I need help with the attached assignment.
Homework 5 Scope and Methods Linear Regression 1) Input the following data into SPSS: X (Average job satisfaction) Y (Job Turnover Rate) 50.0 2.0 38.0 4.25 16.0 58.5 28.5 222.5 25.0 46.75 44.0 194.25 49.0 13.5 38.5 10.0 37.5 8.75 22.5 256.0 44.5 66.75 48.5 12.0 49.5 13.25 47.0 1.25 41.0 206.0 0.0 358.75 49.0 38.25 22.0 41.75 40.5 8.0 33.5 108.0 0.0 13.5 44.0 2.0 50.0 30.75 9.0 0.5 49.5 14.25 a) Run a simple linear regression of the above data. Report the unstandardized beta coefficient and the standard error (note the standard error is found in the output next to each beta coefficient). Interpret your results. Is there a relationship between job satisfaction and job turnover? b) Finally, plot the results on a graph (note: it can be any type of graph). 2) Have SPSS open the “Non-profit.xls” Excel file, then answer the questions below: Hopecore, a non-profit agency that collects donations by selling autographed memorabilia through the telephone, has hired you to do a study on what factors cause people to donate/buy more products over the phone. You proposed and gathered data for the following model: Y (donation) = Bo(constant) + B1 (donorstatus) + B2 (onhold) + B3(region) + B4(industry) + e The coding for the dependent variable (donation) is the total amount spent (in actual dollars) by the donor during the phone call. a) Estimate the parameters from your model above. From the Excel or Stata output, fill in the corresponding values in the below table based on the above model: Bo (constant) SE B1 SE B2 SE B3 SE B4 SE R-Sq Notes: You can just report the unstandardized beta coefficients. SE is simply the standard error of each beta coefficient. b) Now, in the space below, interpret the unstandardized beta coefficients of EACH of the above variables ONLY if they are significant. c) Now, write a paragraph or two describing your findings to the executive board at Hopecore (note: they are unfamiliar with regression modeling, so you have to explain it to them in laymen’s terms). 3) Input the following data: Couple Divorce Index Parents Income Children 1 78 2 20.4 0 2 34 0 44.8 1 3 45 0 39.7 0 4 78 2 12.1 1 5 60 2 18.7 1 6 13 0 77.3 1 7 18 0 70.1 0 9 12 0 102.1 0 10 78 2 55 0 11 64 0 57 1 12 45 1 60 1 13 50 1 67 0 14 67 1 45 0 15 80 2 21 1 You want to discover what factors increase a couple’s risk for getting a divorce. The coding for the following is: Couple: The ID variable for the couple Divorce Index: How at risk they are for getting a divorce (0 is most likely to stay together and 100 is most likely to get a divorce) based on the interpretation of a divorce counselor. Parents : The divorce status of the couple’s parents where: 0 = Both couple’s parents are together 1 = One of the members of the couple has divorced parents 2 = Both couples’ parents are divorced. Income: The combined family income in thousands. Children: Whether or not the couple has children 0 = No 1 = Yes a) In SPSS, run a simple linear regression on the variables: Y = Divorce index and X = Income. Print out or report your results here. Report and interpret the r-squared (the coefficient of determination), the p-value (or the t-ratio), the beta coefficient and standard the error. Interpret each and describe the relationship. b) Now, run a multiple regression with all of the other variables (except the ID variable). Report and interpret each beta value if they are significant. Interpret and describe your results. What can you conclude from your analysis? c) Test the assumptions of the regression model. What did you find? What do you recommend? Sheet1 IDDonationOnholdRegionIndustryDonorstatus 6$04430 123$04111 465$04120 478$03111 570$04220CODEBOOK 669$04311 674$04231ID = Specific customer 738$04410Donation = Exact dollar amount of donation 869$04311Onhold: Time on hold 1 = Less than one minute 2 = 1-2 Minutes 3 = 2-4 Minutes 4 = More than 4 minutes 1108$04331Region: 1 = North 2 = South 3 = East 4= West 1405$03431Industry: 1 = Government 2 = Commercial 3 = Acdemic 1536$02210Donor Status: 1 = Regular 2 = Non-regular 893$134321 1484$344230 379$404321 1165$464220 207$644221 579$1284420 784$1392211 1406$2182410 1538$2744111 987$2824110 217$3054211 583$3074230 1250$3122421 92$3374320 1206$3514421 331$3824130 185$3844311 771$4114130 703$4243331 57$4304420 80$4304231 993$4544410 1298$4604411 1152$4612430 28$4653431 284$4823111 1011$4883231 1510$5004430 614$5004330 824$5012431 581$5024331 1444$5093111 1566$5142111 752$5154421 1429$5324311 131$5434131 1348$5454211 569$5554130 448$5674431 1255$5693420 686$5724131 919$5733311 730$5923111 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