Essay --

Kayla McCarthy Period 2 History Mrs.Dowd 1/6/14 Disagreements Between the North and South Secession between the North and the South was very different. The secession led to the Confederate States of the United States. The Confederate States Constitution closely resembled the Constitution of the United States and it states that it, â€Å"protected and recognized slavery.† The southerners feared that if they did not succeed that an end to their entire way of life was at hand because they felt that they needed to preserve slavery. Succeeding seemed to be the only way of saving slavery. Many other southern states began to succeed when they learned that slavery was going to be removed in the South. For example,Compromise of 1850, Compromise of 1820, the Dred Scott Decision, Raid at Harpers Ferry, â…â€" Compromise, Kansas Nebraska Act, and Missouri Compromise were some of the compromises and decisions made at the time to deal with disagreements many of these people had over slavery(Batten). Slavery was relied on very much in the south because the slaves were their way of making money. The sl...

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

Organizational Behavior in the Nfl - 2865 Words

Organizational Behavior in the National Football League Katie Johnston MT3250 Organizational Behavior Dr. Carl Proehl February 4, 2013 Abstract This paper will explore how people within the National Football League (NFL) interact with each other to reach their goals as a team, and an organization as a whole. Sports teams are defined as two or more individuals who possess a common identity, have common goals and objectives, share a common fate, exhibit structured patterns of interaction and modes of communication, hold common perceptions about group structure, are personally and instrumentally interdependent, reciprocate interpersonal attraction and consider themselves to be a group (Group Dynamics, 2004). There are many people†¦show more content†¦There are a series of periods that teams go through in making their decisions. The first is the preparation period, where the coaches and players anticipate the actions the opponents intend to perform and the probability of the actions. This early stage process is the one where video observation is used, studying the opponent’s strengths, weaknesses, and styles of play. The next decision regards how the team (A) should react, or respond, to the actions of the opposing team (B). Timing and quality must be a factor into team A’s next move. If the opposing team (B) changes their plan of action that was anticipated by team A, quick modifications must be made to ensure a successful retaliation. The success of decision making within a team depends on different mechanisms operating simultaneously and in mutual interaction (Decision Making in Sport, 2004). Let’s look more in depth at the visual aspect of decision making. Visual information is critical for any team. The visual (spatial) learning system is when a person (i.e. athlete) prefers learning through images, colors, pictures and maps to gather information. 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Why Year Round School Is More Harm Than Good - 978 Words

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Proven Record Of Food Service Industry With 30 Years Of...

Proven record in food service industry with 30 years of experience and industry intelligence, capabilities include: leading by example, with a strong work ethic, while demonstrating integrity, creative strategies with consistent follow through and effective project management skills. †¢ Self-starter with strong organizational skills. †¢ Demonstrate the ability in establishing both internal and external long-term strategic partnerships. †¢ Pragmatic decision maker and proven problem solver. †¢ Developed business processes and analytics related to Request for Proposals. †¢ Effective project manager. †¢ Experienced negotiator, successfully managing and growing both the Frozen / Bakery category by 10.18% and Dairy category by 13.31% when I†¦show more content†¦Ã¢â‚¬ ¢ Supervise the Exclusive Brand Conversion project in the role of primary contact for our members, suppliers and internal customers in the implementation of the project. †¢ Act as liaison to solve all concerns, negotiate project scope, timelines and execution with all interested parties including suppliers, members and outside graphics agencies involved in the Exclusive Brand Conversion project which consists of revitalizing our entire brand offering including all packaging, brand definition and establishing of guardrails for each category. †¢ Developed tracking report to record progress of each project on a weekly basis. †¢ Hired, trained, supervised and mentored Trafficking Specialist. †¢ Strategically develop, negotiate and administer programs in the Frozen categories which provide enhanced earnings to the IMA member companies. †¢ Manage assigned suppliers, which includes conducting supplier review meetings, on-going program maintenance and handling day-to-day situations as they arise through member companies. †¢ Act as RFP Liaison conducting and overseeing the RFP process for the Frozen category, from inception in coordination with the PAC through product introduction in coordination with the members. †¢ Collect revenues due IMA office on a timely basis and according to the terms of our agreements to include Service Fees, Sales Training Tools, and Marketing. †¢ Responsible for compliance with regards to

Online Shopping Behavior-Free-Samples for Students-Myassignment

Question: Discuss about the online shopping behavior. Answer: Online shopping is a much developed phenomena in the current world. The online shopping behavior of the consumers are directly related to e-stores, logistics support, product characteristics, websites technological characteristics, information characteristic and home page presentation. The consumer behavior towards traditional and current online shopping is different. The traditional shopping is more influenced by the social, cultural and personal factors compared to online shopping. However, a customer not always buys a product because of usefulness or needs, but also because of its value or how much it is worthy of buying. Online shopping has become a very common phenomenon in current days. It is to be noted that online shopping provides more satisfaction to the modern day consumers who seeks convenience and speed, both at the same time. Even it can also link up with the traditional sale and promotion methods. With the same, it is also to be mentioned that the importance of using o nline store is still depending on the type of product and services that are in demand among the consumer. However, internet has broadened the communication platform in the market by taking into consideration the way how people can get easy access to their products by simply sitting over there places and accessing their desktop, notebook or mobile phone. The enormous content accessible on the wide range of online shopping sites as well as the ability of the users to access that content; controls the overall experience of the users.

The Cycle of Never Ending Cause and Effect Essay Example For Students

The Cycle of Never Ending Cause and Effect Essay The Cycle of Never Ending Cause and EffectThere is no such thing as first or second, or as cause and effect. Humanity has constantly searched for the beginning of things asking questionssuch as Which came first, the chicken or the egg?. They search for answerswhich are simply entangled in a never ending cycle of events. Belief beforeevidence or evidence before belief, it doesnt matter. Both compound a cyclewhere before belief theres evidence and before evidence there is belief and soon. If the mind teaser about the chicken and the egg is traced back to itsbeginning, for example taking the generations and going back on time, there willcome a point where the beginning of things will be put under observation. Howdid things begin? Scientists believe that our world began with the Big Bang,yet for the Big Bang to originate there must have been the Sun and the Universeitself. Then what was before the Universe? An atom? And before the atom? Theword nothing is a common answer to these questions, supposedly ending theinfinite quest for knowledge. Yet before the nothing, there must have beensomething else, maybe more nothingness, who knows? The fact simply is thathumanity doesnt know what came first and have thrive to come up with answerswhich range from the scientific point of view to the religious. The religiousanswers, which are completely based on belief, used to be entirely accepted bypeople, but as science began to flourish, scientific answers, which use logicand reasoni ng, became the primary source for belief. Now a days it is importantto have evidence in order to believe. Yet when scientists discover new things,do they just find the evidence? Or they believe that something is there andbegin their quest to find it? Again, be it one way or the other, it doesntmatter. Lets take for example that the scientist believed that something wasthere, his/her belief must have been based on evidence. How else then could theyhave thought about it? Yet that evidence in return, before being discovered,was based on belief and so on. We will write a custom essay on The Cycle of Never Ending Cause and Effect specifically for you for only $16.38 $13.9/page Order now It is all a cycle indeed. One cannot say which came first. The beginningof things will always be an unknown if humanity keeps searching for it. Thereis no beginning. The cycle causes effects which in return cause causes whichcause effects. In a family where there is constant fighting, problems are theresult of other problems and so on. One would have to trace all the way back tosee what or who was guilty from the beginning. The same applies to the searchfor the beginning of times. In order to stop the fighting one would have tostop the cycle. Everyone in the family would have to forget passed events andstart all over, from the beginning. Yet because no such thing as completelyforgetting exists, someone would again do a misdeed that would spark thefighting chain. The world is a sphere which rotates without stopping. Once itstops, the cycle of never ending cause and effect keeps on going. As a newbegging takes place, the cycle would be rotating. It would be the same cycle,not a differe nt one. The destruction of our world would indeed cause thebeginning of another; if the present world had never been destroyed then the newone would never have been formed. A counter argument to the idea of a never ending cycle would easily bedisregarded. One can say that the cycle must have been put into motion by aforce as objects on earth are given a force to begin their motion. Yet thatcycle would not be the same one. The never-ending cycle of cause and effect, ofbelief or evidence, of first and second, goes beyond all parameters. It is thecycle itself which causes everything. It would be the cycle which would causethe force to put a smaller cycle into motion. The cycle is an entity in itself. .u36c6de52e465c5f02945e1ab0dbfb988 , .u36c6de52e465c5f02945e1ab0dbfb988 .postImageUrl , .u36c6de52e465c5f02945e1ab0dbfb988 .centered-text-area { min-height: 80px; position: relative; } .u36c6de52e465c5f02945e1ab0dbfb988 , .u36c6de52e465c5f02945e1ab0dbfb988:hover , .u36c6de52e465c5f02945e1ab0dbfb988:visited , .u36c6de52e465c5f02945e1ab0dbfb988:active { border:0!important; } .u36c6de52e465c5f02945e1ab0dbfb988 .clearfix:after { content: ""; display: table; clear: both; } .u36c6de52e465c5f02945e1ab0dbfb988 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .u36c6de52e465c5f02945e1ab0dbfb988:active , .u36c6de52e465c5f02945e1ab0dbfb988:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .u36c6de52e465c5f02945e1ab0dbfb988 .centered-text-area { width: 100%; position: relative ; } .u36c6de52e465c5f02945e1ab0dbfb988 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .u36c6de52e465c5f02945e1ab0dbfb988 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .u36c6de52e465c5f02945e1ab0dbfb988 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .u36c6de52e465c5f02945e1ab0dbfb988:hover .ctaButton { background-color: #34495E!important; } .u36c6de52e465c5f02945e1ab0dbfb988 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .u36c6de52e465c5f02945e1ab0dbfb988 .u36c6de52e465c5f02945e1ab0dbfb988-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .u36c6de52e465c5f02945e1ab0dbfb988:after { content: ""; display: block; clear: both; } READ: Hate Crimes Essay ThesisIt has always been and will always be. Therefore, belief or evidence, are the result of one another. One causedthe other which in return caused the other. Both are part of the cycle and willremain as part of it forever. Philosophy