Sub ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Education MMSE Age 16 28 12 28 13 29 8 29 12 28 12 29 16 30 13 28 12 26 14 26 13 29 17 30 12 29 16 28 14 26 20 28 15 28 16 30 16 29 18 29 16 28 14 30 16 28 18 29 18 30 14 30 12 30 18 28 15 29 12 29 12 29 19 28 14 30 16 30 19 28 13 28 18 30 16 28 18 27 16 30 20 30 16 30 20 29 18 30 16 30 12 29 65.8 66.9 76.9 79.9 84.1 72.6 75.6 79.6 78.0 66.3 65.1 67.2 69.0 78.9 69.4 73.6 78.4 69.6 65.8 69.5 67.0 69.9 68.8 71.5 73.8 71.1 74.5 66.6 70.7 76.8 75.4 66.7 65.5 72.8 76.5 72.9 69.7 68.2 65.7 73.4 65.8 66.0 71.4 75.8 68.1 67.5 Executive 84.44 79.88 80.11 100.42 97.59 89.75 108.95 100.00 100.05 97.63 95.63 98.52 103.15 89.73 91.22 93.71 96.87 101.35 97.42 110.68 107.64 103.58 96.67 108.34 90.26 91.85 99.20 112.89 97.22 111.00 91.38 105.13 102.57 102.72 107.08 96.14 95.35 104.15 104.76 106.80 100.89 110.68 111.41 114.54 117.27 123.48 47 48 49 50 16 18 16 14 30 30 30 29 67.8 73.0 71.9 68.3 116.20 108.14 117.73 93.19 Netsales Storesize Adcost Area 10.00 0.50 3.00 15.00 0.60 2.50 20.00 1.20 3.30 50.00 1.10 3.10 65.00 1.20 4.70 68.00 0.60 4.90 98.00 1.60 4.60 99.00 0.80 2.80 156.00 2.20 6.90 161.00 2.60 7.20 195.00 2.50 7.70 231.00 3.00 8.20 299.00 3.10 8.10 341.00 3.50 9.80 347.00 3.60 9.60 397.00 3.80 10.40 398.00 4.30 5.50 400.00 8.60 7.00 428.00 4.20 10.50 437.00 4.40 10.60 464.00 4.70 11.30 487.00 4.80 11.80 497.00 5.30 11.50 507.00 5.10 12.00 519.00 5.50 12.00 528.00 5.60 12.30 570.00 5.40 17.40 Competitor 4.30 2.50 2.10 1.60 3.30 4.70 2.70 6.50 4.10 6.30 8.40 8.20 10.10 11.50 11.30 13.90 16.00 12.00 14.00 14.10 15.00 12.70 16.30 15.70 16.10 16.00 12.30 1 8 5 13 8 5 10 12 0 11 4 0 4 14 14 11 10 15 7 1 7 3 12 15 1 12 6 Q1 Data Set Variable Name Education Description Number of years of schooling Values Number of years MMSE Mini Mental Status Exam Test score Age Age of participant Number of years Executive Executive function Test score Variable Netsales Description annual net sale amount (in thousands) Value dollar amount in thousands Storesize square footage of the store # of square feet Adcost advertising cost dollar amount in thousands Area size of the area served by the store # of square miles Competitor number of competitors in the area served by #the of store stores Q2 Data Set Fall A 2020 M6 Assignment (35 points) INSTRUCTION: Use this Word document to fill in the answers to the questions. The answers must be supported either by typing out the calculation process or by pasting SPSS output or data as instructed. When prompted to paste something from the SPSS data or output file, you may use the copy/paste function or take a screen shot of the relevant part to paste in as a picture. Q1. Perform and Interpret a Bivariate Linear Regression (9 points total, if SPSS output file is missing for this analysis, 50% of the total number of earned points will be deducted) This data set contains cognitive function scores and demographics from a group of older adults. The focus of this question set is the executive function (Executive). Create an SPSS data file by importing the Q1Q2 data. Configure the “measure” for each variable. A . Perform exploratory bivariate correlations in SPSS. 1. Perform bivariate (Pearson’s) correlations among all the variables in the data set. Paste the correlation matrix (table) here. (1 point for the table) 2. Report the correlation result in APA format (including r and p) for each of the following pairs of variables: (1.5 points: .5 for each correlation, both r and p must be correct to earn .5 point) Education and Executive: MMSE and Executive: Age and Executive: B. Perform a bivariate (simple) linear regression. The regression model should contain the following: Outcome variable - Executive Predictor - Variable with the strongest correlation with Executive (regardless of direction) 1. Create a scatter plot between the predictor variable (X axis) and outcome variable (Y axis). Make sure the scatter plot has labels for the X and Y axes. Paste the scatter plot here. (1 point: Deduct .5 for each error up to a total of 1. No point is earned is the graph is not pasted here. ) 2. Perform the bivariate regression analysis in SPSS. Report the omnibus test result in APA style on the regression model, including F, p, and adjusted R2. Be sure to paste the relevant output tables here to support your answer. (1.5 points: .5 for each statistic, both value and APA format must be correct to earn the credit for each statistic. No credit is earned if no table is pasted.) Fall A 2020 3. Discuss the regression result. Is the null hypothesis rejected? What does the result mean? (1 point: .5 for each answer) 4. Report the coefficient test on the predictor variable in APA format, including t and p. Be sure to paste the relevant output tables if they have not been pasted above. (1 point: .5 for each statistic, both value and APA format must be correct to earn the credit for each statistic) 5. Explain the coefficient test result. Is the null hypothesis rejected? What does that mean? (1 point: .5 for each answer) 6. How much of the variance in the outcome variable can be predicted by the predictor variable? (1 point) Q2. Perform and Interpret a Multiple Linear Regression (8 points total, if SPSS output file is missing for this analysis, 50% of the total number of earned points will be deducted) In Q1 above, we examined the relationship between executive function and one predictor variable. Here we are interested in how multiple predictors may be combined to predict executive function even better. Specifically, we would like to build a regression model for executive function with two predictor variables that have the highest and second highest correlations with Executive. A. Perform a multiple linear regression according to the research question above and answer the following questions. Use “ENTER” (the default in SPSS) as the method of adding the predictor variables to the regression model. 1. Report the omnibus F test result for the regression model, in APA format, including F, p, and adjusted R2. Be sure to paste the relevant table(s) here to support your answers. (1.5 points total. .5 for each statistic. Both value and format must be correct to earn the point. No credit is earned if no relevant table is pasted.) 2. Interpret the test result by answer the following questions: (2 points total) a. What was the null hypothesis (in words) tested by this multiple regression analysis? (.5 point) b. What was the hypothesis test result? (Do you reject or fail to reject the null hypothesis?) (.5 point) c. What is the effect size of this regression model? (.5 point) d. What does the effect size mean? (.5 point) 3. Report the statistics on each predictor variable in APA format, including , t, and p. Fall A 2020 (1.5 points total. .5 for each predictor, both value and format must be correct to earn points.) 5. Discuss the relative contributions of the predictors in the model. (2 points total) a. Which predictor is significant? (.5 point) b. Which is the strongest predictor? (.5 point) c. Which is the weakest predictor? (.5 point) d. How do you know? (.5 point) B. Compare this current multiple regression model with the bivariate regression model in Q1. Does the model with three predictors predict the outcome variable better than the model with only one predictor? How do you know? (1 point. .5 for each answer. Deduct .5 if the results are not pasted but the answers are correct.) Q3. Perform and Interpret a Bivariate Linear Regression (9.5 points total, if SPSS output file is missing for this analysis, 50% of the total number of earned points will be deducted) This analysis will be performed on a data set collected by a national company on their 27 chain stores. The marketing department would like to know how various factors contribute to the net sales (Netsales) amount for a chain store. Create an SPSS data file by importing the Q3Q4 data. Configure the “measure” for each variable. A . Perform exploratory bivariate correlations in SPSS. 1. Perform bivariate (Pearson’s) correlations among all the variables in the data set. Paste the correlation matrix (table) here. (1 point for the table) 2. Report the correlation result in APA format (including r and p) for each of the following pairs of variables: (2 points: .5 for each correlation, both r and p must be correct to earn .5 point) Storesize and Netsales: Adcost and Netsales: Area and Netsales: Competitor and Netsales: B. Perform a bivariate (simple) linear regression. The regression model should contain the following: Outcome variable - Netsales Predictor - Variable with the strongest correlation with Netsales (regardless of direction) Fall A 2020 1. Create a scatter plot between the predictor variable (X axis) and outcome variable (Y axis). Make sure the scatter plot has labels for the X and Y axes. Paste the scatter plot here. (1 point: Deduct .5 for each error up to a total of 1. No point is earned is the graph is not pasted here. ) 2. Perform the bivariate regression analysis in SPSS. Report the omnibus test result in APA style on the regression model, including F, p, and adjusted R2. Be sure to paste the relevant output tables here to support your answer. (1.5 points: .5 for each statistic, both value and APA format must be correct to earn the credit for each statistic. No credit is earned if no table is pasted.) 3. Discuss the regression result. Is the null hypothesis rejected? What does the result mean? (1 point: .5 for each answer) 4. Report the coefficient test on the predictor variable in APA format, including t and p. Be sure to paste the relevant output tables if they have not been pasted above. (1 point: .5 for each statistic, both value and APA format must be correct to earn the credit for each statistic) 5. Explain the coefficient test result. Is the null hypothesis rejected? What does that mean? (1 point: .5 for each answer) 6. How much of the variance in the outcome variable can be predicted by the predictor variable? (1 point) Q4. Perform and Interpret a Multiple Linear Regression (8.5 points total, if SPSS output file is missing for this analysis, 50% of the total number of earned points will be deducted) In Q3 above, we examined the relationship between Netsales and one predictor variable. Here we are interested in how multiple predictors may be combined to predict Netsales even better. Specifically, we would like to build a regression model for Netsales with four predictor variables: Storesize, Adcost, Area, Competitor. A. Perform a multiple linear regression according to the research question above and answer the following questions. Use “ENTER” (the default in SPSS) as the method of adding the predictor variables to the regression model. 1. Report the omnibus F test result for the regression model, in APA format, including F, p, and adjusted R2. Be sure to paste the relevant table(s) here to support your answers. Fall A 2020 (1.5 points total. .5 for each statistic. Both value and format must be correct to earn the point. No credit is earned if no relevant table is pasted.) 2. Interpret the test result by answer the following questions: (2 points total) a. What was the null hypothesis (in words) tested by this multiple regression analysis? (.5 point) b. What was the hypothesis test result? (Do you reject or fail to reject the null hypothesis?) (.5 point) c. What is the effect size of this regression model? (.5 point) d. What does the effect size mean? (.5 point) 3. Report the statistics on each predictor variable in APA format, including , t, and p. (2 points total. .5 for each predictor, both value and format must be correct to earn points.) 5. Discuss the relative contributions of the predictors in the model. (2 points total) a. Which predictor is significant? (.5 point) b. Which is the strongest predictor? (.5 point) c. Which is the weakest predictor? (.5 point) d. How do you know? (.5 point) B. Compare this current multiple regression model with the bivariate regression model in Q3. Does the model with four predictors predict the outcome variable better than the model with only one predictor? How do you know? (1 point. .5 for each answer. Deduct .5 if the results are not pasted but the answers are correct.) 
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