Assignment Example | Topics and Well Written Essays - 250 words - 75

Assignment Example The song Waving Flag is appropriate to this scene because the audience is aware that Will is joining for the ride as an equal, and he will see through the task. 4. Will wanted to take what he earned. He saved the posse on the ridge, grabbed Doc and hustled him out of the tunnels and started the cover stampede at the climax of the film after breaking away from the railroad man. Will went the distance to help his father get their rightful property back. 5. The LaBarge article specifies heroism as a larger than life character limiting the possibilities. Will is a 14-year-old boy about to become a 14- year-old man. Will bides his time and then saves the posse. He joins as an equal. He actively participates in the run to the station, a very dangerous endeavor. Will preferred to do great things than accept what was given him. Will acted heroically in every sense. At the end of the film, Will has the opportunity to shoot Ben. He chooses not to because he understands Ben’s role in getting to the train on time. Will respects Ben’s decision to help complete the task, but despises Ben for leading the gang of thugs that ultimately led to his father’s death. Will chose the righteous path and let Ben board the

The Government Should Not Cut Education Budgets To solve Its Financial Research Paper

The Government Should Not Cut Education Budgets To solve Its Financial Problems - Research Paper Example The proposed way to give the United States a continued competitive edge in today’s complex world is to give students in the U.S. an excellent education. Educational funding should not be used to solve the national budget crisis because: 1) money needs to be cut from the national defense budget, which is overcompensated; 2) education is too important to be relegated to lesser funding; and 3) cutting the budget for education will promote an elitist society because many people will see an education as a privilege and not a right. A Bloated National Defense Budget Defense spending being cut could be the solution to solving not only the nation’s budget crisis, but could also be used to funnel more money into education as a result. More money for the budget would then allow one to logically then conclude there would be more money for education in the national budget. "Consistent with US military needs and declining threats, defense spending will be cut [emphasis added], which will help reduce the deficit and provide funds to invest for economic growth."1 Unfortunately, military spending in the United States has gotten out of control. â€Å"Government control over the military's budget is also deficient, because the government lacks the ability to estimate the army's needs and to evaluate the manner in which its budget is utilized.†2 First when Pres. George W. Bush invaded Iraq without consulting Congress or the international community, he pledged American troops to eight years of combat in Iraq—from which the country still has not recovered. It has been ten years that the United States has been engaged in combat in Afghanistan as well. Both wars have drained the U.S. of its budget surplus which was in effect when Pres. Bill Clinton left office. That surplus quickly dwindled with the advent of the two wars. If less money was spent on wars and more money was pumped into education, perhaps schools all across the country would not require rest ructuring due to failing Adequate Yearly Progress (AYP) benchmarks since the advent of the No Child Left Behind Act. America, frankly speaking, must prioritize in order to realize what is really important—having an educated public to make good decisions, or keeping the American people safe from all the possible dangers of the world by getting involved in every major conflict in the globe that could potentially endanger vested U.S. interests. These are difficult choices but they must be made, because the country’s energies are being divided in a haphazard fashion. Education: Too Important to Ignore The problems began when President Bush got elected to office in 2000 and started whittling down the surplus Bill Clinton had left behind when he left the office of the President—severely making cuts to education. â€Å"[President Bush’s] budget cut†¦funding for elementary and secondary education, denying 3.2 million children the extra reading and math help they were promised by the so-called No Child Left Behind Act.†3 Although President Barack Obama came to office in January of 2009 promising â€Å"change,† unfortunately he is also making cuts to education, including grants for history. â€Å"The President’s fiscal year 2012 budget request for the Department of Education once again eliminates Teaching American History Grants (TAH) as a separately funded program†¦

Analysis of Ethical Decision Making Literature review - 102

Analysis of Ethical Decision Making - Literature review Example Nevertheless, the ethical roles of managers are that their decision-making processes be aimed at maximizing the wealth of the shareholders and the business in general. As such, the utilization of the ethics principal in decision-making processes ensure that the laws are strictly observed and that individuals have no chance to manipulate them through dubious activities such as corruption, fraud among others.   Most businesses across the world have emphasized on ethical leadership which articulates that there must be a combination of personal morality and professional morality in order to generate an ethical decision-making process. In terms of personal morality, there must be a combination of characters such as honesty, trustworthiness, integrity among others. Similarly, the professional aspect should entail forthrightness in relation to the business and society at large. Additionally, morality involves behaviors such as doing the right things, being concern about the welfare of other people, and being open with issues. Consequently, managers are expected to practice various approaches that are related to ethical decision making such as egoism approach, deontology approach and utilitarianism approach which ought to serve as guidance in their profession.  Ã‚   An egoism approach in ethical decision making revolves around the actions of an individual pertaining to making a particular decision. It argues that any good action is best approved by an individual.  Ã‚  

Dependent Variables Depression Anxiety Stress Psychology Essay

Dependent Variables Depression Anxiety Stress Psychology Essay Adolescence is a crucial phase in life, during which the teenagers can succumb to conditions like depression, anxiety and stress which can increase chance of mental illnesses. The present study aims at measuring the level of depression, Anxiety and Stress of the 10th std students between the age group of 14-15 years studying in rural and urban High Schools. The sample of the study obtained using purposive sampling consisted of 60 students (30 urban and 30 rural) drawn from two schools one from the metropolitan city of Hyderabad and the other from Nagarkurnool, Mahboobnagar district. In order to carry on the research, the investigator used DAS scale (Depression, Anxiety and Stress) developed by the  University of New South Wales (Australia) 1995 . Adolescence is a transitional stage of physical and psychological human development generally occurring between puberty and legal adulthood. The period of adolescence is most closely associated with the teenage years, although its physical, psychological and cultural expressions can begin earlier and end later. In adolescence, cognitive developments result in greater self awareness of others and their thoughts and judgments, the ability to think about abstract, future possibilities, and the ability to consider multiple possibilities at once. As a result, adolescents experience a significant shift from the simple, concrete, and global self descriptions typical of young children; as children, they defined themselves with physical traits whereas as adolescents, they define themselves based on their values, thoughts and opinions. The competitive nature of present day educational system has great influence on the youngsters. Every student is faced with a high demand to surpass oneself. Fa ilure to do so may often be considered as a mark of the failure of ones existence by the youngster, whose limited life experience does not permit him/her to seek an alternative. Home and school are the centers of these problems. Most of the conflicting issues arise because of the fear of loss of friends, parents. They become entangled in the grip of insecurity. Most of the time they have this fear that if they are not able to meet expectations of their near and dear ones then he or she will lose them. The additional burden of general expectations of parents, friends, teachers etc stresses the youngster and when confronted with failure hurts their self-esteem. Adolescents thus see themselves in highly conflicting situations, as they often expect to perform their best in the academic field. They often get frustrated, anxious and stressed that suicide becomes their only escape. It is important to realize that stress affects memory and the psychological well being of students. Academic stress particularly among students has been assessed as one of the most important causal factors for adolescents depression. The term depression is difficult to define because of the ambiguity inherent in it. Depression as a medical condition in which a person feels very sad and anxious and often has physical symptoms such as being unable to seep etc Inability to do so leads to stress and this begins to wear out people and the result is most often depression. Stress is the major factor influencing depression. Depression is a state of emotional dejection. Extreme feelings of hopelessness, sadness, isolation, worry, loss of interest or pleasure, feelings of guilt or low self worth, disturbed sleep or appetite, low energy and poor concentration are the signs of depression. According to salmons (1997), depression is a state of low mood and aversion to activity that can affect a persons thoughts, behavior, feelings and physical well-being. . The depressed person has negative thoughts, low self-esteem, the feeling of the hopelessness about the future, loss of motivation, change in aptitude, sleep disturbance, an d loss of energy. Depression is closely related to anxiety, most depressed individuals have high levels of anxiety (Mac Leod,Byrne,Valentine,1996) Anxiety is a subjective state of internal discomfort. . It is a normal emotion with adaptive value, in it that acts as a warning system to alert a person to impending danger. Anxiety is a mood -state characterized by negative effects, bodily symptoms of tensions and apprehension about the future (American Psychological Association,1995).psychologists believe that a small amount of anxiety helps to arouse individuals to perform better(Yerkes and Dodson,1908). However, a large amount of anxiety might hinder performance. Anxiety is considered to be a universal phenomenon existing across cultures, although its contexts and manifestations are influenced by cultural beliefs and practices (Good Kleinman, 1985; Guarnaccia, 1997). Generally, more girls than boys develop anxiety disorders and symptoms. Adolescent girls report a greater number of worries, more separation anxiety, and higher levels of generalized anxiety (Campbell Rapee, 1994; Costello, Egger Angold, 2003; Poulton, Milne, Cra ske Menzies, 2001; Weiss Last, 2001). Anxiety is known to affect both learning and performance (McDonald, 2001), no empirical research has explored the relationship between adolescent anxiety and school type, school choice, or mode of instruction. In India ,the main documented cause of anxiety among adolescents is parents high educational expectations and pressure for academic achievement. Stress is a state of mind involving demand on physical or mental energy, a state or circumstance that disturbs the normal, physical and mental health of a person. Stress is a consequence of or a general response to an action or situation that places special physical or psychological demands, or both, on a person. As such, stress involves an interaction of the person and the environment. According to Hans Selye (1974) stress is a response of the body to any stimulus that upset the individuals homeostasis. Any experience that affects ones homeostasis is considered to be stress (Rice, 1992). Hans Selye further defined stress as the nonspecific response of the body to demand made upon it. Stress is a condition and the stimuli causing it called stressors or triggers. Stress can be either positive or negative and can be further divided into two groups which are external and internal (Selye, 2009). Early in the 20th century, it was believed that children and adolescents could not suffer from depression. Later in the century, psychologists changed their minds and accepted that children can get depressed, however many agreed childhood depression is different from adult depression (Clarizio, 1989). A major cause or trigger of depression in the adolescents is thought to be stress. A predisposition to depression may also play a role; nonetheless, the additive stresses of every day adolescent life often appear to trigger depression (Clarizio 1989). There is a complex relationship between depression and suicide. Many depressed patients are suicidal and conversely most but not all suicidal individual manifest depressive mode and symptoms if not depressive illness (Pfeffer, 1989.) Adolescence can be a crucial phase in every ones life. There can be a lot emotional upheaval and stress. Adolescents can experience stress from family discord at home as well as having difficulties with peer relationships at school and academic performance. Adolescence during this period under goes with major changes body changes, change in thought pattern, and changes in feelings. Strong feelings of stress, confusion, fear and uncertainty, as well as pressure to succeed, and the ability to think about things in new ways influence a teenagers problem solve and decision making abilities. Majority of the adolescents undergo stress, whatever the sources may be internal or external it hampers the major functioning of the body. Most of the youngsters face multiple problems in their life. Each individual has to cope with different kinds of pressures laid down by the society and family. On the verge of coping those pressures, an individual himself or herself unconsciously frames a net and is caught in the same. Most of the students are pseudo they keep their own self in a rosy world and when they are confronted with the actual situation, they are unable to handle and thus it throws them to a stressful situation. The present study aim on the level of depression, anxiety, stress in the urban and the rural students. Contemporary views on the structure of negative emotion have largely arrived from the well documented observation that scores from various instruments designed to measure the levels of depression and anxiety tend to be highly correlated. (Clark and Watson 1991), and high rates of co morbidity exist among the anxiety and mood disorders (Andrew,1996). Clark and Watson (1991) proposed a tripartite model of anxiety and depression, which claims that both states are characterized by symptoms of elevated negative affect or general distress (example, distress, irritability),but that anhedonia (low levels of positive effect, eg. happiness, confidence, enthusiasm) is specific to depression and physiological hyper arousal ( autonomic symptoms, example trembling , sweating) is unique to anxiety. An urban area is characterized by higher population density and vast human features in comparison to areas surrounding it. Urban areas may be cities, towns or conurbations, but the term is not commonly extended to rural settlements such as villages and hamlets. Urban areas are created and further developed by the process of urbanization. Measuring the extent of an urban area helps in analyzing population density and urban sprawl, and in determining urban and rural populations. Rural areas or the countryside are areas of land that are not urbanized, though when large areas are described, country towns and smaller cities will be included. They have a low population density, and typically much of the land is devoted to agriculture and there may be less air and water pollution than in an urban area. About 80 percent of the Indian population lives in villages. When travelling through the length and breadth of this subcontinent, one can really visualize the difference between rural and urb an in India. There is a big difference between urban and rural India. One of the major differences that can be seen between rural India and urban India is their standards of living. People living in urban India have better living conditions than those living in the rural parts of India. There is a wide economic gap between rural urban India. Rural India is very poor when compared to urban India. Another difference that can be seen between urban and rural India, is their education. In rural India, the parents seldom educate their children, and instead, make their children work in the fields. Poverty, and lack of sufficient infrastructure, can be attributed to the lack of education in rural India. Methodology Design: The research design used in this study is ex-post facto research design,as it explores the already existing causal conditions between the considered sample groups. The hypothesized the level of depression anxiety stress is higher in the urban school student then the rural school student background.To choose the participants purposive sampling methods were employed Sample The participants consisted of 60 students belonging to the age group of 14-15 years studying in rural and urban high schools. The sample of the study drawn from two schools one from the metropolitan city of Hyderabad and the other from Nagarkurnool,Mahboobnagar district. The students were divided into two groups of 30 each, namely urban and rural. Instruments Depression, Anxiety and Stress Scale: In the present study, DASS-42 versin of the perep pencil test was used on the 10th STD students studying in urban and rural high schools. The DASS is a 42- item self report instrument designed by Lovibond and Lovibond (1995), to measure the three related negative emotional states of depression, anxiety and stress. Each of the three DAS scales consists of 14 items divided into subscales of 2to 5 items having similar content to make up a total of 42 items which are placed in a random order in these scales. The depression scales assesses dysphoria, hopelessness, devaluation of life, self-deprecation, lack of interest or involvement, anhedonia and inertia (Lovibond, S.H.Lovibond,P.F.;1995). The anxiety scale contains autonomic arousal, skeletal muscle  effects, situational anxiety and subjective experience of anxious affect (Lovibond et al., 1995). The stress scale being sensitive to chronic non-specific arousal assesses difficulty in relaxing, ner vous arousal, and being easily upset or agitated, irritable or over-reactive and impatient(Lovibond et al.,1995). The items are to be rated on a 4 point Likert Scale of 0 to 3. the option 3 to 0 signify how the sentence applied to the individual with the response ranging in the past week. The rating scale is as follows: 0 Did not apply to me at all 1 Applied to me to some degree, or some of the time. 2 Applied to me a considerable degree, or a good part of the time. 3 Applied to me very much, or most of the time. The option the participant chooses for each item (ie.0,1,2or 3) is regarded as the score for that item and the sum of the relevant items belonging to each one of the three subscales gives the scores for that subscale. These scores are then interpreted to determine the DAS level of the participant. Crawford and Henry (2003) found the internal consistency of the DASS subscales to be high with Cronbachs alphas of 0.94, 0.88 and 0.93 for depression, anxiety and stress respectively. According to Lovibond et al.(1995) the reliability scores of the scales in terms of Cronbachs alpha scores rate the Depression scale at 0.91, the Anxiety scale at 0.84 and the Stress scale at 0.90 in the normative sample. The measns and standard deviations for each scale are 6.34 and 6.97 for depression, 4.7 and 4.91 for anxiety and 10.11 and 7.91 for stress respectively.(Lovibond et al.,1995) Procedure: The test was administered on a one to one basis. Each participant was approached and was briefed about the purpose of the study. The consent was taken before conducting the test and was allowed to withdraw from the study whenever the participant wanted. The questionnaire was given to the subject and was asked to answer the questionnaire carefully based on how many times their parents might have used it. Instructions on paper were read out by the researcher in order to clear all doubts. The participant was asked to work through the items as quickly and as accurately as possible, including a cross mark against the appropriate opinion. Every doubts and any kind of ambiguity that arose in the participants mind were clarified. After the test was accomplished, the researcher expressed her gratitude to the participant for the cooperation. Later the questionnaire was collected and was statistically analyzed by the researcher. The descriptive statistics, Mean and Standard Deviation and the in ferential statistics t-ratio and p-value are used for analysis of the data.

Dell :: essays research papers

Brief Background Summary   Ã‚  Ã‚  Ã‚  Ã‚  How would you like to take a $1,000 investment to start a company and turn it into 41 billion dollars revenue producing enterprise in just twenty years? I’m sure everyone would, but it became a reality for one man, Michael Dell. Michael Dell is the current CEO of Dell, Inc, one of the largest companies in the world. He started this company back in 1984 with just $1,000 at the age of 19 and now at the age of 39 he is the fourth richest man in America. (DuBrin 313) â€Å"Mr. Dell became the youngest CEO of a company to have their company ranked in Fortune 500. (Dell Annual Report) Today, Dell Inc. is ranked incredibly number 125.   Ã‚  Ã‚  Ã‚  Ã‚  Dell Inc., which is located at Building 2 One Dell Way, Round Rock, Texas, is one of the premier companies in computer products and services. The company current employs approximately 46,000 people and is the â€Å"biggest online seller of computers. Dell’s primary products include enterprise systems, household PCs and notebook computers. They are also currently trying to expand their way into the printer market even further.   Ã‚  Ã‚  Ã‚  Ã‚  He started with the idea of taking plain computers and adding parts to it to make it a more appealing to computers buyers. â€Å"Dell hired a small staff and began selling his machines over the phone and through the mail. Several years later he created the industry’s first on-site service program, in which the repair technician visits your office or home.† (DuBrin 313) Problem Identification   Ã‚  Ã‚  Ã‚  Ã‚  In creating a multi-billion dollar company there were issues and problems that had to be identified and made. In being such a rapid growing company, Mr. Dell had to fill up his company with top professionals and visionaries that could continue the success. The company needed to find ways to stay competitive and stay focused on their vision. For a company to stay growing at the pace they were, which was about 50 percent a year in the 90’s, it needed to continue to add strong employees and keep the motivation going.   Ã‚  Ã‚  Ã‚  Ã‚  In the computer market there are a lot of competitors, just waiting for someone to make a mistake. The fight over market share is very competitive. Dell needs to stay very aware of their business competition and what they are doing. I think that Dell has done very well picking up most of the trends and consumer needs to come out with new ideas, to stay on top.

Hole in the Wall

Hidden treasure in the big city One thing that all people can relate to is the love for wonderful food. You never see people not wanting to share a great dining experience with friends, family, or the world through social websites. In every person you see the excitement of trying new foods and restaurants. We have all had that perfect dining experiences that have made an imprint on our lives. Well what I want to share with you is a place that is hidden within the growing metro of Magmata in an unlikely place.This little place is a hole in the wall restaurant allied Baa Not, this little restaurant strives to deliver an authentic Vietnamese experience to their customers in the big city of Magmata. Let me go into the history of Baa Non briefly. As explained by the owner, this restaurant is being managed by two men a Filipino and a Vietnamese, who got all the dishes from his grandmother. Which also became the name since Baa Non meaner grandmother in the father's side in Vietnamese. Baa N on puts pride that they serve some of the best authentic Vietnamese food in manila.Well enough about the history, let's get down to business, I know what you all want o hear. Let's talk about the FOOD. Well in total I tried nine different dishes each with its own individual flavor that stood out from the rest. First off the bat would be the Pooh Boo which is a homemade beef noodle soup with spices and fresh herbs. The broth was tasty without being overpowering that would normally overpower the flavors of the spices and the fresh herbs but this was not the case you could taste every flavor in the dish.The balance of the dish is worth mentioning since you rarely get such good beef noodles which is not overpowering in taste. What also interested e was the fact that in the menu, there was a guide on how to eat the dish. First, you need to squeeze the lime on your pooh then add the bean sprouts and the basil after that, put the chili depending on your taste and finally, add the black bea n paste and stir or mix it. We decided to try the two kinds of spring rolls they had to offer, it was Go Con and Chaw Gig and the difference between the two is that Go Con was a fresh spring roll while the Chaw Gig was fried spring roll.Go Con is made of pork, shrimp and vermicelli attractively rolled in rice paper served with their famous meant sauce which I think was a marvelous peanut sauce. It had lots of flavor and also was not to thick a great accompaniment to the Go Con . The Chaw Gig is a mixture of ground pork, shrimp, taro, and black fungus wrapped and deep fried and served with fresh greens and traditional NCO AMA dipping sauce. Between the two spring rolls I have to put my money on the Go Con which was very well balanced and add that with the peanut sauce, you are asking for a recipe for success.We had four main dishes for our meal. We chose all the house-favorites/recommendations. The first of the dish was the Boo Luck Lack stir-fried spiced beef served with fried basil . The beef was cooked medium rare with sesame seeds and the beef had a good flavor and when eaten with the fried basil it blends since the basil was fried the intense flavor was mellowed to allow a good combination with the beef. The owner told us meat could not meet the standards of the owners.The second dish was the Ga Among which is grilled chicken in rich lemongrass marinade. The grilling of the chicken was amazing it was moist but also tasty and the taste of the lemon grass was intense which gave it a nice flavor. The third is had Soon Mongo Xa, pork rabbets flavored with their special lemongrass fusion, pan-fried and served with soy-chili sauce. This dish was a tad plain but I still got the good fusion of flavors but it was not strong enough to compare to the other two dishes I tried earlier.The That Hoe KHz Truing is a sweet-savory dish of pork and egg simmered in coconut Juice this dish I think was a mistake to order. It was very salty and didn't fit with all the other dishe s we ordered. The main course was good overall with three hits and a miss. We had Ban Dan Loon for our dessert. This dessert made everything worthwhile; the dish is eke a mixture of Spain-Spain and stating but with a filling in the middle topped with a coconut cream and sesame seeds.The dish was sweet but not too sweet to make you forget all the other flavors you had during your meal. I think this is one of the highlights of our dining experience. For the drink I had their new Vietnamese Blended Mocha which is not yet in their menu since the owner is still testing it out and is looking for feedback from guest before placing it on their menu. The flavor of the strong coffee and the nice sweetness of the chocolate made the drink a big plus n my books. The drink was balanced and very good since that day was quite hot.Well I guess this has to conclude recount of my visit to Baa Not. I will leave you with this, everyone has good dining experiences but it takes a good combination of servi ce and food to make this experience a memorable one and I must say Baa Non has made an impression of me. I for one am confident to call it a hidden treasure. So why not try it yourself to see if what I wrote is true. I bid thee farewell and hope you had as much fun reading as I had recalling and writing this article.

Forecasting †Simple Linear Regression Applications

STATISTICS FOR MGT DECISIONS FINAL EXAMINATION Forecasting – Simple Linear Regression Applications Interpretation and Use of Computer Output (Results) NAME SECTION A – REGRESSION ANALYSIS AND FORECASTING 1) The management of an international hotel chain is in the process of evaluating the possible sites for a new unit on a beach resort. As part of the analysis, the management is interested in evaluating the relationship between the distance of a hotel from the beach and the hotel’s average occupancy rate for the season. A sample of 14 existing hotels in the area is chosen, and each hotel reports its average occupancy rate.The management records the hotel’s distance (in miles) from the beach. The following set of data is obtained: Distance (miles)0. 10. 10. 20. 30. 40. 40. 50. 60. 7 Occupancy (%)929596908996908385 Continue Distance (miles)0. 70. 80. 80. 90. 9 Occupancy (%)8078767275 Use the computer output to respond to the following questions: a) A simple linear regression was ran with the occupancy rate as the dependent (explained) variable and distance from the beach as the independent (explaining) variable Occpnc=b[pic]+b[pic](Distncy) What is the estimated regression equation?The regression model is: Occpnc = b[pic] + b[pic](Distncy) The estimated regression equation is: OCCUPNC = 99. 61444 – 26. 703 DISTNCY b) Interpret the meaning behind the values you get for both coefficients b[pic] and b[pic]. b[pic]=99. 61444, represent the y-intercept as well as the starting figure for the distance coverage. This is the amount of distance in miles that the hotel is from a beach. b[pic] = 26. 703, represents the percentage of occupancy a hotel has depending on the distance of the hotel from a beach. c) What sort of relationship exists between average hotel occupancy rate and the hotel’s distance from the beach?Does this relationship make sense to you? Why or why not? Both distance and occupancy have a direct relationship. This is true because closer the hotel is to the beach, the higher the chance that the hotel’s occupancy will be greater. If a person is going to stay at a hotel, chances are they are on vacation. People on vacation love to spend time on a beach for relaxation purposes, so it would only make sense that a hotel that is closer to the beach will have a higher occupancy rate. d) Interpret the R-Square value in your computer output R-Squared = 0. 848195 = 84. 8195 ) Predict the expected occupancy rate for a hotel that is (i) one mile from the beach in that area, (ii) one and half miles from the beach. i. OCCUPNC = 99. 61444 – 26. 703 (1) = 99. 61444 – 26. 703 = 72. 911 ii. OCCUPNC = 99. 61444 – 26. 703 (1. 5) = 99. 61444 – 40. 055 = 59. 559 f) In your mind, what other variables contribute positively or negatively to hotel occupancy besides distance from the beach? Other variables that contribute positively or negatively to hotel occupancy besides distance fr om the beach include the distance of restaurants, shopping centers, and airport from the hotel.The closer theses variables are to the hotel the chances the occupancy rate will be higher. In addition, other variables may include what type of amenities that are offered by the hotel, customer service, and rating of the hotel. g) At a level of significance, ? = 0. 01 or 1 percent test the following pair of hypotheses: H[pic]: b[pic]= 0 H[pic]: b[pic]? 0 On the model: Occpnc=b[pic]+b[pic](Distncy) What is your conclusion and why that particular conclusion? COMPUTER OUTPUT – PART 1 INTERNATIONAL HOTEL REGRESSION FUNCTION & ANOVA FOR OCCPNCY OCCPNCY = 99. 61444 – 26. 703 DISTANCER-Squared = 0. 848195 Adjusted R-Squared = 0. 835545 Standard error of estimate = 3. 339362 Number of cases used = 14 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 747. 68 1 747. 68390 67. 04880 0. 000002 Residual 133. 82 12 11. 15134 Total 881. 50 13 COMPUTER OUTPUT  œ PART 1 INTERNATIONAL HOTEL REGRESSION COEFFICIENTS FOR OCCPNCY Two-Sided p-value Variable Coefficient Std Error t Value Sig Prob Constant 99. 61444 1. 4107 51. 31933 0. 000000 DISTANCE -26. 70300 3. 26110 -8. 18833 0. 000002 * Standard error of estimate = 3. 339362 Durbin-Watson statistic = 1. 324282 MULTIPLE REGRESSION 2) You want to find out factors that explain an individual’s weekly savings. You are given a set of data below: Sampled WeeklyHouseFoodEntertain/Weekly IndividualIncomeRentExpenseExpenseSavings Case 1$25085952520 Case 2$1907590100 Case 3$4201401204050 Case 4$340120130040 Case 5$2801101003015 Case 6$310801252525 Case 7$5201501405580 Case 8$440175155450 Case 9$36090852095 Case 10$3851051353530Case 11$2058010505 Case 12$26565951515 Case 13$19550801020 Case 14$25090100250 Case 15$4801401604545 A multiple regression was ran with WEEKLY SAVINGS as the DEPENDENT VARIABLE and the rest as the INDEPENDENT VARIABLES. SAVINGS = b[pic][pic]+ b[pic]INCOME + b[pic]RENT + b [pic]FOOD + b[pic]ENTERT a) What is the estimated multiple regression equation? SAVINGS = 23. 14156 + 0. 591446 INCOME – 0. 341793 RENT – 1. 119734 FOOD – 0. 907868 ENTERT b) What relationship exists between (i) SAVINGS and INCOME? , SAVINGS and RENT? , SAVINGS and FOOD expense, SAVINGS and ENTERTAINMENT expense?There are no direct relationship between saving and income, savings and rent, savings and food expense, and savings and entertainment expense. c) Which of the independent (explaining) variables are (is) significant in the multiple regression and which ones are (is) not significant (use ? = 0. 05 level of significance). Are the results in line with Maslow hierarchy of needs? Explain. COMPUTER OUTPUT PART I WEEKLY SAVINGS REGRESSION FUNCTION & ANOVA FOR SAVINGS SAVINGS = 23. 14156 + 0. 591446 INCOME – 0. 341793 RENT – 1. 119734 FOOD – 0. 907868 ENTERT R-Squared = 0. 917562 Adjusted R-Squared = 0. 70454 Standard error of estimate = 10. 9635 Number of cases used = 12 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 9364. 86 4 2341. 21 19. 47795 0. 000677 Residual 841. 39 7 120. 198 Total 10206. 250 11 COMPUTER OUTPUT PART II WEEKLY SAVINGS REGRESSION COEFFICIENTS FOR SAVINGS Two-Sidedp-value Variable Coefficient Std Error t Value Sig Prob Constant 23. 14156 18. 34071 1. 26176 0. 247451 INCOME 0. 59145 0. 07388 8. 00526 0. 000091 RENT -0. 4179 0. 19849 -1. 72199 0. 128743 * FOOD -1. 11973 0. 24633 -4. 54565 0. 002650 ENTERT -0. 90787 0. 32460 -2. 79689 0. 026643 * indicates that the variable is marked for leaving Standard error of estimate = 10. 9635 Durbin-Watson statistic = 1. 683103 3) REGRESSION ANALYSIS A business person is trying to estimate the relationship between the price of good X and the sales of good Y of certain groups of staples. Tests in similar cities throughout the country have yielded the data below: PRICE (X)SALES (Y) $2010,300 $259,100 $308,200 $356,500 $405,100 $502,300A simple linear regression of a model SALES(Y) = b[pic] + b[pic]PRICE(X) Was run and the computer output is shown below: PRICE OF X / SALES OF Y REGRESSION FUNCTION & ANOVA FOR SALES(Y) SALES(Y) = 15907. 14 – 269. 7143 PRICE(X) R-Squared = 0. 994999 Adjusted R-Squared = 0. 993749 Standard error of estimate = 230. 9143 Number of cases used = 6 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 4. 24350E+07 1 4. 24350E+07 795. 83480 0. 000009 Residual 213285. 70000 4 53321. 43000 Total 4. 26483E+07 5PRICE OF X / SALES OF Y REGRESSION COEFFICIENTS FOR SALES(Y) Two-Sidedp-value Variable Coefficient Std Error t Value Sig Prob Constant 15907. 14000 332. 34250 47. 86370 0. 000001 PRICE(X) -269. 71430 9. 56076 -28. 21054 0. 000009 * Standard error of estimate = 230. 9143 Durbin-Watson statistic = 1. 687953 QUESTIONS a) What is the estimated equation of the model: SALES(Y) = b[pic] + b[pic]PRICE(X)? SALES(Y) = 15907. 14 – 269. 7143 PRICE(X) b) What sort of relationship exists between SALES OF Y and the PRICE OF X? Does this relationship make sense? Why or why not?There is a direct relationship between Sales of Y and the Price of X. The lower the price the higher are the sales. This makes sense because if the price is lower, a person will purchase more items. c) What can you say about GOOD Y and GOOD X (a good can be an item, a commodity, etc. ). Name a pair of good X and good y that can display this kind of relationship. Suppose the price of candy is $0. 50/lb, the sales of the candy versus the same type of candy that is $0. 80/lb would yield more sales because of the price. The price of the candy directly affects sales in this instance because a person would buy more candy at $0. 0/lb versus $0. 80/lb. 4) REGRESSION ANALYSIS A business person is trying to estimate the relationship between the price of good X and the sales of good Z of certain groups of staples. Tests in similar cities throughout the country have yielded the data belo w: PRICE (X)SALES (Z) $153300 $203900 $254750 $305500 $406550 $507250 A simple linear regression of a model SALES (Z) = b[pic] + b[pic]PRICE(X) Was run and the computer output is shown below: PRICE OF X / SALES OF Z REGRESSION FUNCTION & ANOVA FOR SALES(Y) SALES(Z) = 1740. 686 + 115. 5882 PRICE(X) R-Squared = 0. 977573 Adjusted R-Squared = 0. 71966 Standard error of estimate = 255. 2152 Number of cases used = 6 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 1. 13565E+07 1 1. 13565E+07 174. 35450 0. 000190 Residual 260539. 20000 4 65134. 80000 Total 1. 16171E+07 5 PRICE OF X / SALES OF Z REGRESSION COEFFICIENTS FOR SALES(Z) p-value Variable Coefficient Std Error t Value Sig Prob Constant 1740. 68600 282. 52800 6. 16111 0. 003522 PRICE(X) 115. 58820 8. 75381 13. 20434 0. 000190 *Standard error of estimate = 255. 2152 Durbin-Watson statistic = 1. 240299 QUESTIONS a) What is the estimated equation of the model: SALES(Z) = b[pic] + b[pic]PRICE(X)? SALES (Z) = 17 40. 686 + 115. 5882 PRICE(X) b) What sort of relationship exists between SALES OF Z and the PRICE OF X? Does this relationship make sense? Why or why not? There is a direct relationship between Sales of Y and the Price of X. The higher the price the higher are the sales. This makes sense as it relates to supply and demand. The higher the demand and for the product and unavailability of the product, the price will go up even though sales may he same due to the price increase the sales amount will be higher. c) What can you say about GOOD Z and GOOD X (a good can be an item, a commodity, etc. ). Give an example of good X and good Z that can display this kind of relationship A prime example that displays this kind of relationship is gas. The price of gas has been going up for sometime now. The demand for it is high, but the supply of is low. Therefore, even though the amount of sales may stay constant, the dollar amount will be higher because the price is higher. Chi-Squared Test #1M&M , makers of Chocolate Candies, conducted a national poll in which more than ten million people indicated their preference for a new color. The tally of this poll resulted in the replacement of tan-colored M&Ms with a new blue color. In the brochure â€Å"Colors,† made available by M&MS Consumer Affairs, the distribution of colors for the plain candies is as follows: BROWNYELLOWREDORANGEGREENBLUE 30%20%20%10%10%10% In a follow-up study two years later, samples of 1-pound bags were used to determine whether the reported percentages were still valid. The following results were obtained (observed) for one sample of 506 plain candiesBROWNYELLOWREDORANGEGREENBLUE 17713579413638 Use a level of significance ( = 0. 05 to determine whether these data support the percentages reported by the company Hint: To obtain the Expected Number of multiply the sample value (506) by each color’s probability, i. e. , E = BROWNYELLOWREDORANGEGREENBLUE 30% (506)20%(506)20%(506)10%(506)10%(506)1 0%(506) Then compute the Chi-Squared. H[pic]: f[pic], f[pic], f[pic], f[pic], f[pic], f[pic] hold previous year’s patterns or percentages H[pic]: At least one frequency differs from the previous year’s pattern or percentages E = 506/6 = 84. 33 [pic]=(177 –84. 33)[pic]/84. 33+(135 – 84. 33)[pic]/84. 33 + (79 – 84. 33)[pic]/84. 33+(41 – 84. 33)[pic]/84. 33)+(36 – 84. 33)[pic]/84. 33+(38 – 84. 33)[pic]/84. 33) ([pic]=101. 937 + 30. 49217 + 0. 333215 + 22. 24069 + 27. 67367 + 25. 4293 ([pic]=208. 106. This is the computed ([pic]-value. ( = 0. 05 d. f. = 6 – 1 = 5. Go to ([pic]-tables at ( = 0. 05, and d. f. = 5, you will get CRITICAL ([pic]-value = 11. 070. Since Computed ([pic]-value is greater than Critical ([pic]-value REJECT NULL H[pic]:P[pic] = P[pic] = P[pic] = P[pic] = P[pic] ALTERNATIVE H[pic]: At least one P is different is correct Forecasting – Simple Linear Regression Applications STATISTICS FOR MGT DECISIONS FINAL EXAMINATION Forecasting – Simple Linear Regression Applications Interpretation and Use of Computer Output (Results) NAME SECTION A – REGRESSION ANALYSIS AND FORECASTING 1) The management of an international hotel chain is in the process of evaluating the possible sites for a new unit on a beach resort. As part of the analysis, the management is interested in evaluating the relationship between the distance of a hotel from the beach and the hotel’s average occupancy rate for the season. A sample of 14 existing hotels in the area is chosen, and each hotel reports its average occupancy rate.The management records the hotel’s distance (in miles) from the beach. The following set of data is obtained: Distance (miles)0. 10. 10. 20. 30. 40. 40. 50. 60. 7 Occupancy (%)929596908996908385 Continue Distance (miles)0. 70. 80. 80. 90. 9 Occupancy (%)8078767275 Use the computer output to respond to the following questions: a) A simple linear regression was ran with the occupancy rate as the dependent (explained) variable and distance from the beach as the independent (explaining) variable Occpnc=b[pic]+b[pic](Distncy) What is the estimated regression equation?The regression model is: Occpnc = b[pic] + b[pic](Distncy) The estimated regression equation is: OCCUPNC = 99. 61444 – 26. 703 DISTNCY b) Interpret the meaning behind the values you get for both coefficients b[pic] and b[pic]. b[pic]=99. 61444, represent the y-intercept as well as the starting figure for the distance coverage. This is the amount of distance in miles that the hotel is from a beach. b[pic] = 26. 703, represents the percentage of occupancy a hotel has depending on the distance of the hotel from a beach. c) What sort of relationship exists between average hotel occupancy rate and the hotel’s distance from the beach?Does this relationship make sense to you? Why or why not? Both distance and occupancy have a direct relationship. This is true because closer the hotel is to the beach, the higher the chance that the hotel’s occupancy will be greater. If a person is going to stay at a hotel, chances are they are on vacation. People on vacation love to spend time on a beach for relaxation purposes, so it would only make sense that a hotel that is closer to the beach will have a higher occupancy rate. d) Interpret the R-Square value in your computer output R-Squared = 0. 848195 = 84. 8195 ) Predict the expected occupancy rate for a hotel that is (i) one mile from the beach in that area, (ii) one and half miles from the beach. i. OCCUPNC = 99. 61444 – 26. 703 (1) = 99. 61444 – 26. 703 = 72. 911 ii. OCCUPNC = 99. 61444 – 26. 703 (1. 5) = 99. 61444 – 40. 055 = 59. 559 f) In your mind, what other variables contribute positively or negatively to hotel occupancy besides distance from the beach? Other variables that contribute positively or negatively to hotel occupancy besides distance fr om the beach include the distance of restaurants, shopping centers, and airport from the hotel.The closer theses variables are to the hotel the chances the occupancy rate will be higher. In addition, other variables may include what type of amenities that are offered by the hotel, customer service, and rating of the hotel. g) At a level of significance, ? = 0. 01 or 1 percent test the following pair of hypotheses: H[pic]: b[pic]= 0 H[pic]: b[pic]? 0 On the model: Occpnc=b[pic]+b[pic](Distncy) What is your conclusion and why that particular conclusion? COMPUTER OUTPUT – PART 1 INTERNATIONAL HOTEL REGRESSION FUNCTION & ANOVA FOR OCCPNCY OCCPNCY = 99. 61444 – 26. 703 DISTANCER-Squared = 0. 848195 Adjusted R-Squared = 0. 835545 Standard error of estimate = 3. 339362 Number of cases used = 14 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 747. 68 1 747. 68390 67. 04880 0. 000002 Residual 133. 82 12 11. 15134 Total 881. 50 13 COMPUTER OUTPUT  œ PART 1 INTERNATIONAL HOTEL REGRESSION COEFFICIENTS FOR OCCPNCY Two-Sided p-value Variable Coefficient Std Error t Value Sig Prob Constant 99. 61444 1. 4107 51. 31933 0. 000000 DISTANCE -26. 70300 3. 26110 -8. 18833 0. 000002 * Standard error of estimate = 3. 339362 Durbin-Watson statistic = 1. 324282 MULTIPLE REGRESSION 2) You want to find out factors that explain an individual’s weekly savings. You are given a set of data below: Sampled WeeklyHouseFoodEntertain/Weekly IndividualIncomeRentExpenseExpenseSavings Case 1$25085952520 Case 2$1907590100 Case 3$4201401204050 Case 4$340120130040 Case 5$2801101003015 Case 6$310801252525 Case 7$5201501405580 Case 8$440175155450 Case 9$36090852095 Case 10$3851051353530Case 11$2058010505 Case 12$26565951515 Case 13$19550801020 Case 14$25090100250 Case 15$4801401604545 A multiple regression was ran with WEEKLY SAVINGS as the DEPENDENT VARIABLE and the rest as the INDEPENDENT VARIABLES. SAVINGS = b[pic][pic]+ b[pic]INCOME + b[pic]RENT + b [pic]FOOD + b[pic]ENTERT a) What is the estimated multiple regression equation? SAVINGS = 23. 14156 + 0. 591446 INCOME – 0. 341793 RENT – 1. 119734 FOOD – 0. 907868 ENTERT b) What relationship exists between (i) SAVINGS and INCOME? , SAVINGS and RENT? , SAVINGS and FOOD expense, SAVINGS and ENTERTAINMENT expense?There are no direct relationship between saving and income, savings and rent, savings and food expense, and savings and entertainment expense. c) Which of the independent (explaining) variables are (is) significant in the multiple regression and which ones are (is) not significant (use ? = 0. 05 level of significance). Are the results in line with Maslow hierarchy of needs? Explain. COMPUTER OUTPUT PART I WEEKLY SAVINGS REGRESSION FUNCTION & ANOVA FOR SAVINGS SAVINGS = 23. 14156 + 0. 591446 INCOME – 0. 341793 RENT – 1. 119734 FOOD – 0. 907868 ENTERT R-Squared = 0. 917562 Adjusted R-Squared = 0. 70454 Standard error of estimate = 10. 9635 Number of cases used = 12 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 9364. 86 4 2341. 21 19. 47795 0. 000677 Residual 841. 39 7 120. 198 Total 10206. 250 11 COMPUTER OUTPUT PART II WEEKLY SAVINGS REGRESSION COEFFICIENTS FOR SAVINGS Two-Sidedp-value Variable Coefficient Std Error t Value Sig Prob Constant 23. 14156 18. 34071 1. 26176 0. 247451 INCOME 0. 59145 0. 07388 8. 00526 0. 000091 RENT -0. 4179 0. 19849 -1. 72199 0. 128743 * FOOD -1. 11973 0. 24633 -4. 54565 0. 002650 ENTERT -0. 90787 0. 32460 -2. 79689 0. 026643 * indicates that the variable is marked for leaving Standard error of estimate = 10. 9635 Durbin-Watson statistic = 1. 683103 3) REGRESSION ANALYSIS A business person is trying to estimate the relationship between the price of good X and the sales of good Y of certain groups of staples. Tests in similar cities throughout the country have yielded the data below: PRICE (X)SALES (Y) $2010,300 $259,100 $308,200 $356,500 $405,100 $502,300A simple linear regression of a model SALES(Y) = b[pic] + b[pic]PRICE(X) Was run and the computer output is shown below: PRICE OF X / SALES OF Y REGRESSION FUNCTION & ANOVA FOR SALES(Y) SALES(Y) = 15907. 14 – 269. 7143 PRICE(X) R-Squared = 0. 994999 Adjusted R-Squared = 0. 993749 Standard error of estimate = 230. 9143 Number of cases used = 6 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 4. 24350E+07 1 4. 24350E+07 795. 83480 0. 000009 Residual 213285. 70000 4 53321. 43000 Total 4. 26483E+07 5PRICE OF X / SALES OF Y REGRESSION COEFFICIENTS FOR SALES(Y) Two-Sidedp-value Variable Coefficient Std Error t Value Sig Prob Constant 15907. 14000 332. 34250 47. 86370 0. 000001 PRICE(X) -269. 71430 9. 56076 -28. 21054 0. 000009 * Standard error of estimate = 230. 9143 Durbin-Watson statistic = 1. 687953 QUESTIONS a) What is the estimated equation of the model: SALES(Y) = b[pic] + b[pic]PRICE(X)? SALES(Y) = 15907. 14 – 269. 7143 PRICE(X) b) What sort of relationship exists between SALES OF Y and the PRICE OF X? Does this relationship make sense? Why or why not?There is a direct relationship between Sales of Y and the Price of X. The lower the price the higher are the sales. This makes sense because if the price is lower, a person will purchase more items. c) What can you say about GOOD Y and GOOD X (a good can be an item, a commodity, etc. ). Name a pair of good X and good y that can display this kind of relationship. Suppose the price of candy is $0. 50/lb, the sales of the candy versus the same type of candy that is $0. 80/lb would yield more sales because of the price. The price of the candy directly affects sales in this instance because a person would buy more candy at $0. 0/lb versus $0. 80/lb. 4) REGRESSION ANALYSIS A business person is trying to estimate the relationship between the price of good X and the sales of good Z of certain groups of staples. Tests in similar cities throughout the country have yielded the data belo w: PRICE (X)SALES (Z) $153300 $203900 $254750 $305500 $406550 $507250 A simple linear regression of a model SALES (Z) = b[pic] + b[pic]PRICE(X) Was run and the computer output is shown below: PRICE OF X / SALES OF Z REGRESSION FUNCTION & ANOVA FOR SALES(Y) SALES(Z) = 1740. 686 + 115. 5882 PRICE(X) R-Squared = 0. 977573 Adjusted R-Squared = 0. 71966 Standard error of estimate = 255. 2152 Number of cases used = 6 Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 1. 13565E+07 1 1. 13565E+07 174. 35450 0. 000190 Residual 260539. 20000 4 65134. 80000 Total 1. 16171E+07 5 PRICE OF X / SALES OF Z REGRESSION COEFFICIENTS FOR SALES(Z) p-value Variable Coefficient Std Error t Value Sig Prob Constant 1740. 68600 282. 52800 6. 16111 0. 003522 PRICE(X) 115. 58820 8. 75381 13. 20434 0. 000190 *Standard error of estimate = 255. 2152 Durbin-Watson statistic = 1. 240299 QUESTIONS a) What is the estimated equation of the model: SALES(Z) = b[pic] + b[pic]PRICE(X)? SALES (Z) = 17 40. 686 + 115. 5882 PRICE(X) b) What sort of relationship exists between SALES OF Z and the PRICE OF X? Does this relationship make sense? Why or why not? There is a direct relationship between Sales of Y and the Price of X. The higher the price the higher are the sales. This makes sense as it relates to supply and demand. The higher the demand and for the product and unavailability of the product, the price will go up even though sales may he same due to the price increase the sales amount will be higher. c) What can you say about GOOD Z and GOOD X (a good can be an item, a commodity, etc. ). Give an example of good X and good Z that can display this kind of relationship A prime example that displays this kind of relationship is gas. The price of gas has been going up for sometime now. The demand for it is high, but the supply of is low. Therefore, even though the amount of sales may stay constant, the dollar amount will be higher because the price is higher. Chi-Squared Test #1M&M , makers of Chocolate Candies, conducted a national poll in which more than ten million people indicated their preference for a new color. The tally of this poll resulted in the replacement of tan-colored M&Ms with a new blue color. In the brochure â€Å"Colors,† made available by M&MS Consumer Affairs, the distribution of colors for the plain candies is as follows: BROWNYELLOWREDORANGEGREENBLUE 30%20%20%10%10%10% In a follow-up study two years later, samples of 1-pound bags were used to determine whether the reported percentages were still valid. The following results were obtained (observed) for one sample of 506 plain candiesBROWNYELLOWREDORANGEGREENBLUE 17713579413638 Use a level of significance ( = 0. 05 to determine whether these data support the percentages reported by the company Hint: To obtain the Expected Number of multiply the sample value (506) by each color’s probability, i. e. , E = BROWNYELLOWREDORANGEGREENBLUE 30% (506)20%(506)20%(506)10%(506)10%(506)1 0%(506) Then compute the Chi-Squared. H[pic]: f[pic], f[pic], f[pic], f[pic], f[pic], f[pic] hold previous year’s patterns or percentages H[pic]: At least one frequency differs from the previous year’s pattern or percentages E = 506/6 = 84. 33 [pic]=(177 –84. 33)[pic]/84. 33+(135 – 84. 33)[pic]/84. 33 + (79 – 84. 33)[pic]/84. 33+(41 – 84. 33)[pic]/84. 33)+(36 – 84. 33)[pic]/84. 33+(38 – 84. 33)[pic]/84. 33) ([pic]=101. 937 + 30. 49217 + 0. 333215 + 22. 24069 + 27. 67367 + 25. 4293 ([pic]=208. 106. This is the computed ([pic]-value. ( = 0. 05 d. f. = 6 – 1 = 5. Go to ([pic]-tables at ( = 0. 05, and d. f. = 5, you will get CRITICAL ([pic]-value = 11. 070. Since Computed ([pic]-value is greater than Critical ([pic]-value REJECT NULL H[pic]:P[pic] = P[pic] = P[pic] = P[pic] = P[pic] ALTERNATIVE H[pic]: At least one P is different is correct