 46 million tons of material annually to produce 24 million vehicles. Today, a vehicle consists of approximately 15,000 parts. Steel, iron and plastic, and non-ferrous metal dominate automobile construction. They account for more than 80% of the material used in today's vehicles. (19-20) I first learned about the total life-cycle analysis of automobiles during a lecture in Chem. 112 (Chemistry in the news). My professor Don Shillady pointed me in the right direction to obtain sufficient information for this report. During Don Shilladys lecture, I learned the obtainment and processing of new resources that serve as input for automotive material cause environmental impacts and concerns as well. In addition, large amounts of energy are consumed in heating, cooling, and producing millions of tons of steel, aluminum, plast...

A List of Free Printable History Worksheets

A List of Free Printable History Worksheets Many different teaching approaches can bring history alive for your students. Add these printable history worksheets to your studies to reinforce your lessons and allow students to hone their knowledge of important historical events and people. President Abraham Lincoln Abraham Lincoln PrintablesUse word searches, vocabulary quizzes, crossword puzzles, and coloring pages to help students learn about Abraham Lincoln, the 16th president of the United States. Activities also teach about the Lincoln Boyhood National Memorial and the first lady from 1861 to 1865, Mary Todd Lincoln. Black History Month: Famous Firsts Black History Month PrintablesAt this link, teachers can find important background information about Black History Month in addition to worksheets and other activities focused on famous firsts among black Americans. The Famous Firsts Challenge, for example, has students match up a famous first for black Americans, such as the first African-American to go into space, with the correct name from a list of choices. Chinas Long and Ancient History Chinese History PrintablesWith a history spanning thousands of years, China is for many people the subject of a lifetime of study. While your students probably wont embark on such an endeavor, this link offers handouts to introduce your students to concepts related to Chinese culture and government. One handout also presents a number matching activity for students to learn how to count to 10 in Chinese. The American Civil War U.S. Civil War PrintablesAmericas Civil War might be the most studied and debated subject in U.S. history. Using the printables at this link, students can become more familiar with the names, places, and events that defined this crucial era for the American republic. Lewis and Clark and the American Frontier Lewis and Clark PrintablesExploration and expansion of the American frontier are essential elements to understanding the United States as a nation and a people. Meriwether Lewis and William Clark were hired to explore the Louisiana Territory that President Thomas Jefferson bought from the French. With the activities and worksheets at this link, students learn more about issues related to Lewis and Clark and their travels. Medieval Times Medieval Era PrintablesThe medieval era is a fascinating time for many students, with tales of knights and jousting as well as political and religious intrigue. Among the activities at this link is a detailed coloring sheet for learning all about a suit of armor. Also included is Medieval Times Theme Paper on which students can write a story, poem or essay about the period. New Seven Wonders of the World New 7 Wonders of the World PrintablesWith an announcement in July 2007, the world was introduced to the New Seven Wonders of the World. The Pyramids of Giza, the oldest and only Ancient Wonder still standing, is included as an honorary candidate. The printables here teach students about the Pyramids and the others: the Great Wall of China, the Taj Mahal, Machu Picchu, Chichen Itza, Christ the Redeemer, the Colosseum, and Petra. American Revolutionary War Revolutionary War PrintablesBy learning about the Revolutionary War students discover the actions and principles of the nations founders. With the activities at this link, students gain a good overview of vocabulary and names related to the Revolution, as well as particular events, such as the Surrender of Cornwallis and Paul Reveres Ride. Womens History Month (March) Womens History Month PrintablesMarch in the United States is National Womens History Month, which recognizes and celebrates womens contributions to Americas history, society, and culture. The printables at this link introduce many important women with significant historical legacies whose names students might not immediately know. These worksheets and activities will heighten students appreciation for the role of women in U.S. history. World War II Historical Timeline WWII History PrintablesStudents will use and expand their knowledge of World War II to complete the activities at this link, which include a crossword puzzle; spelling, alphabetizing and vocabulary sheets; and coloring pages.

Law Journal Essay Example | Topics and Well Written Essays - 2000 words

Law Journal - Essay Example A few of the abovementioned components are examined briefly below. Legislation is enacted by Parliament which contains two chambers – the House of Commons and the House of Lords. An Act of Parliament begins life as a bill, which is a proposed draft of an Act and passes through the various stages of the enactment process prior to becoming binding law. Delegated legislation as the name suggests is brought about in situations where the statute alone cannot provide for all the technicalities required. So it provides the broad framework whilst the details are filled in by the relevant minister by way of delegated legislation. These regulations when made in the approved manner are just as much law as the parent statute itself. (b) The English Legal System’s civil court structure consists of the European court of Justice, the House of Lords, the Court of Appeal, the Divisional Courts, the High Court, the County Court and the Magistrates Court. The system of Judicial Precedent in a nutshell would involve a court being bound by similar decisions made by courts of equal or higher status and is not merely a mechanical process of matching similarities and differences but involves the art of interpreting the principle derived from an earlier case. Decisions of the House of Lords bind all lower courts. After a protracted debate on whether or not House of Lord decisions binds future House of Lord’s cases the Practice Statement (Judicial Precedent) 1966 1 WLR 1234 established that though the doctrine of being bound had many commendable points â€Å"a too rigid adherence to precedent may lead to injustice in a particular case and also unduly restrict the proper development of the law†. However, the Lords depart from earlier decision only in rare circumstances. One such case is the case of British Railways Board Vs Herrington1 where the lords faced a number of earlier decisions wherein they had held that there was only a limited duty of care in neglig ence owed to children who trespassed onto property. Since perceptions of public policy have changed over the years their lordships felt able to ignore the earlier decisions and impose on British Railways a duty of care in keeping railway fences repaired. Strictly speaking the Court of Appeal is bound to follow all decisions of the House of Lords. There were some attempts by Lord Denning however to change this strict rule. He launched a two pronged attack by saying that (a) that if a House of Lords decision had been made per incuriam it could not be followed and (b) that if the reasoning for a rule had lapsed or seek to be of significance it need not be followed. These attempts were however not viewed favourably by the House of Lords and therefore the Court of Appeal is now bound to follow all House of Lords decisions. It may however choose between its own conflicting decisions. All courts that are lower in status than the Court of Appeal are bound by the doctrine of Judicial Precede nt in the normal way. Contract (a) An offer is a proposal by one person to another of certain terms of performance, which proposal is made with the intention that it be accepted by such other person. The promise of performance however is conditional upon a return promise or an act or forbearance being received in exchange for it for it to mature into a contract. An offer should be definite. Therefore a promise to pay a specified sum if a horse purchased were â€Å"

Professional Team Sports Essay Example | Topics and Well Written Essays - 2000 words

Professional Team Sports - Essay Example Making profits is a key aspect in any business venture (Wladimir and Stefan, 2006:617). This understanding has created interest in finding out the real effect of decisions made by club owners and franchise on the structure and regulation of leagues around the world. This paper therefore seeks to interrogate the different ways in which the objectives and decisions of club owners in professional sports affect the overall sporting arena. In order to get better sales in sports, high level of competition is required unlike in business where monopoly is the ultimate goal. If there are championships or leagues, the participation of more than two clubs will be necessary to ensure better products to the fans. If one club is far better than the rest and keeps on winning all games with ease, the products become so predictable and therefore less marketable to the fans (Wladimir & Stefan, 2006:27). Fans will get bored in watching a team that wins with big margins repeatedly and so need some degre e of uncertainty for them to enjoy watching the game. This phenomenon of the professional sports as an industry has led to the development of cooperation among clubs and the adoption of governing bodies charged with ensuring that the industry attains its optimal production capacity by way of organising championships and leagues. These leagues are highly competitive and as such have become some of the most profitable enterprises around the globe. For instance, the European champions’ league, the Barclays premier league in England and the La-liga of Spain are some examples among many leagues in football that are leading income earners for the respective clubs and contribute a considerable amount of the countries’ GDP. Baseball, basketball, indoor sports, golf, athletics, and Olympics in general all form a multibillion-dollar economy (Masteralexis and Hums, 2002:295). The graph below shows how revenue from sporting activities has increased over the years. Figure 1: graphi c illustration of increase in revenue in the sports sector associated with increasing commercialization Retrieved from http://www.econweb.com/MacroWelcome/sandd/D-Shift_New_Equilibrium.gif According to some economists, this feature of professional sport is quite favourable as it eradicates monopolies, which are responsible for poor quality of products or services offered and high non-commensurate prices. In the end, the whole arena of professional sports forms a model of free market where competitiveness of the product offered carries the largest share. This competition however is not always healthy especially with respect to the labour market (Stefan, 2007:47). Here, the free relocation and transfer of players from one club to the other based on the wages has made the wealthier clubs maintain a grip of the top leagues and championships over the less wealthy clubs. Therefore, wealthy club owners can get all the best talent there is in the market and thereby in a way kill competition , which is the very phenomenon on which the industry thrives (Rodney, 2004:25). This has resulted to creation of oligopolistic cartels where the higher level of game is exclusive to the rich clubs where as the less wealthy clubs play in the lower divisions that are less competitive and less famous among the fans ((Wladimir and Stefan, 2006:64). This means that fans will be flocking the gates only when big teams are playing. This obviously means very high revenues for them where as the poorer clubs will only have small number of fans in

Childhood Obesity Prevention and Intervention Essay Example for Free

Childhood Obesity Prevention and Intervention Essay â€Å"Childhood obesity has more than tripled in the last 30 years† (Centers for Disease Control and Prevention [CDC], 2008). That statistic is staggering. Data and surveys from the 1970’s to present suggest that the United States has been fighting obesity for a great deal of time and the battle continues, increasing in numbers and ever more alarming trends (Fals, 2009). Obesity has historically been treated as an adult problem, but the tripling of childhood obesity over the last three decades clearly illustrates that children are at exponentially higher risk, requiring a shift in focus. Immediate and continuing efforts are essential in the battle against childhood obesity. Prevention, education, and intervention require the involvement of not only affected children and their parents, but the public, government, and medical community as well. The government and society need to become involved in making prevention and intervention of childhood obesity a top priority. Family involvement is also critical; however, aid is needed to support and educate them. The First Lady, Michelle Obama, has been promoting a campaign (Let’s Move!) to raise awareness and help prevent childhood obesity (White House, 2011). The campaign’s checklists for parents and childcare providers contain numerous simple, but helpful tips like dietary changes, exercising, and restricting television and video game time. Similarly, We Can!  ® is a program offering many resources to parents, caregivers, and local community groups. Like the latter campaign, this program also places focus on diet, exercise, and media usage. The program partners nationally with a number of organizations and media outlets to ensure families in all parts of America have access to information and help (U.S. Department of Health Human Services [HHS], 2012). More programs like these are needed in communities and nationwide. The number of resources is increasing but not as quickly as the problem they are attempting to help. Strategies for childhood obesity intervention and prevention are surprisingly simple, but it is up to parents to implement them. One strategy is sharing meals as a family. Choosing healthy foods is not easy and parents can help by providing healthy meals and sharing them with their children. In addition, children often model the behavior of parents; therefore, a parent eating healthy foods may influence the child to do the same. Eating together has the added benefit of being an emotionally positive experience for the entire family. According to guidelines from the U.S. Department of Health and Human Services, food choices and physical activity are the most important factors in the obesity battle. Consequently, parents must offer healthy foods in addition to lowering the calories their children consume. Children must also be encouraged to exercise (HHS, 2010, p. 10). Finally, reduction of television, video games, and computer usage encourages children to be more active and gets them moving. Reducing screen time to no more than two hours per day is recommended by the American Academy of Pediatrics (AAP, 2003, p. 427). By implementing these strategies, parents will be able to help their children with making strides towards health. Research studies have determined health dangers faced by obese children are comparable to those of obese adults. Formerly adult-associated chronic diseases are striking children. These diseases (comorbidities) are numerous and may have grave consequences. Diabetes, high blood pressure, sleep apnea, and the metabolic syndrome are only a few examples of the diseases striking obese children (Daniels et al., 2005, p. 2002). Furthermore, additional research has determined that childhood and adolescent obesity can cause early death (Reilly Kelly, 2011, p. 894). The medical community must provide more research and education to help with prevention of these disease processes and better interventions. Researchers have stressed the â€Å"stigmatization of fatness,† with regard to societal views of this disease (Gard Wright, 2005, p. 69). Obese children are assumed to be lazy and unworthy of attention. Obese children suffer many psychological issues and the bullying is one of the contributors. A recent study reported, â€Å"Bullying happens every day† and it â€Å"has a direct impact on stress and trauma symptoms† (Brandt et al., 2012). Furthermore, â€Å"Children who are obese are more likely to be bullied† asserts another study (Lumeng et al., 2010). In general, obese children tend to be at risk for depression, anxiety disorders, social phobias, poor self-esteem, eating disorders, and a higher risk of suicidal tendencies. Psychiatric intervention is required to aid obese children in developing normally so these problems do not continue into adulthood. Individual support and support groups could be very effective interventions to consider. The epidemic of childhood obesity is not getting better. Every year it appears to be getting excessively worse. Realistic goals are to educate parents, healthcare providers, and the public, giving them as much research and information as possible. Education, intervention, and prevention are the most important factors for combating the effects of childhood obesity and in helping to ensure the health and happiness of children suffering from this disease. References American Academy of Pediatrics. (2003). Prevention of pediatric overweight and obesity. Pediatrics, 112(2), 427. Retrieved from http://www2.aap.org/obesity/ppt/PREVENTION%20OF%20PEDIATRIC%20OBESITY%20AAP.ppt 9k 2010-03-11 Brandt, A., Zaveri, K., Fernandez, K., Jondoh, L., Duran, E., Bell, L., . . . Gutierrez, J. (2012). School bullying hurts: Evidence of psychological and academic challenges among students with bullying histories. Undergraduate Research Journal for the Human Sciences: Special Edition, 11. Retrieved from http://www.kon.org/urc/v11/bullying/brandt.html Centers for Disease Control and Prevention. (2008). CDC Obesity Facts Adolescent and school health. Centers for Disease Control and Prevention. Retrieved from http://www.cdc.gov/healthyyouth/obesity/facts.htm Daniels, S., Arnett, D., Eckel, R., Gidding, S., Hayman, L., Kumanyika, S., . . . Robinson, T. (2005). Overweight in children and adolescents. Circulation, 111(15), 1999-2012. Fals, A. (2009). Childhood obesity : A bit of history National childhood obesity. Examiner. Retrieved from http://www.examiner.com/article/childhood-obesity-a-bit-of-history Gard, M., Wright, J. (2005). The obesity epidemic: Science, morality, and ideology (1st Ed.). New York, U.S.A.: Taylor Francis Inc. Lumeng, J., Forrest, P., Appugliese, D., Kaciroti, N., Corwyn, R., Bradley, R. (2010). Weight Status as a Predictor of Being Bullied in Third Through Sixth Grades. Journal of the American Academy of Pediatrics, 125(6), 1301-1307. doi:10.1542/peds.2009-0774. Reilly, J., Kelly, J. (2011). Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: Systematic review. International Journal of Obesity, 35(7), 891-898. doi:10.1038/ijo.2010.222.

Tony Kytes, The Arc-Deceiver by Thomas Hardy and Tickets, Please by D.H. Lawrence :: Hardy Kytes Tickets Lawrence Essays

"Tony Kytes, The Arc-Deceiver" by Thomas Hardy and "Tickets, Please" by D.H. Lawrence In this essay I will be discussing my views on the two short stories, "Tony Kytes, The Arc-Deceiver" by Thomas Hardy and "Tickets, Please" by D.H.Lawrence. "Tony Kytes, The Arch-Deceiver" was written in 1894, pre-first world war. "Tickets, Please" was written in 1922-24, post first world war. I am going to discuss how what happened between these years has affected how the stories have been told. During this time-space a lot happened; women became more independent and got the vote which was something they had been campaigning for many years. They were also more independent owing to the fact that during the first world war they had had to take mens jobs. So I am going also going to show how the books have shown this change in womens attitudes. In "Tony Kytes,The Arch-Deceiver" Tony is engaged to Milly. He is all set to marry her when a girl called Hannah comes along and shows an interest in him. Also on his travels he meets a girl called Unity who also shows an interest in him. After a while Tony asks Hannah to marry him but she turns him down, he also asks Unity to marry him but she also turns down his proposal. At the end of the story he asks Milly if she will come back and marry him which she excepts and they get married. In "Tickets, Please" John Thomas a station inspector takes advantage of the new female station conductors. He takes them for walks, only if they consent and providing that they are sufficiently attractive. He meets one, a Miss Annie, who he has liked for quite some time but she has kept him at arms length. She then agrees to walk with him, he takes her out to the fair. She then starts to show some intelligent interest so he lets her go, just like all the others before her. The women then decide that John needs to be punished for his actions. They corner him in a room where they ask him to choose one of them to become his wife. He refuses so they beat him up again and ask him again to choose one of them to be his wife, again he refuses so they beat him up again. They then ask him one last time to choose one of them to become his wife, he chooses Miss Annie, who bitterly refuses him, then all the girls refuse him and they let him get up an d leave. They then tidy them selves up and pretend that it had never happened.

Promote Products Essay

1. 1 Choose a product or service that could be promoted. Explain how and why you would promote that product or service. Identify at least three types of personnel you could use to help you plan and organise the promotion. What role would they play? How would their skills and experience help you? At work we are currently promoting our GPS products. We have chosen to promote these via direct marketing and by targeting certain business types. I liaised with our sales consultants, who talk to these people on a daily basis, to advise on what language should be used and what information they felt was most relevant to put across. We needed a graphic designer to create and develop ideas for the letters and DL flyers being sent. I also needed to involve juniors in the business to assist with folding letters and stuffing envelopes as we had determined that this would be a cheaper option for the business than using a mail house. 1. 2 Make a list of resources you would need for the promotion and identify where you could get them. Explain any actions you would need to take in order to have the resources ready for promotion. The database we purchased contained 7,000 leads so we then needed to purchase paper, envelops, ink, return stamps and organise postage. Paper, envelopes and labels for return address were all ordered in advance from Staples. Ink was also pre-ordered to ensure we didn’t run out during the print job. We then liaised with Australia Post to determine the best way to post this number of letters. We chose their â€Å"clean mail† option which meant having to mark each envelope with a pre paid stamp before taking to the post office. This stamp was purchased through Australia Post.

Om Heizer Om10 Ism 04

Chapter FORECASTING Discussion Questions 1.? Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When quantitative data are difficult to obtain, qualitative models may be appropriate. 2.? Approaches are qualitative and quantitative. Qualitative is relatively subjective; quantitative uses numeric models. 3.? Short-range (under 3 months), medium-range (3 months to 3 years), and long-range (over 3 years). 4.? The steps that should be used to develop a forecasting system are: (a)?Determine the purpose and use of the forecast (b)? Select the item or quantities that are to be forecasted (c)? Determine the time horizon of the forecast (d)? Select the type of forecasting model to be used (e)? Gather the necessary data (f)? Validate the forecasting model (g)? Make the forecast (h)? Implement and evaluate the results 5.? Any three of: sales planning, production planning and budgeting, cash budgeting, analyzing various operating plans. 6.? There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends. .? Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially. 8.? MAD, MSE, and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model, forecast with each tool for several periods where the demand outcome is known, and calculate MSE, MAPE, or MAD for each. The smaller error indicates the better forecast. 9.? The Delphi technique involves: (a)? Assembling a group of experts in such a manner as to preclude direct communication between identifiable members of the group (b)?Assembling the responses of each expert to the questions or problems of interest (c)? Summarizing these responses (d)? Providing each expert with the summary of all responses (e)? Asking each expert to study the summary of the responses and respond again to the questions or problems of interest. (f)? Repeating steps (b) through (e) several times as necessary to obtain convergence in responses. If convergence has not been obtained by the end of the fourth cycle, the responses at that time should probably be accepted and the process terminated—little additional convergence is likely if the process is continued. 0.? A time series model predicts on the basis of the assumption that the future is a function of the past, whereas an associative model incorporates into the model the variables of factors that might influence the quantity being forecast. 11.? A time series is a sequence of evenly spaced data points with the four components of trend, seasonality, cyclical, and random variation. 12.? When the smoothing constant, (, is large (close to 1. 0), more weight is given to recent data; when ( is low (close to 0. 0), more weight is given to past data. 13.? Seasonal patterns are of fixed duration a nd repeat regularly.Cycles vary in length and regularity. Seasonal indices allow â€Å"generic† forecasts to be made specific to the month, week, etc. , of the application. 14.? Exponential smoothing weighs all previous values with a set of weights that decline exponentially. It can place a full weight on the most recent period (with an alpha of 1. 0). This, in effect, is the naive approach, which places all its emphasis on last period’s actual demand. 15.? Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes its present limit. 16.?Tracking signals alert the user of a forecasting tool to periods in which the forecast was in significant error. 17.? The correlation coefficient measures the degree to which the independent and dependent variables move together. A negative value would mean that as X increases, Y tends to fall. The variables move together, but move in opposite directions. 18.? Independent variable (x) is said to explain variations in the dependent variable (y). 19.? Nearly every industry has seasonality. The seasonality must be filtered out for good medium-range planning (of production and inventory) and performance evaluation. 20.? There are many examples.Demand for raw materials and component parts such as steel or tires is a function of demand for goods such as automobiles. 21.? Obviously, as we go farther into the future, it becomes more difficult to make forecasts, and we must diminish our reliance on the forecasts. Ethical Dilemma This exercise, derived from an actual situation, deals as much with ethics as with forecasting. Here are a few points to consider:  ¦ No one likes a system they don’t understand, and most college presidents would feel uncomfortable with this one. It does offer the advantage of depoliticizing the funds al- location if used wisely and fairly.But to do so means all parties must have input to the process (such as smoothing constants) and all data need to be open to everyone.  ¦ The smoothing constants could be selected by an agreed-upon criteria (such as lowest MAD) or could be based on input from experts on the board as well as the college.  ¦ Abuse of the system is tied to assigning alphas based on what results they yield, rather than what alphas make the most sense.  ¦ Regression is open to abuse as well. Models can use many years of data yielding one result or few years yielding a totally different forecast.Selection of associative variables can have a major impact on results as well. Active Model Exercises* ACTIVE MODEL 4. 1: Moving Averages 1.? What does the graph look like when n = 1? The forecast graph mirrors the data graph but one period later. 2.? What happens to the graph as the number of periods in the moving average increases? The forecast graph becomes shorter and smoother. 3.? What value for n minimizes the MAD for this data? n = 1 (a naive forecast) ACTIVE MODEL 4. 2: Exponential Smoothing 1.? Wha t happens to the graph when alpha equals zero? The graph is a straight line.The forecast is the same in each period. 2.? What happens to the graph when alpha equals one? The forecast follows the same pattern as the demand (except for the first forecast) but is offset by one period. This is a naive forecast. 3.? Generalize what happens to a forecast as alpha increases. As alpha increases the forecast is more sensitive to changes in demand. *Active Models 4. 1, 4. 2, 4. 3, and 4. 4 appear on our Web site, www. pearsonhighered. com/heizer. 4.? At what level of alpha is the mean absolute deviation (MAD) minimized? alpha = . 16 ACTIVE MODEL 4. 3: Exponential Smoothing with Trend Adjustment .? Scroll through different values for alpha and beta. Which smoothing constant appears to have the greater effect on the graph? alpha 2.? With beta set to zero, find the best alpha and observe the MAD. Now find the best beta. Observe the MAD. Does the addition of a trend improve the forecast? alpha = . 11, MAD = 2. 59; beta above . 6 changes the MAD (by a little) to 2. 54. ACTIVE MODEL 4. 4: Trend Projections 1.? What is the annual trend in the data? 10. 54 2.? Use the scrollbars for the slope and intercept to determine the values that minimize the MAD. Are these the same values that regression yields?No, they are not the same values. For example, an intercept of 57. 81 with a slope of 9. 44 yields a MAD of 7. 17. End-of-Chapter Problems [pic] (b) | | |Weighted | |Week of |Pints Used |Moving Average | |August 31 |360 | | |September 7 |389 |381 ( . 1 = ? 38. 1 | |September 14 |410 |368 ( . 3 = 110. 4 | |September 21 |381 |374 ( . 6 = 224. 4 | |September 28 |368 |372. | |October 5 |374 | | | |Forecast 372. 9 | | (c) | | | |Forecasting | Error | | |Week of |Pints |Forecast |Error |( . 20 |Forecast| |August 31 |360 |360 |0 |0 |360 | |September 7 |389 |360 |29 |5. 8 |365. 8 | |September 14 |410 |365. 8 |44. 2 |8. 84 |374. 64 | |September 21 |381 |374. 64 |6. 36 |1. 272 |375. 12 | |Se ptember 28 |368 |375. 912 |–7. 912 |–1. 5824 |374. 3296| |October 5 |374 |374. 3296 |–. 3296 |–. 06592 |374. 2636| The forecast is 374. 26. (d)? The three-year moving average appears to give better results. [pic] [pic] Naive tracks the ups and downs best but lags the data by one period. Exponential smoothing is probably better because it smoothes the data and does not have as much variation. TEACHING NOTE: Notice how well exponential smoothing forecasts the naive. [pic] (c)? The banking industry has a great deal of seasonality in its processing requirements [pic] b) | | |Two-Year | | | |Year |Mileage |Moving Average |Error ||Error| | |1 |3,000 | | | | | |2 |4,000 | | | | | |3 |3,400 |3,500 |–100 | |100 | |4 |3,800 |3,700 |100 | |100 | |5 |3,700 |3,600 |100 | |100 | | | |Totals| |100 | | |300 | | [pic] 4. 5? (c)? Weighted 2 year M. A. ith . 6 weight for most recent year. |Year |Mileage |Forecast |Error ||Error| | |1 |3,000 | | | | |2 |4,000 | | | | |3 |3,400 |3,600 |–200 |200 | |4 |3,800 |3,640 |160 |160 | |5 |3,700 |3,640 |60 |60 | | | | | | | 420 | | Forecast for year 6 is 3,740 miles. [pic] 4. 5? (d) | | |Forecast |Error ( |New | |Year |Mileage |Forecast |Error |( = . 50 |Forecast | |1 |3,000 |3,000 | ?0 | 0 |3,000 | |2 |4,000 |3,000 |1,000 |500 |3,500 | |3 |3,400 |3,500 | –100 |–50 |3,450 | |4 |3,800 |3,450 | 350 |175 |3,625 | |5 |3,700 |3,625 | 75 |? 38 |3,663 | | | |Total |1,325| | | | The forecast is 3,663 miles. 4. 6 |Y Sales |X Period |X2 |XY | |January |20 |1 |1 |20 | |February |21 |2 |4 |42 | |March |15 |3 |9 |45 | |April |14 |4 |16 |56 | |May |13 |5 |25 |65 | |June |16 |6 |36 |96 | |July |17 |7 |49 |119 | |August |18 |8 |64 |144 | |September |20 |9 |81 |180 | |October |20 |10 |100 |200 | |November |21 |11 |121 |231 | |December |23 |12 |144 |276 | |Sum | 18 |78 |650 |1,474 | |Average |? 18. 2 | 6. 5 | | | (a) [pic] (b)? [i]? NaiveThe coming January = December = 23 [ii]? 3-month moving (20 + 21 + 23)/3 = 21. 33 [iii]? 6-month weighted [(0. 1 ( 17) + (. 1 ( 18) + (0. 1 ( 20) + (0. 2 ( 20) + (0. 2 ( 21) + (0. 3 ( 23)]/1. 0 = 20. 6 [iv]? Exponential smoothing with alpha = 0. 3 [pic] [v]? Trend? [pic] [pic] Forecast = 15. 73? +?. 38(13) = 20. 67, where next January is the 13th month. (c)? Only trend provides an equation that can extend beyond one month 4. 7? Present = Period (week) 6. a) So: where [pic] )If the weights are 20, 15, 15, and 10, there will be no change in the forecast because these are the same relative weights as in part (a), i. e. , 20/60, 15/60, 15/60, and 10/60. c)If the weights are 0. 4, 0. 3, 0. 2, and 0. 1, then the forecast becomes 56. 3, or 56 patients. [pic] [pic] |Temperature |2 day M. A. | |Error||(Error)2| Absolute |% Error | |93 |— | — |— |— | |94 |— | — |— |— | |93 |93. 5 | 0. 5 |? 0. 25| 100(. 5/93) | = 0. 54% | |95 |93. 5 | 1. 5 | ? 2. 25| 100(1. 5/95) | = 1. 58% | |96 |94. 0 | 2. 0 |? 4. 0 0| 100(2/96) | = 2. 08% | |88 |95. 5 | 7. | 56. 25| 100(7. 5/88) | = 8. 52% | |90 |92. 0 | 2. 0 |? 4. 00| 100(2/90) | = 2. 22% | | | | |13. 5| | | 66. 75 | | |14. 94% | MAD = 13. 5/5 = 2. 7 (d)? MSE = 66. 75/5 = 13. 35 (e)? MAPE = 14. 94%/5 = 2. 99% 4. 9? (a, b) The computations for both the two- and three-month averages appear in the table; the results appear in the figure below. [pic] (c)? MAD (two-month moving average) = . 750/10 = . 075 MAD (three-month moving average) = . 793/9 = . 088 Therefore, the two-month moving average seems to have performed better. [pic] (c)? The forecasts are about the same. [pic] 4. 12? t |Day |Actual |Forecast | | | | |Demand |Demand | | |1 |Monday |88 |88 | | |2 |Tuesday |72 |88 | | |3 |Wednesday |68 |84 | | |4 |Thursday |48 |80 | | |5 |Friday | |72 |( Answer | Ft = Ft–1 + ((At–1 – Ft–1) Let ( = . 25. Let Monday forecast demand = 88 F2 = 88 + . 25(88 – 88) = 88 + 0 = 88 F3 = 88 + . 25(72 – 88) = 88 – 4 = 84 F4 = 84 + . 25(68 – 84) = 84 – 4 = 80 F5 = 80 + . 25(48 – 80) = 80 – 8 = 72 4. 13? (a)? Exponential smoothing, ( = 0. 6: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 6(45–41) = 43. 4 |6. 6 | |3 |52 |43. 4 + 0. 6(50–43. 4) = 47. 4 |4. 6 | |4 |56 |47. 4 + 0. 6(52–47. 4) = 50. 2 |5. 8 | |5 |58 |50. 2 + 0. 6(56–50. 2) = 53. 7 |4. 3 | |6 |? |53. 7 + 0. 6(58–53. 7) = 56. 3 | | ( = 25. 3 MAD = 5. 06 Exponential smoothing, ( = 0. 9: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 9(45–41) = 44. 6 |5. 4 | |3 |52 |44. 6 + 0. 9(50–44. 6 ) = 49. 5 |2. 5 | |4 |56 |49. 5 + 0. 9(52–49. 5) = 51. 8 |4. 2 | |5 |58 |51. 8 + 0. 9(56–51. 8) = 55. 6 |2. 4 | |6 |? |55. 6 + 0. 9(58–55. 6) = 57. 8 | | ( = 18. 5 MAD = 3. 7 (b)? 3-year moving average: | | |Three-Year |Absolute | |Year |Demand |Moving Average |Deviation | |1 45 | | | |2 |50 | | | |3 |52 | | | |4 |56 |(45 + 50 + 52)/3 = 49 |7 | |5 |58 | (50 + 52 + 56)/3 = 52. 7 |5. 3 | |6 |? | (52 + 56 + 58)/3 = 55. 3 | | ( = 12. 3 MAD = 6. 2 (c)? Trend projection: | | | |Absolute | |Year |Demand |Trend Projection |Deviation | |1 |45 |42. 6 + 3. 2 ( 1 = 45. 8 |0. 8 | |2 |50 |42. 6 + 3. 2 ( 2 = 49. 0 |1. 0 | |3 |52 |42. 6 + 3. 2 ( 3 = 52. 2 |0. 2 | |4 |56 |42. 6 + 3. 2 ( 4 = 55. 4 |0. | |5 |58 |42. 6 + 3. 2 ( 5 = 58. 6 |0. 6 | |6 |? |42. 6 + 3. 2 ( 6 = 61. 8 | | ( = 3. 2 MAD = 0. 64 [pic] | X |Y |XY |X2 | | 1 |45 | 45 | 1 | | 2 |50 |100 | 4 | | 3 |52 |156 | 9 | | 4 |56 |224 |16 | | 5 |58 |290 |25 | Then: (X = 15, (Y = 261, (XY = 815, (X2 = 55, [pic]= 3, [pic]= 52. 2 Therefore: [pic] (d)? Comparing the results of the forecasting methodologies for parts (a), (b), and (c). |Forecast Methodology |MAD | |Exponential smoothing, ( = 0. |5. 06 | |Exponential smoothing, ( = 0. 9 |3. 7 | |3-year moving average |6. 2 | |Trend projection |0. 64 | Based on a mean absolute deviation criterion, the trend projection is to be preferred over the exponential smoothing with ( = 0. 6, exponential smoothing with ( = 0. 9, or the 3-year moving average forecast methodologies. 4. 14 Method 1:MAD: (0. 20 + 0. 05 + 0. 05 + 0. 20)/4 = . 125 ( better MSE : (0. 04 + 0. 0025 + 0. 0025 + 0. 04)/4 = . 021 Method 2:MAD: (0. 1 + 0. 20 + 0. 10 + 0. 11) / 4 = . 1275 MSE : (0. 01 + 0. 04 + 0. 01 + 0. 0121) / 4 = . 018 ( better 4. 15 | |Forecast Three-Year |Absolute | |Year |Sales |Moving Average |Deviation | |2005 |450 | | | |2006 |495 | | | |2007 |518 | | | |2008 |563 |(450 + 495 + 518)/3 = 487. 7 |75. 3 | |2009 |584 |(495 + 518 + 563)/3 = 525. 3 |58. 7 | |2010 | |(518 + 563 + 584)/3 = 555. 0 | | | | | ( = 134 | | | | MAD = 67 | 4. 16 Year |Time Period X |Sales Y |X2 |XY | |2005 |1 |450 | 1 |450 | |2006 |2 |495 | 4 |990 | |2007 |3 |518 | 9 |1554 | |2008 |4 |563 |16 |2252 | |2009 |5 |584 |25 |2920 | | | | ( = 2610| |( = 55 | |( = 8166 | [pic] [pic] |Year |Sales |Forecast Trend |Absolute Deviation | |2005 |450 |454. 8 |4. 8 | |2006 |495 |488. 4 |6. | |2007 |518 |522. 0 |4. 0 | |2008 |563 |555. 6 |7. 4 | |2009 |584 |589. 2 |5. 2 | |2010 | |622. 8 | | | | | | ( = 28 | | | | | MAD = 5. 6 | 4. 17 | | |Forecast Exponential |Absolute | |Year |Sales |Smoothing ( = 0. 6 |Deviation | |2005 |450 |410. 0 |40. | |2006 |495 |410 + 0. 6(450 – 410) = 434. 0 |61. 0 | |2007 |518 |434 + 0. 6(495 – 434) = 470. 6 |47. 4 | |2008 |563 |470. 6 + 0. 6(518 – 470. 6) = 499. 0 |64. 0 | |2009 |584 |499 + 0. 6(563 – 499) = 537. 4 |46. 6 | |2010 | |537. 4 + 0. 6(584 – 537. 4) = 565. 6 | | | | | ( = 259 | | | | MAD = 51. 8 | | | |Forecast Exponential |Absolute | |Year |Sales |Smoothing ( = 0. |Deviation | |2005 |450 |410. 0 |40. 0 | |2006 |495 |410 + 0. 9(450 – 410) = 446. 0 |49. 0 | |2007 |518 |446 + 0. 9(495 – 446) = 490. 1 |27. 9 | |2008 |563 |490. 1 + 0. 9(518 – 490. 1) = 515. 2 |47. 8 | |2009 |584 |515. 2 + 0. 9(563 – 515. 2) = 558. 2 |25. 8 | |2010 | |558. 2 + 0. 9(584 – 558. 2) = 581. 4 | | | | |( = 190. 5 | | | |MAD = 38. 1 | (Refer to Solved Problem 4. 1)For ( = 0. 3, absolute deviations for 2005–2009 are 40. 0, 73. 0, 74. 1, 96. 9, 88. 8, respectively. So the MAD = 372. 8/5 = 74. 6. [pic] Because it gives the lowest MAD, the smoothing constant of ( = 0. 9 gives the most accurate forecast. 4. 18? We need to find the smoothing constant (. We know in general that Ft = Ft–1 + ((At–1 – Ft–1); t = 2, 3, 4. Choose either t = 3 or t = 4 (t = 2 won’t let us find ( because F2 = 50 = 50 + ((50 – 50) holds for any (). Let’s pick t = 3. Then F3 = 48 = 50 + ((42 – 50) or 48 = 50 + 42( – 50( or –2 = –8( So, . 25 = ( Now we can find F5 : F5 = 50 + ((46 – 50)F5 = 50 + 46( – 50( = 50 – 4( For ( = . 25, F5 = 50 – 4(. 25) = 49 The forecast for time period 5 = 49 units. 4. 19? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 2 | | |Unadjusted | |Adjusted | | | |Month |Income |Forecast |Trend |Forecast ||Error||Error2 | |February |70. 0 | 65. 0 | 0. 0 | 65 |? 5. 0 |? 25. 0 | |March |68. 5 | 65. 5 | 0. 1 | 65. 6 |? 2. 9 |? 8. 4 | |April |64. 8 | 65. 9 | 0. 16 |66. 05 |? 1. 2 |? 1. 6 | |May |71. 7 | 65. 92 | 0. 13 |66. 06 |? 5. 6 |? 31. 9 | |June |71. | 66. 62 | 0. 25 |66. 87 |? 4. 4 |? 19. 7 | |July |72. 8 | 67. 31 | 0. 33 |67. 64 |? 5. 2 |? 26. 6 | |August | | 68. 16 | |68. 60 | |24. 3| | |113. 2| | MAD = 24. 3/6 = 4. 05, MSE = 113. 2/6 = 18. 87. Note that all numbers are rounded. Note: To use POM for Windows to solve this problem, a period 0, which contains the initial forecast and initial trend, must be added. 4. 20? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 8 [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] 4. 23? Students must determine the naive forecast for the four months .The naive forecast for March is the February actual of 83, etc. |(a) | |Actual |Forecast ||Error| ||% Error| | | |March |101 |120 |19 |100 (19/101) = 18. 81% | | |April |? 96 |114 |18 |100 (18/96) ? = 18. 75% | | |May |? 89 |110 |21 |100 (21/89) ? = 23. 60% | | |June |108 |108 |? 0 |100 (0/108) ? = 0% | | | | | | |58 | | | 61. 16% | [pic] |(b)| |Actual |Naive ||Error| ||% Error| | | |March |101 |? 83 |18 |100 (18/101) = 17. 82% | | |April |? 96 |101 |? |100 (5/96) ? = 5. 21% | | |May |? 89 |? 96 |? 7 |100 (7/89) ? =? 7. 87% | | |June |108 |? 89 |19 |100 (19/108) = 17. 59% | | | | | | |49| | |48. 49% | | [pic] Naive outperforms management. (c)? MAD for the manager’s technique is 14. 5, while MAD for the naive forecast is only 12. 25. MAPEs are 15. 29% and 12. 12%, respectively. So the naive method is better. 4. 24? (a)? Graph of demand The observations obviously do not form a straight line but do tend to cluster about a straight line over the range shown. (b)? Least-squares regression: [pic] Assume Appearances X |Demand Y |X2 |Y2 |XY | |3 | 3 | 9 | 9 | 9 | |4 | 6 |16 | 36 |24 | |7 | 7 |49 | 49 |49 | |6 | 5 |36 | 25 |30 | |8 |10 |64 |100 |80 | |5 | 7 |25 | 49 |35 | |9 | ? | | | | (X = 33, (Y = 38, (XY = 227, (X2 = 199, [pic]= 5. 5, [pic]= 6. 33. Therefore: [pic] The following figure shows both the data and the resulting equation: [pic] (c) If there are nine performances by Stone Temple Pilots, the estimated sales are: (d) R = . 82 is the correlation coefficient, and R2 = . 68 means 68% of the variation in sales can be explained by TV appearances. 4. 25? |Number of | | | | | |Accidents | | | | |Month |(y) |x |xy |x2 | |January | 30 | 1 | 30 | 1 | |February | 40 | 2 | 80 | 4 | |March | 60 | 3 |180 | 9 | |April | 90 | 4 |360 |16 | |? Totals | |220 | | | [pic] The regression line is y = 5 + 20x. The forecast for May (x = 5) is y = 5 + 20(5) = 105. 4. 26 |Season |Year1 |Year2 |Average |Average |Seasonal |Year3 | | |Demand |Demand |Year1(Year2 |Season |Index |Demand | | | | |Demand |Demand | | | |Fall |200 |250 |225. 0 |250 |0. 90 |270 | |Winter |350 |300 |325. |250 |1. 30 |390 | |Spring |150 |165 |157. 5 |250 |0. 63 |189 | |Summer |300 |285 |292. 5 |250 |1. 17 |351 | 4. 27 | | Winter |Spring |Summer |Fall | |2006 |1,400 |1,500 |1,000 |600 | |2007 |1,200 |1,400 |2,100 |750 | |2008 |1,000 |1,600 |2,000 |650 | |2009 | 900 |1,500 |1,900 | 500 | | |4,500 |6,000 |7,000 |2,500 | 4. 28 | | | | |Average | | | | | | |Average |Quarterly |Seasonal | |Quarter |2007 |2008 |2009 |Demand |Demand |Index | |Winter | 73 | 65 | 89 | 75. 67 |106. 67 |0. 709 | |Spring |104 | 82 |146 |110. 67 |106. 67 |1. 037 | |Summer |168 |124 |205 |165. 67 |106. 67 |1. 553 | |Fall | 74 | 52 | 98 | 74. 67 |106. 67 |0. 700 | 4. 29? 2011 is 25 years beyond 1986. Therefore, the 2011 quarter numbers are 101 through 104. | | | | |(5) | | |(2) |(3) |(4) |Adjusted | |(1) |Quarter |Forecast |Seasonal |Forecast | |Quarter |Number |(77 + . 3Q) |Factor |[(3) ( (4)] | |Winter |101 |12 0. 43 | . 8 | 96. 344 | |Spring |102 |120. 86 |1. 1 |132. 946 | |Summer |103 |121. 29 |1. 4 |169. 806 | |Fall |104 |121. 72 | . 7 | 85. 204 | 4. 30? Given Y = 36 + 4. 3X (a) Y = 36 + 4. 3(70) = 337 (b) Y = 36 + 4. 3(80) = 380 (c) Y = 36 + 4. 3(90) = 423 4. 31 4. 33? (a)? See the table below. For next year (x = 6), the number of transistors (in millions) is forecasted as y = 126 + 18(6) = 126 + 108 = 234. Then y = a + bx, where y = number sold, x = price, and |4. 32? a) | x |y |xy |x2 | | | 16 | 330 | 5,280 |256 | | | 12 | 270 | 3,240 |144 | | | 18 | 380 | 6,840 |324 | | | 14 | 300 | 4,200 |196 | | | 60 |1,280 |19,560 |920 | So at x = 2. 80, y = 1,454. 6 – 277. 6($2. 80) = 677. 32. Now round to the nearest integer: Answer: 677 lattes. [pic] (b)? If the forecast is for 20 guests, the bar sales forecast is 50 + 18(20) = $410. Each guest accounts for an additional $18 in bar sales. |Table for Problem 4. 33 | | | | | |Year |Transistors | | | | | | | |(x) |(y) |xy |x2 |126 + 18x |E rror |Error2 ||% Error| | | |? 1 |140 |? 140 |? 1 |144 |–4 |? 16 |100 (4/140)? = 2. 86% | | |? 2 |160 |? 320 |? 4 |162 |–2 | 4 |100 (2/160)? = 1. 25% | | |? 3 |190 |? 570 |? 9 |180 |10 |100 |100 (10/190) = 5. 26% | | |? 4 |200 |? 800 |16 |198 |? 2 | 4 |100 (2/200) = 1. 00% | | |? |210 |1,050 |25 |216 |–6 |? 36 |100 (6/210)? = 2. 86% | |Totals |15 | | |900 | | |2,800 | | (b)? MSE = 160/5 = 32 (c)? MAPE = 13. 23%/5 = 2. 65% 4. 34? Y = 7. 5 + 3. 5X1 + 4. 5X2 + 2. 5X3 (a)? 28 (b)? 43 (c)? 58 4. 35? (a)? [pic] = 13,473 + 37. 65(1860) = 83,502 (b)? The predicted selling price is $83,502, but this is the average price for a house of this size. There are other factors besides square footage that will impact the selling price of a house. If such a house sold for $95,000, then these other factors could be contributing to the additional value. (c)?Some other quantitative variables would be age of the house, number of bedrooms, size of the lot, and size of the garage, etc. (d)? Coefficient of determination = (0. 63)2 = 0. 397. This means that only about 39. 7% of the variability in the sales price of a house is explained by this regression model that only includes square footage as the explanatory variable. 4. 36? (a)? Given: Y = 90 + 48. 5X1 + 0. 4X2 where: [pic] If: Number of days on the road ( X1 = 5 and distance traveled ( X2 = 300 then: Y = 90 + 48. 5 ( 5 + 0. 4 ( 300 = 90 + 242. 5 + 120 = 452. 5 Therefore, the expected cost of the trip is $452. 50. (b)? The reimbursement request is much higher than predicted by the model. This request should probably be questioned by the accountant. (c)?A number of other variables should be included, such as: 1.? the type of travel (air or car) 2.? conference fees, if any 3.? costs of entertaining customers 4.? other transportation costs—cab, limousine, special tolls, or parking In addition, the correlation coefficient of 0. 68 is not exceptionally high. It indicates that the model explains approximately 46% of the overall variation in trip cost. This correlation coefficient would suggest that the model is not a particularly good one. 4. 37? (a, b) |Period |Demand |Forecast |Error |Running sum ||error| | | 1 |20 |20 |0. 00 |0. 00 |0. 00 | | 2 |21 |20 |1. 00 |1. 0 |1. 00 | | 3 |28 |20. 5 |7. 50 |8. 50 |7. 50 | | 4 |37 |24. 25 |12. 75 |21. 25 |12. 75 | | 5 |25 |30. 63 |–5. 63 |15. 63 |5. 63 | | 6 |29 |27. 81 |1. 19 |16. 82 |1. 19 | | 7 |36 |28. 41 |7. 59 |24. 41 |7. 59 | | 8 |22 |32. 20 |–10. 20 |14. 21 |10. 20 | | 9 |25 |27. 11 |–2. 10 |12. 10 |2. 10 | |10 |28 |26. 05 | 1. 95 |14. 05 | | | | | | |1. 95 | | | | | | | | | | | | | | | |MAD[pic]5. 00 | Cumulative error = 14. 05; MAD = 5? Tracking = 14. 05/5 ( 2. 82 4. 38? (a)? least squares equation: Y = –0. 158 + 0. 1308X (b)? Y = –0. 158 + 0. 1308(22) = 2. 719 million (c)? coefficient of correlation = r = 0. 966 coefficient of determination = r2 = 0. 934 4. 39 |Year X |Patients Y |X2 |Y2 |XY | |? 1 |? 36 | 1 |? 1,296 | 36 | |? 2 |? 33 | |? 1,089 | 66 | |? 3 |? 40 | 9 |? 1,600 |? 120 | |? 4 |? 41 |? 16 |? 1,681 |? 164 | |? 5 |? 40 |? 25 |? 1,600 |? 200 | |? 6 |? 55 |? 36 |? 3,025 |? 330 | |? 7 |? 60 |? 49 |? 3,600 |? 420 | |? 8 |? 54 |? 64 |? 2,916 |? 432 | |? 9 |? 58 |? 81 |? 3,364 |? 522 | |10 |? 61 |100 |? 3,721 |? 10 | |55 | | |478 | | |X |Y |Forecast |Deviation |Deviation | |? 1 |36 |29. 8 + 3. 28 ( ? 1 = 33. 1 |? 2. 9 |2. 9 | |? 2 |33 |29. 8 + 3. 28 ( ? 2 = 36. 3 |–3. 3 |3. 3 | |? 3 |40 |29. 8 + 3. 28 ( ? 3 = 39. 6 |? 0. 4 |0. 4 | |? 4 |41 |29. 8 + 3. 28 ( ? 4 = 42. 9 |–1. 9 |1. 9 | |? 5 |40 |29. 8 + 3. 28 ( ? 5 = 46. 2 |–6. 2 |6. 2 | |? 6 |55 |29. 8 + 3. 28 ( ? 6 = 49. 4 |? 5. 6 |5. 6 | |? 7 |60 |29. 8 + 3. 28 ( ? 7 = 52. 7 |? 7. 3 |7. 3 | |? |54 |29. 8 + 3. 28 ( ? 8 = 56. 1 |–2. 1 |2. 1 | |? 9 |58 |29. 8 + 3. 28 ( ? 9 = 59. 3 |–1. 3 |1. 3 | |10 |61 |29. 8 + 3. 28 ( 10 = 62. 6 |–1. 6 |1. 6 | | | | | | ( = | | | | | |32. 6 | | | | | |MAD = 3. 26 | The MAD is 3. 26—this is approximately 7% of the average number of patients and 10% of the minimum number of patients. We also see absolute deviations, for years 5, 6, and 7 in the range 5. 6–7. 3.The comparison of the MAD with the average and minimum number of patients and the comparatively large deviations during the middle years indicate that the forecast model is not exceptionally accurate. It is more useful for predicting general trends than the actual number of patients to be seen in a specific year. 4. 40 | |Crime |Patients | | | | |Year |Rate X |Y |X2 |Y2 |XY | |? 1 |? 58. 3 |? 36 |? 3,398. 9 |? 1,296 |? 2,098. 8 | |? 2 |? 61. 1 |? 33 |? 3,733. 2 |? 1,089 |? 2,016. 3 | |? 3 |? 73. |? 40 |? 5,387. 6 |? 1,600 |? 2,936. 0 | |? 4 |? 75. 7 |? 41 |? 5,730. 5 |? 1,681 |? 3,103. 7 | |? 5 |? 81. 1 |? 40 |? 6,577. 2 |? 1,600 |? 3,244. 0 | |? 6 |? 89. 0 |? 55 |? 7,921. 0 |? 3,025 |? 4,895. 0 | |? 7 |101. 1 |? 60 |10,221. 2 |? 3,600 |? 6,066. 0 | |? 8 |? 94 . 8 |? 54 |? 8,987. 0 |? 2,916 |? 5,119. 2 | |? 9 |103. 3 |? 58 |10,670. 9 |? 3,364 |? 5,991. 4 | |10 |116. 2 |? 61 |13,502. 4 |? 3,721 |? 7,088. 2 | |Column | |854. | | |478 | |Totals | | | | | | |months) |(Millions) |(1,000,000s) | | | | |Year |(X) |(Y) |X2 |Y2 |XY | |? 1 |? 7 |1. 5 |? 49 |? 2. 25 |10. 5 | |? 2 |? 2 |1. 0 | 4 |? 1. 00 |? 2. 0 | |? 3 |? 6 |1. 3 |? 36 |? 1. 69 |? 7. 8 | |? 4 |? 4 |1. 5 |? 16 |? 2. 25 |? 6. 0 | |? 5 |14 |2. 5 |196 |? 6. 25 |35. 0 | |? 6 |15 |2. 7 |225 |? 7. 9 |40. 5 | |? 7 |16 |2. 4 |256 |? 5. 76 |38. 4 | |? 8 |12 |2. 0 |144 |? 4. 00 |24. 0 | |? 9 |14 |2. 7 |196 |? 7. 29 |37. 8 | |10 |20 |4. 4 |400 |19. 36 |88. 0 | |11 |15 |3. 4 |225 |11. 56 |51. 0 | |12 |? 7 |1. 7 |? 49 |? 2. 89 |11. 9 | Given: Y = a + bX where: [pic] and (X = 132, (Y = 27. 1, (XY = 352. 9, (X2 = 1796, (Y2 = 71. 59, [pic] = 11, [pic]= 2. 26. Then: [pic] andY = 0. 511 + 0. 159X (c)?Given a tourist population of 10,000,000, the model predicts a ridership of: Y = 0. 511 + 0. 159 ( 10 = 2. 101, or 2,101,000 persons. (d)? If there are no tourists at all, the model predicts a ridership of 0. 511, or 511,000 persons. One would not place much confidence in this forecast, however, because the number of tourists (zero) is outside the range of data used to develop the model. (e)? The standard error of the estimate is given by: (f)? The correlation coefficient and the coefficient of determination are given by: [pic] 4. 42? (a)? This problem gives students a chance to tackle a realistic problem in business, i. e. , not enough data to make a good forecast.As can be seen in the accompanying figure, the data contains both seasonal and trend factors. [pic] Averaging methods are not appropriate with trend, seasonal, or other patterns in the data. Moving averages smooth out seasonality. Exponential smoothing can forecast January next year, but not farther. Because seasonality is strong, a naive model that students create on their own might be best. (b) One model might be: Ft+1 = At–11 That is forecastnext period = actualone year earlier to account for seasonality. But this ignores the trend. One very good approach would be to calculate the increase from each month last year to each month this year, sum all 12 increases, and divide by 12.The forecast for next year would equal the value for the same month this year plus the average increase over the 12 months of last year. (c) Using this model, the January forecast for next year becomes: [pic] where 148 = total monthly increases from last year to this year. The forecasts for each of the months of next year then become: |Jan. |29 | |July. |56 | |Feb. |26 | |Aug. |53 | |Mar. |32 | |Sep. |45 | |Apr. |35 | |Oct. |35 | |May. |42 | |Nov. |38 | |Jun. |50 | |Dec. |29 | Both history and forecast for the next year are shown in the accompanying figure: [pic] 4. 3? (a) and (b) See the following table: | |Actual |Smoothed | |Smoothed | | |Week |Value |Value |Forecast |Value |Forecast | |t |A(t) |Ft (( = 0. 2) |Err or |Ft (( = 0. 6)|Error | | 1 |50 |+50. 0 |? +0. 0 |+50. 0 |? +0. 0 | | 2 |35 |+50. 0 |–15. 0 |+50. 0 |–15. 0 | | 3 |25 |+47. 0 |–22. 0 |+41. 0 |–16. 0 | | 4 |40 |+42. 6 |? –2. 6 |+31. 4 |? +8. 6 | | 5 |45 |+42. 1 |? –2. 9 |+36. 6 |? +8. | | 6 |35 |+42. 7 |? –7. 7 |+41. 6 |? –6. 6 | | 7 |20 |+41. 1 |–21. 1 |+37. 6 |–17. 6 | | 8 |30 |+36. 9 |? –6. 9 |+27. 1 |? +2. 9 | | 9 |35 |+35. 5 |? –0. 5 |+28. 8 |? +6. 2 | |10 |20 |+35. 4 |–15. 4 |+32. 5 |–12. 5 | |11 |15 |+32. 3 |–17. 3 |+25. 0 |–10. 0 | |12 |40 |+28. 9 |+11. 1 |+19. 0 |+21. 0 | |13 |55 |+31. 1 |+23. 9 |+31. 6 |+23. 4 | |14 |35 |+35. 9 |? 0. 9 |+45. 6 |–10. 6 | |15 |25 |+36. 7 |–10. 7 |+39. 3 |–14. 3 | |16 |55 |+33. 6 |+21. 4 |+30. 7 |+24. 3 | |17 |55 |+37. 8 |+17. 2 |+45. 3 |? +9. 7 | |18 |40 |+41. 3 |? –1. 3 |+51. 1 |–11. 1 | |19 |35 |+41. 0 |? –6. 0 |+44. 4 |? –9. 4 | |20 |60 |+39. 8 |+20. 2 |+38. 8 |+21. 2 | |21 |75 |+43. 9 |+31. 1 |+51. 5 |+23. 5 | |22 |50 |+50. 1 |? –0. 1 |+65. 6 |–15. | |23 |40 |+50. 1 |–10. 1 |+56. 2 |–16. 2 | |24 |65 |+48. 1 |+16. 9 |+46. 5 |+18. 5 | |25 | |+51. 4 | |+57. 6 | | | | |MAD = 11. 8 |MAD = 13. 45 | (c)? Students should note how stable the smoothed values are for ( = 0. 2. When compared to actual week 25 calls of 85, the smoothing constant, ( = 0. 6, appears to do a slightly better job. On the basis of the standard error of the estimate and the MAD, the 0. 2 constant is better. However, other smoothing constants need to be examined. |4. 4 | | | | | | |Week |Actual Value |Smoothed Value |Trend Estimate |Forecast |Forecast | |t |At |Ft (( = 0. 3) |Tt (( = 0. 2) |FITt |Error | |? 1 |50. 000 |50. 000 |? 0. 000 |50. 000 | 0. 000 | |? 2 |35. 000 |50. 000 |? 0. 000 |50. 000 |–15. 000 | |? 3 |25. 000 |45. 500 |–0. 900 |44. 600 |–19. 600 | |? 4 |40. 000 |38. 720 |– 2. 076 |36. 644 | 3. 56 | |? 5 |45. 000 |37. 651 |–1. 875 |35. 776 | 9. 224 | |? 6 |35. 000 |38. 543 |–1. 321 |37. 222 |? –2. 222 | |? 7 |20. 000 |36. 555 |–1. 455 |35. 101 |–15. 101 | |? 8 |30. 000 |30. 571 |–2. 361 |28. 210 | 1. 790 | |? 9 |35. 000 |28. 747 |–2. 253 |26. 494 | 8. 506 | |10 |20. 000 |29. 046 |–1. 743 |27. 03 |? –7. 303 | |11 |15. 000 |25. 112 |–2. 181 |22. 931 |? –7. 931 | |12 |40. 000 |20. 552 |–2. 657 |17. 895 |? 22. 105 | |13 |55. 000 |24. 526 |–1. 331 |23. 196 |? 31. 804 | |14 |35. 000 |32. 737 |? 0. 578 |33. 315 | 1. 685 | |15 |25. 000 |33. 820 |? 0. 679 |34. 499 |? –9. 499 | |16 |55. 000 |31. 649 |? 0. 109 |31. 58 |? 23. 242 | |17 |55. 000 |38. 731 |? 1. 503 |40. 234 |? 14. 766 | |18 |40. 000 |44. 664 |? 2. 389 |47. 053 |? –7. 053 | |19 |35. 000 |44. 937 |? 1. 966 |46. 903 |–11. 903 | |20 |60. 000 |43. 332 |? 1. 252 |44. 584 |? 15. 416 | |21 |75. 00 0 |49. 209 |? 2. 177 |51. 386 |? 23. 614 | |22 |50. 000 |58. 470 |? 3. 94 |62. 064 |–12. 064 | |23 |40. 000 |58. 445 |? 2. 870 |61. 315 |–21. 315 | |24 |65. 000 |54. 920 |? 1. 591 |56. 511 | 8. 489 | |25 | |59. 058 |? 2. 100 |61. 158 | | To evaluate the trend adjusted exponential smoothing model, actual week 25 calls are compared to the forecasted value. The model appears to be producing a forecast approximately mid-range between that given by simple exponential smoothing using ( = 0. 2 and ( = 0. 6.Trend adjustment does not appear to give any significant improvement. 4. 45 |Month |At |Ft ||At – Ft | |(At – Ft) | |May |100 |100 | 0 | 0 | |June | 80 |104 |24 |–24 | |July |110 | 99 |11 |11 | |August |115 |101 |14 |14 | |September |105 |104 | 1 | 1 | |October |110 |104 |6 |6 | |November |125 |105 |20 |20 | December |120 |109 |11 |11 | | | | |Sum: 87 |Sum: 39 | |4. 46 (a) | |X |Y |X2 |Y2 |XY | | |? 421 |? 2. 90 |? 177241 | 8. 41 |? 1220. 9 | | |? 377 | ? 2. 93 |? 142129 | 8. 58 |? 1104. 6 | | |? 585 |? 3. 00 |? 342225 | 9. 00 |? 1755. 0 | | |? 690 |? 3. 45 |? 476100 |? 11. 90 |? 2380. 5 | | |? 608 |? 3. 66 |? 369664 |? 13. 40 |? 2225. 3 | | |? 390 |? 2. 88 |? 52100 | 8. 29 |? 1123. 2 | | |? 415 |? 2. 15 |? 172225 | 4. 62 | 892. 3 | | |? 481 |? 2. 53 |? 231361 | 6. 40 |? 1216. 9 | | |? 729 |? 3. 22 |? 531441 |? 10. 37 |? 2347. 4 | | |? 501 |? 1. 99 |? 251001 | 3. 96 | 997. 0 | | |? 613 |? 2. 75 |? 375769 | 7. 56 |? 1685. 8 | | |? 709 |? 3. 90 |? 502681 |? 15. 21 |? 2765. 1 | | |? 366 |? 1. 60 |? 133956 | 2. 56 | 585. 6 | | |Column |6885 | |36. 6 | | | |totals | | | | | |January |400 |— |— | — |— | |February |380 |400 |— |20. 0 |— | |March |410 |398 |— |12. 0 |— | |April |375 | 399. 2 |396. 67 |24. 2 |21. 67 | |May |405 | 396. 8 |388. 33 |8. 22 |16. 67 | | | | |MAD = | |16. 11| | |19. 17| | (d)Note that Amit has more forecast observations, while Barbara’s moving average does not start until month 4. Also note that the MAD for Amit is an average of 4 numbers, while Barbara’s is only 2. Amit’s MAD for exponential smoothing (16. 1) is lower than that of Barbara’s moving average (19. 17). So his forecast seems to be better. 4. 48? (a) |Quarter |Contracts X |Sales Y |X2 |Y2 |XY | |1 |? 153 |? 8 |? 23,409 |? 64 |? 1,224 | |2 |? 172 |10 |? 29,584 |100 |? 1,720 | |3 |? 197 |15 |? 38,809 |225 |? 2,955 | |4 |? 178 |? 9 |? 31,684 |? 81 |? 1,602 | |5 |? 185 |12 |? 34,225 |144 |? 2,220 | |6 |? 199 |13 |? 39,601 |169 |? 2,587 | |7 |? 205 |12 |? 42,025 |144 |? ,460 | |8 |? 226 |16 |? 51,076 |256 |? 3,616 | |Totals | | 1,515 | | |95 | b = (18384 – 8 ( 189. 375 ( 11. 875)/(290,413 – 8 ( 189. 375 ( 189. 375) = 0. 1121 a = 11. 875 – 0. 1121 ( 189. 375 = –9. 3495 Sales ( y) = –9. 349 + 0. 1121 (Contracts) (b) [pic] 4. 49? (a) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||E rror| |Error2 | | 1 |? 0. 25 |0. 25 |0. 00 |? 0. 00 | | 2 |? . 24 |0. 25 |0. 01 |? 0. 0001 | | 3 |? 0. 24 |0. 244 |0. 004 |? 0. 0000 | | 4 |? 0. 26 |0. 241 |0. 018 |? 0. 0003 | | 5 |? 0. 25 |0. 252 |0. 002 |? 0. 00 | | 6 |? 0. 30 |0. 251 |0. 048 |? 0. 0023 | | 7 |? 0. 31 |0. 280 |0. 029 |? 0. 0008 | | 8 |? 0. 32 |0. 298 |0. 021 |? 0. 0004 | | 9 |? 0. 24 |0. 311 |0. 071 |? 0. 0051 | |10 |? 0. 26 |0. 68 |0. 008 |? 0. 0000 | |11 |? 0. 25 |0. 263 |0. 013 |? 0. 0002 | |12 |? 0. 33 |0. 255 |0. 074 |? 0. 0055 | |13 |? 0. 50 |0. 300 |0. 199 |? 0. 0399 | |14 |? 0. 95 |0. 420 |0. 529 |? 0. 2808 | |15 |? 1. 70 |0. 738 |0. 961 |? 0. 925 | |16 |? 2. 30 |1. 315 |0. 984 |? 0. 9698 | |17 |? 2. 80 |1. 906 |0. 893 |? 0. 7990 | |18 |? 2. 80 |2. 442 |0. 357 |? 0. 278 | |19 |? 2. 70 |2. 656 |0. 043 |? 0. 0018 | |20 |? 3. 90 |2. 682 |1. 217 |? 1. 4816 | |21 |? 4. 90 |3. 413 |1. 486 |? 2. 2108 | |22 |? 5. 30 |4. 305 |0. 994 |? 0. 9895 | |23 |? 6. 20 |4. 90 |1. 297 |? 1. 6845 | |24 |? 4. 10 |5. 680 |1. 580 |? 2. 499 | |25 |? 4. 50 |4. 732 |0. 232 |? 0. 0540 | |26 |? 6. 10 |4. 592 |1. 507 |? 2. 2712 | |27 |? 7. 0 |5. 497 |2. 202 |? 4. 8524 | |28 |10. 10 |6. 818 |3. 281 |10. 7658 | |29 |15. 20 |8. 787 |6. 412 |41. 1195 | (Continued) 4. 49? (a)? (Continued) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||Error| |Error2 | |30 |? 18. 10 |12. 6350 | 5. 46498 |29. 8660 | |31 |? 24. 10 |15. 9140 |8. 19 |67. 01 | |32 |? 25. 0 |20. 8256 |4. 774 |22. 7949 | |33 |? 30. 30 |23. 69 | 6. 60976 |43. 69 | |34 |? 36. 00 |27. 6561 | 8. 34390 |69. 62 | |35 |? 31. 10 |32. 6624 | 1. 56244 | 2. 44121 | |36 |? 31. 70 |31. 72 | 0. 024975 | 0. 000624 | |37 |? 38. 50 |31. 71 |6. 79 |? 46. 1042 | |38 |? 47. 90 |35. 784 |12. 116 |146. 798 | |39 |? 49. 10 |43. 0536 |6. 046 |36. 56 | |40 |? 55. 80 |46. 814 | 9. 11856 | 83. 1481 | |41 |? 70. 10 |52. 1526 |17. 9474 |322. 11 | |42 |? 70. 90 |62. 9210 | 7. 97897 |63. 66 | |43 |? 79. 10 |67. 7084 |11. 3916 |129. 768 | |44 |? 94. 0 0 |74. 5434 | 19. 4566 | 378. 561 | |TOTALS | |787. 30 | | | |150. 3 | | |1,513. 22 | |AVERAGE | 17. 8932 | | 3. 416 | 34. 39 | | | | |(MAD) |(MSE) | |Next period forecast = 86. 2173 |Standard error = 6. 07519 | Method ( Linear Regression (Trend Analysis) | |Year |Period (X) |Deposits (Y) |Forecast |Error2 | |? 1 |? 1 |0. 25 |–17. 330 |309. 061 | |? 2 |? 2 |0. 24 |–15. 692 |253. 823 | |? 3 |? 3 |0. 24 |–14. 054 |204. 31 | |? 4 |? 4 |0. 26 |–12. 415 |160. 662 | |? 5 |? 5 |0. 25 |–10. 777 |121. 594 | |? 6 |? 6 |0. 30 |? –9. 1387 |89. 0883 | |? 7 |? 7 |0. 31 |? –7. 50 |61. 0019 | |? 8 |? 8 |0. 32 |? –5. 8621 |38. 2181 | |? |? 9 |0. 24 |? –4. 2238 |19. 9254 | |10 |10 |0. 26 |? –2. 5855 |8. 09681 | |11 |11 |0. 25 |? –0. 947 |1. 43328 | |12 |12 |0. 33 |? 0. 691098 |0. 130392 | |13 |13 |0. 50 |? 2. 329 |3. 34667 | |14 |14 |0. 95 |? 3. 96769 |9. 10642 | |15 |15 |1. 70 |? 5. 60598 |15. 2567 | |16 |16 |2. 30 |? 7. 24 427 |24. 4458 | |17 |17 |2. 0 |? 8. 88257 |36. 9976 | |18 |18 |2. 80 |? 10. 52 |59. 6117 | |19 |19 |2. 70 |? 12. 1592 |89. 4756 | |20 |20 |3. 90 |? 13. 7974 |97. 9594 | |21 |21 |4. 90 |? 15. 4357 |111. 0 | |22 |22 |5. 30 |? 17. 0740 |138. 628 | |23 |23 |6. 20 |? 18. 7123 |156. 558 | |24 |24 |4. 10 |? 20. 35 |264. 083 | |25 |25 |4. 50 |? 21. 99 |305. 62 | |26 |26 |6. 10 |? 23. 6272 |307. 203 | |27 |27 |7. 70 |? 25. 2655 |308. 547 | |28 |28 |10. 10 |? 26. 9038 |282. 367 | |29 |29 |15. 20 |? 28. 5421 |178. 011 | |30 |30 |18. 10 |? 30. 18 |145. 936 | |31 |31 |24. 10 |? 31. 8187 |59. 58 | |32 |32 |25. 60 |? 33. 46 |61. 73 | |33 |33 |30. 30 |? 35. 0953 |22. 9945 | |34 |34 |36. 0 |? 36. 7336 |0. 5381 | |35 |35 |31. 10 |? 38. 3718 |52. 8798 | |36 |36 |31. 70 |? 40. 01 |69. 0585 | |37 |37 |38. 50 |? 41. 6484 |9. 91266 | |38 |38 | 47. 90 |? 43. 2867 |21. 2823 | |39 | 39 |49. 10 |? 44. 9250 |17. 43 | |40 | 40 |55. 80 |? 46. 5633 |? ? 85. 3163 | |41 | 41 |70. 10 |? 48. 2016 |? 479. 54 | |42 | 4 2 |70. 90 |? 49. 84 |? 443. 28 | |43 | 43 |79. 10 |? 51. 4782 |? 762. 964 | |44 | 44 |94. 00 |? 53. 1165 | 1,671. 46 | |TOTALS | |990. 00 | | |787. 30 | | | | | | | | | | | | | |7,559. 95 | | |AVERAGE |22. 50 | 17. 893 | |171. 817 | | | | | |(MSE) | |Method ( Least squares–Simple Regression on GSP | | |a |b | | | | |–17. 636 |13. 936 | | | | |Coefficients: |GSP |Deposits | | | | |Year |(X) |(Y) |Forecast ||Error| |Error2 | |? 1 |0. 40 |? 0. 25 |–12. 198 |? 12. 4482 |? 154. 957 | |? 2 |0. 40 |? 0. 24 |–12. 198 |? 12. 4382 |? 154. 71 | |? 3 |0. 50 |? 0. 24 |–10. 839 |? 11. 0788 |? 122. 740 | |? 4 |0. 70 |? 0. 26 |–8. 12 | 8. 38 | 70. 226 | |? 5 |0. 90 |? 0. 25 |–5. 4014 | 5. 65137 | 31. 94 | |? 6 |1. 00 |? 0. 30 |–4. 0420 | 4. 342 | 18. 8530 | |? 7 |1. 40 |? 0. 31 |? 1. 39545 | 1. 08545 | 1. 17820 | |? 8 |1. 70 |? 0. 32 |? 5. 47354 | 5. 5354 | 26. 56 | |? 9 |1. 30 |? 0. 24 |? 0. 036086 | 0. 203914 | 0. 041581 | |10 |1. 20 |? 0. 2 6 |–1. 3233 | 1. 58328 | 2. 50676 | |11 |1. 10 |? 0. 25 |–2. 6826 | 2. 93264 | 8. 60038 | |12 |0. 90 |? 0. 33 |–5. 4014 | 5. 73137 | 32. 8486 | |13 |1. 20 |? 0. 50 |–1. 3233 | 1. 82328 | 3. 32434 | |14 |1. 20 |? 0. 95 |–1. 3233 | 2. 27328 | 5. 16779 | |15 |1. 20 |? 1. 70 |–1. 3233 | 3. 02328 | 9. 14020 | |16 |1. 60 |? 2. 30 |? 4. 11418 | 1. 81418 | 3. 9124 | |17 |1. 50 |? 2. 80 |? 2. 75481 | 0. 045186 | 0. 002042 | |18 |1. 60 |? 2. 80 |? 4. 11418 | 1. 31418 | 1. 727 | |19 |1. 70 |? 2. 70 |? 5. 47354 | 2. 77354 | 7. 69253 | |20 |1. 90 |? 3. 90 |? 8. 19227 | 4. 29227 | 18. 4236 | |21 |1. 90 |? 4. 90 |? 8. 19227 | 3. 29227 | 10. 8390 | |22 |2. 30 |? 5. 30 |13. 6297 | 8. 32972 | 69. 3843 | |23 |2. 50 |? 6. 20 |16. 3484 |? 10. 1484 |? 102. 991 | |24 |2. 80 |? 4. 10 |20. 4265 |? 16. 3265 |? 266. 56 | |25 |2. 90 |? 4. 50 |21. 79 |? 17. 29 |? 298. 80 | |26 |3. 40 |? 6. 10 |28. 5827 |? 22. 4827 |? 505. 473 | |27 |3. 80 |? 7. 70 |34. 02 |? 26. 32 |? 6 92. 752 | |28 |4. 10 |10. 10 |38. 0983 |? 27. 9983 |? 783. 90 | |29 |4. 00 |15. 20 |36. 74 |? 21. 54 |? 463. 924 | |30 |4. 00 |18. 10 |36. 74 |? 18. 64 |? 347. 41 | |31 |3. 90 |24. 10 |35. 3795 |? 11. 2795 |? 127. 228 | |32 |3. 80 |25. 60 |34. 02 | 8. 42018 | 70. 8994 | |33 |3. 0 |30. 30 |34. 02 | 3. 72018 | 13. 8397 | |34 |3. 70 |36. 00 |32. 66 | 3. 33918 | 11. 15 | |35 |4. 10 |31. 10 |38. 0983 | 6. 99827 | 48. 9757 | |36 |4. 10 |31. 70 |38. 0983 | 6. 39827 |? 40. 9378 | |37 |4. 00 |38. 50 |36. 74 | 1. 76 | 3. 10146 | |38 |4. 50 |47. 90 |43. 5357 | 4. 36428 | 19. 05 | |39 |4. 60 |49. 10 |44. 8951 | 4. 20491 | 17. 6813 | |40 |4. 50 |55. 80 |43. 5357 |? 12. 2643 |? 150. 412 | |41 |4. 60 |70. 10 |44. 951 |? 25. 20 |? 635. 288 | |42 |4. 60 |70. 90 |44. 8951 |? 26. 00 |? 676. 256 | |43 |4. 70 |79. 10 |46. 2544 |? 32. 8456 |1,078. 83 | |44 |5. 00 |94. 00 |50. 3325 |? 43. 6675 |1,906. 85 | |TOTALS | | | |451. 223 |9,016. 45 | |AVERAGE | | | |? 10. 2551 |? 204. 92 | | | | | |? (MAD) |? (MS E) | Given that one wishes to develop a five-year forecast, trend analysis is the appropriate choice. Measures of error and goodness-of-fit are really irrelevant.Exponential smoothing provides a forecast only of deposits for the next year—and thus does not address the five-year forecast problem. In order to use the regression model based upon GSP, one must first develop a model to forecast GSP, and then use the forecast of GSP in the model to forecast deposits. This requires the development of two models—one of which (the model for GSP) must be based solely on time as the independent variable (time is the only other variable we are given). (b)? One could make a case for exclusion of the older data. Were we to exclude data from roughly the first 25 years, the forecasts for the later year

Defining Histology and How Its Used

Defining Histology and How It's Used Histology is defined as the scientific study of the microscopic structure (microanatomy) of cells and tissues. The term histology comes from the Greek words histos, meaning tissue or columns, and logia, which means study. The word histology first appeared in a 1819 book written by German anatomist and physiologist Karl Meyer, tracing its roots back to 17th-century microscopic studies of biological structures performed by Italian physician Marcello Malpighi. How Histology Works Courses in histology focus on the preparation of histology slides, relying on previous mastery of anatomy and physiology. Light and electron microscopy techniques are usually taught separately. The five steps of preparing slides for histology are: FixingProcessingEmbeddingSectioningStaining Cells and tissues must be fixed to prevent decay and degradation. Processing is required to prevent excessive alteration of tissues when they are embedded. Embedding involves placing a sample within a supporting material (e.g., paraffin or plastic) so small samples can be cut into thin sections, suitable for microscopy. Sectioning is performed using special blades called microtomes or ultramicrotomes. Sections are placed on microscope slides and stained. A variety of staining protocols are available, chosen to enhance the visibility of specific types of structures. The most common stain is a combination of hematoxylin and eosin (HE stain). Hematoxylin stains cellular nuclei blue, while eosin stains cytoplasm pink. Images of HE slides tend to be in shades of pink and blue. Toluidine blue stains the nucleus and cytoplasm blue, but mast cells purple. Wrights stain colors red blood cells blue/purple, while turning white blood cells and platelets other colors. Hematoxylin and eosin produce a permanent stain, so slides made using this combination may be kept for later examination. Some other histology stains are temporary, so photomicrography is necessary in order to preserve data. Most of the trichrome stains are differential stains, where a single mixture produces multiple colors. For example, Malloys trichrome stain colors cytoplasm pale red, the nucleus and muscle red, red blood cells and keratin orange, cartilage blue, and bone deep blue. Types of Tissues The two broad categories of tissues are plant tissue and animal tissue. Plant histology usually is called plant anatomy to avoid confusion. The main types of plant tissues are: Vascular tissueDermal tissueMeristematic tissueGround tissue In humans and other animals, all tissue may be classified as belonging to one of four groups: Nervous tissueMuscle tissueEpithelial tissueConnective tissue Subcategories of these main types include epithelium, endothelium, mesothelium, mesenchyme, germ cells, and stem cells. Histology may also be used to study structures in microorganisms, fungi, and algae. Careers in Histology A person who prepares tissues for sectioning, cuts them, stains them, and images them is called a histologist. Histologists work in labs and have highly refined skills, used to determine the best way to cut a sample, how to stain sections to make important structures visible, and how to image slides using microscopy. Laboratory personnel in a histology lab include biomedical scientists, medical technicians, histology technicians (HT), and histology technologists (HTL). The slides and images produced by histologists are examined by medical doctors called pathologists. Pathologists specialize in identifying abnormal cells and tissues. A pathologist can identify many conditions and diseases, including cancer and parasitic infection, so other doctors, veterinarians, and botanists can devise treatment plans or determine whether an abnormality led to death. Histopathologists are specialists who study diseased tissue. A career in histopathology typically requires a medical degree or doctorate. Many scientists in this discipline have dual degrees. Uses of Histology Histology is important in science education, applied science, and medicine. Histology is taught to biologists, medical students, and veterinary students because it helps them understand and recognize different types of tissues. In turn, histology bridges the gap between anatomy and physiology by showing what happens to tissues at the cellular level.Archaeologists use histology to study biological material recovered from archaeological sites. Bones and teeth are most likely to provide data. Paleontologists may recover useful material from organisms preserved in amber or frozen in permafrost.Histology is used to diagnose diseases in humans, animals, and plants and to analyze the effects of treatment.Histology is used during autopsies and forensic investigations to help understand unexplained deaths. In some cases, a cause of death may be evident from microscopic tissue examination. In other cases, the microanatomy may reveal clues about the environment after death.

CASE STUDY # 1 Example | Topics and Well Written Essays - 500 words

# 1 - Case Study Example However, some children may accurately develop these abilities but still have symptoms of language disorder. The speech disorders without known causes are usually referred to developmental language disorder. Nonetheless, numerous agents including brain injury, developmental problems, hearing loss, autistic spectrum disorder, and learning disabilities, may cause the language disorder in children (The New York Times 01). Notably, each agent has its unique symptoms, system of diagnosis, and treatment. Cori Williams, the national president announced during the Federal Lections of the year 2007 lobbied a national campaign against speech disorder among children. William wanted the speech pathology to be discussed extensively and sufficiently so that the government could adopt the Speech Pathology as an Australia’s policy (Speech Pathology Australia 01). The main areas that this public policy aimed at addressing included the functions of the speech pathology services to children with speech and language disorders. William also wanted the improved access of pathology services to children in remote areas. Finally, it lobby called for extended pathology services within the Medicare Allied Health Initiative (Speech Pathology Australia 01). There numerous exams and tests that are often conducted in children to determine the type of speech disorder they might be suffering. In some cases, a speech disorder in a child may be traced from the medical history of the child’s family (Simms 432). In such a case, it may be revealed that the child’s close relative might have suffered speech and language problems. Moreover, a child who might be suspected to be suffering from language or speech disorder can be taken for standardized expressive and receptive language tests. During this test, a language and speech neuropsychologist or therapist will be able to determine the same. Additionally,

Television Advertisements Research Paper Example | Topics and Well Written Essays - 750 words

Television Advertisements - Research Paper Example The paper realizes the prevalence of such behavior and in that acts as a measure to find a solution to the previously answered problem. The research question seeks to address the issue on a producer’s and a television company perspective. It aims to ensure that the involved stakeholders understand their social responsibility in protecting the children by developing relevant measures to combat the adverse effects on the children. The question relates to the significant problem of the various impacts of TV advertisements on children but adopts a different approach from the previous studies. This discussion declares that  the concern will not be a regulation by the prevailing regulative bodies, but an undertaking by advertisement houses and television companies meant to limit the effects of these ads on the children. The answer would work towards improving the overall health of the American population since there will be reduced cases of obesity and smoking among the children. Obesity and smoking are some of the primary causes of heart-related diseases and lung cancer in the United States and other areas in the world. Eliminating the negative influence of TV ads on the children will create a direct effect on the reduction in the prevalence rate of the associated diseases such as lung cancer and heart diseases.  The outcome can be included in the commercial television industry code of practice that will ensure the children are not influenced negatively by the ads and that parents are assured of their children’s safety.