I need help with these three assignments. I've attached the documents belong. Please put each on separate file. Please and thank you !!

DSC 410 - SIMPLE LINEAR REGRESSION Assignment #1 - Fall 2016 Due Monday September 19th, 20 16 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) 0.1 Occupancy (%) 92 0.1 95 0.2 96 0.3 90 0.4 89 Continue Distance (miles) 0.7 Occupancy (%) 80 0.8 78 0.8 76 0.9 72 0.9 75 0.4 96 0.5 90 0.6 83 0.7 85 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 + 0 b (Distncy) 1 The results (computer output is found on the next page. 1 COMPUTER OUTPUT - PART 1 INTERNATIONAL HOTEL REGRESSION FUNCTION ANOVA FOR OCCPNCY OCCPNCY = 99.61444 - 26.703 DISTANCE R-Squared Adjusted R-Squared Standard error of estimate Number of cases used = 0.848195 = 0.835545 = 3.339362 = 14 Analysis of Variance Source SS df MS Regression 747.68 1 747.68390 Residual 133.82 12 11.15134 Total 881.50 13 p-value F Value Sig Prob 67.04880 0.000002 COMPUTER OUTPUT - PART 1 INTERNATIONAL HOTEL REGRESSION COEFFICIENTS FOR OCCPNCY Variable Constant DISTANCE Two-Sided p-value Coefficient Std Error t Value Sig Prob 99.61444 1.94107 51.31933 0.000000 -26.70300 3.26110 -8.18833 0.000002 * Standard error of estimate = 3.339362 Durbin-Watson statistic = 1.324282 Use the above computer output to answer the following questions: 2 (a) The simple linear regression model was: Occpnc = b + 0 b (Distance) 1 What is the estimated simple linear regression? ANSWER (b) What are the estimated values of b and b 0 1 ANSWER b =? 0 b =? 1 (c) In the context of this problem, interpret the meaning behind the values you get for both coefficients b and b . 0 1 ANSWER (d) 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? ANSWER 3 (e) Interpret the R-Square value in your computer output ANSWER (f) 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. ANSWER (g) In your mind, what other variables contribute positively or negatively to hotel occupancy besides distance from the beach? 4 (h) At a level of significance, = 0.01 or 1 percent test the following pair of hypotheses: H :b =0 0 1 H :b 0 A 1 On the model: Occpnc = b + 0 b (Distncy) 1 What is your conclusion and why that particular conclusion? 5 6 DSC 410 - MULTIPLE REGRESSION ANALYSIS Assignment #2 - Fall 2016 Due Monday September 19th, 20 16 NAME_____________________________________ MULTIPLE REGRESSION The date set below was collected from a random sample of 15 households on the following variables: (1) Weekly Income, (2) House Rent, (3) Food Expense, (4) Entertainment Expense, and (5) Weekly Savings. Sampled Individual Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8 Case 9 Case 10 Case 11 Case 12 Case 13 Case 14 Case 15 Weekly Income $250 $190 $420 $340 $280 $310 $520 $440 $360 $385 $205 $265 $195 $250 $480 House Rent 85 75 140 120 110 80 150 175 90 105 80 65 50 90 140 Food Expense 95 90 120 130 100 125 140 155 85 135 105 95 80 100 160 Entertain/ Expense 25 10 40 0 30 25 55 45 20 35 0 15 10 25 45 Weekly Savings 20 0 50 40 15 25 80 0 95 30 5 15 20 0 45 A multiple regression was run with WEEKLY SAVINGS as the DEPENDENT VARIABLE and the rest as the INDEPENDENT VARIABLES. SAVINGS = b + b INCOME + b RENT + b FOOD + b ENTERT 0 1 3 2 The resulting computer output is on the next page. 1 4 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 Adjusted R-Squared Standard error of estimate Number of cases used = 0.917562 = 0.870454 = 10.9635 = 12 Analysis of Variance Source SS Regression 9364.86 Residual 841.39 Total 10206.250 df 4 7 11 MS 2341.21 120.198 p-value F Value Sig Prob 19.47795 0.000677 COMPUTER OUTPUT PART II WEEKLY SAVINGS REGRESSION COEFFICIENTS FOR SAVINGS Variable Constant INCOME RENT FOOD ENTERT Coefficient 23.14156 0.59145 -0.34179 -1.11973 -0.90787 Std Error 18.34071 0.07388 0.19849 0.24633 0.32460 Two-Sided t Value 1.26176 8.00526 -1.72199 -4.54565 -2.79689 * indicates that the variable is marked for leaving Standard error of estimate = 10.9635 Durbin-Watson statistic = 1.683103 2 p-value Sig Prob 0.247451 0.000091 0.128743 * 0.002650 0.026643 Use the above computer output to respond to the following questions: (a) The multiple regression model was: SAVINGS = b + b INCOME + b RENT + b FOOD + b ENTERT 0 1 3 2 4 What is the estimated multiple regression? ANSWER (b) What are the estimated values of b , b , b , b , and b ? 0 1 2 3 4 ANSWERS b = ? 0 b =? 1 b =? 2 b =? 3 b =? 4 (c) What relationship exists between (i) SAVINGS and INCOME?, SAVINGS and RENT?, SAVINGS and FOOD expense, SAVINGS and ENTERTAINMENT expense? ANSWERS 3 (d) Which of the four independent (explaining) variables are (is) significant in the multiple regression and which ones are (is) not significant and why? (Use = 0.05 level of significance). Are the results in line with Maslow hierarchy of needs? Explain ANSWERS 4 SIMPLE LINEAR REGRESSION DSC 430 - STX ASSIGNMENT #1 INTERPRETATION OF COMPUTER OUTPUT Problem # 1. Shrinkage is theft by employees. The data set below shows 12 random weeks data values for shrinkage and the number on sales clerks on duty. Week 1 Shrinkage 20 ($00) #Clerks 20 2 23 3 18 4 17 5 17 6 12 21 22 23 24 25 7 17 25 8 26 9 21 10 11 11 10 12 4 20 22 24 26 28 The simple linear regression model takes form of: Y =b +b X +e i 0 1 i i Assume Shrink = b + b (Clerks) + e 0 1 i That is, shrinkage is dependent on the number of clerks present (on duty). This problem was run using a computer software package and the results (computer output0 are located on the next page. Use the computer output to respond to some questions. 1 RESULTS (COMPUTER OUTPUT #1) Problem #1 REGRESSION FUNCTION ANOVA FOR SHRINK SHRINK = 69.65 - 2.285 CLERKS R-Squared Adjusted R-Squared Standard error of estimate Number of cases used = 0.835396 = 0.818936 = 2.618874 = 12 Analysis of Variance Source SS df Regression Residual 348.08170 68.58500 1 10 Total 416.66670 11 MS 348.08170 6.85850 F Value 50.75187 Sig Prob p-value 0.000032 COMPUTER OUTPUT #2 Problem #1 REGRESSION COEFFICIENTS FOR SHRINK Variable Constant CLERKS Coefficient 69.65000 -2.28500 Std Error 7.52214 0.32075 Two-Sided t Value 9.25933 -7.12403 Standard error of estimate = 2.618874 Durbin-Watson statistic = 1.820899 2 p-value Sig Prob 0.000003 0.000032 * (a) The simple linear regression model was: Shrink = b + b (Clerks) + e 0 i 1 What is the estimated simple linear regression? ANSWER (b) What are the estimated values of b and b ? 0 1 b =? ANSWERS 0 b =? 1 (c) In the context of this problem, interpret the meaning behind the estimated values of b and b 0 1 ANSWERS (d) What sort of relationship exists between the amount of shrinkage and the number of clerks on duty? Does this relationship make sense to you? Explain. ANSWER (e) Using either the F-test or the t-test, at a level of significance = 0. Test for the significance of the relationship. Note the model is: Shrink = b + b (Clerks) 0 1 3 H :b =0 0 1 H :b 0 A 1 What is your conclusion and why that conclusion? ANSWER (f)What amount of shrinkage do you expect to experience when the number of clerks present is (i) fifteen?, (ii) Zero, (iii) 30. ANSWER 4