Steps to Conduct an OLS Regression Analysis in SPSS Standard Multiple Regression • Click Analyze, then Regression, then Linear • Move your dependent variable to the “Dependent” box on the right • Move...

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OLS Regression


Steps to Conduct an OLS Regression Analysis in SPSS Standard Multiple Regression • Click Analyze, then Regression, then Linear • Move your dependent variable to the “Dependent” box on the right • Move your independent variables to the “Independent(s)” box on the right • Click Statistics  Click on Collinearity Diagnostics, optional: Descriptives, optional: Confidence Intervals and set a confidence level (e.g., 95), optional: Part and Partial Correlations  Click Continue • Click OK Sequential Multiple Regression (Block-Entry Regression) • Click Analyze, then Regression, then Linear • Move your dependent variable to the “Dependent” box on the right • Move your first set of independent variables to the “Independent(s)” box on the right • Click Next in the upper right beside “Independents(s)” • Move your second set of independent variables to the “Independent(s)” box on the right. Repeat the prior step and this step until you have entered each set of variables. • Click Statistics  Click on R squared change and Collinearity Diagnostics, optional: Descriptives, optional: Confidence Intervals and set a confidence level (e.g., 95), optional: Part and Partial Correlations  Click Continue • Click OK Statistical Multiple Regression (Stepwise Multiple Regression) • Click Analyze, then Regression, then Linear • Move your dependent variable to the “Dependent” box on the right • Move your independent variables to the “Independent(s)” box on the right • Change “Method:” (to the lower right of the “Independence(s)” box) to Stepwise • Click Statistics  Click on R squared change and Collinearity Diagnostics, optional: Descriptives, optional: Confidence Intervals and set a confidence level (e.g., 95), optional: Part and Partial Correlations  Click Continue • Click OK 1. For this problem, you will use data from the National Football League (football.sav). Conduct a standard multiple regression analysis testing the hypothesis that the number of games won in a season (games) is a function of season passing yards (ydpass), the percentage of rushing plays (pcrush), and the opponents’ season rushing yardage (opprush). Use an alpha of .05 to answer the following questions. a. What is the computed F statistic and what does this indicate? (2 points) b. What is the computed coefficient of determination? Interpret what this number indicates? (2 points) c. Is there a problem with multicollinearity in this analysis? Why or why not? (2 points) d. Which of the independent variables have significant unique relationships with games won? (2 points) e. How do you interpret the unstandardized regression coefficient for opponents’ rushing yardage and for percentage of rushing plays? (2 points) f. What does the t value and accompanying significance level for opponent’s rushing yardage indicate? (2 points) g. What is the final regression equation resulting from this analysis (use the unstandardized regression coefficients to write the regression equation)? (1 point) h. What substantive conclusions would you come to from this analysis? (2 points) 2. Using the same dataset from the National Football League (football.sav), conduct a block-entry regression analysis analyzing the number of games won in a season (games). Enter percentage of rushing plays (pcrush) in the first block, season passing yards (ydpass) in the second block, and opponent’s season rushing yards (opprush) in the third block. Note the increments of variance explained (R-square change) by each block of variables. Use an alpha of .05 to answer the following questions. a. Which variable explains the largest proportion of variance? (1 point) b. Are each of the increments in variance explained significantly different from 0? (1 point) c. What is the final regression equation (use unstandardized coefficients)? (1 point) Now, change the order of entry such that opponents' rushing yardage (opprush) is entered first, then percent rushing plays (pcrush), and finally passing yardage (ydpass). Again, note the increments of variance explained by each addition. d. Which variable now explains the largest proportion of variance? (1 point) e. Again, is each of the increments in variance explained significant? (1 point) f. What is the final regression equation (use unstandardized coefficients)? (1 point) g. Compare each of the resulting equations to each other and to the equation from #1. How do they differ? (2 points) h. What's the point being made??!!! (2 points) OLS Regression 1 Steps to Conduct an OLS Regression Analysis in SPSS Standard Multiple Regression • Click Analyze, then Regression, then Linear • Move your dependent variable to the “Dependent” box on the right • Move your independent variables to the “Independent(s)” box on the right • Click Statistics  Click on Collinearity Diagnostics, optional: Descriptives, optional: Confidence Intervals and set a confidence level (e.g., 95), optional: Part and Partial Correlations  Click Continue • Click OK Sequential Multiple Regression (Block-Entry Regression) • Click Analyze, then Regression, then Linear • Move your dependent variable to the “Dependent” box on the right • Move your first set of independent variables to the “Independent(s)” box on the right • Click Next in the upper right beside “Independents(s)” • Move your second set of independent variables to the “Independent(s)” box on the right. Repeat the prior step and this step until you have entered each set of variables. • Click Statistics  Click on R squared change and Collinearity Diagnostics, optional: Descriptives, optional: Confidence Intervals and set a confidence level (e.g., 95), optional: Part and Partial Correlations  Click Continue • Click OK Statistical Multiple Regression (Stepwise Multiple Regression) • Click Analyze, then Regression, then Linear • Move your dependent variable to the “Dependent” box on the right • Move your independent variables to the “Independent(s)” box on the right • Change “Method:” (to the lower right of the “Independence(s)” box) to Stepwise • Click Statistics  Click on R squared change and Collinearity Diagnostics, optional: Descriptives, optional: Confidence Intervals and set a confidence level (e.g., 95), optional: Part and Partial Correlations  Click Continue • Click OK
football-mqqjyqqy-omqhrk05.sav steps-to-conduct-regression-in-spss-ru0yipxp-rks12kog.pdf homework-jqx1exn4-qpukpbpd.docx football-mqqjyqqy-wxu5ogft.sav steps-to-conduct-regression-in-spss-ru0yipxp-352k5di3.pdf
Answered Same DayMar 26, 2021

Answer To: Steps to Conduct an OLS Regression Analysis in SPSS Standard Multiple Regression • Click Analyze,...

Pritam answered on Mar 31 2021
146 Votes
1. For this problem, you will use data from the National Football League (football.sav). Conduct a standard multiple regression analysis testing the hypothesis that the number of games won in a season (games) is a function of season passing yards (ydpass), the percentage of rushing plays (pcrush), and the opponents’ season rushing yardage (opprush). Use an alpha of .05 to answer the following questions.
a. What is the computed F statistic and what does this indicate? (2 points)
F-statistic of the model is found to be F (3,24) = 23.732, p < 0.0005
The F-statistic signifies how good the regression model fits the data. The p-value shows that the independent variables taken in the model significantly predict the dependent variable, number of games won.
b. What is the computed coefficient of determination? Interpret what this number indicates? (2 points)
The computed coefficient of determination or the R2 = 0.748.
The R2 indicates the amount of variance in the dependent variable, number of games won, explained by the independent variables taken in the model. In our model the value shows that the independent variables explain almost 74.8% variance of the dependent variable.
c. Is there a problem with multicollinearity in this analysis? Why or why not? (2 points)
There is no problem with multicollinearity in the model.
From the collinearity statistics we find the variance inflation factor or VIF for all the three independent variables are less than 5 and hence one can say that there is no problem with the multicollinearity in the model.
d. Which of the independent variables have significant unique relationships with games won? (2 points)
From the coefficient statistics one can see that the variables season passing yardage and opponents’ season rushing yardage are the significant variables with significant p-values of the t-test.
e. How do you interpret the unstandardized regression coefficient for opponents’ rushing yardage and for percentage of rushing plays? (2 points)
The unstandardized coefficient and the p-value for the opponents’ rushing yardage are given by -0.006 and 0.001 respectively. Which implies that the independent variable is highly significant even at a 1% significance level. and with unit increase in the opponents’ rushing yardage, the number of games won decrease by 0.6% with all other independent variables remaining constant and the same for the variable percentage of rushing plays are given by 0.16 and 0.121 respectively. Which shows that the variable is not significantly different from zero at a 5% significance level.
f. What does the t value and accompanying significance level for opponent’s rushing yardage indicate? (2 points)
T-test is used to check the significance for each independent variable. The test statistic is the t-value which is the ratio of coefficient and the standard error of the same. The test finds if the unstandardized coefficients are significantly different from zero with the null hypothesis being the coefficient equal to zero. If the p-value of the test is less than a certain significance level says 5% (0.05), then one can reject the null hypothesis and conclude that the coefficient is significantly different from zero and hence it has some linear relationship with the dependent variable. Here the t-value and the significance level for the variable opponent’s rushing yardage are found to be -3.667 and 0.001 which implies that the coefficient for the corresponding variable is significantly different from zero.
g. What is the final regression equation resulting from this analysis (use the unstandardized regression coefficients to write the regression equation)? (1 point)
The final regression equation is given below.

h. What substantive conclusions would you come to from this analysis? (2 points)
The analysis implies that season passing yardage and opponents’ season rushing yardage have significant relationship with number of games won. Whereas, percent of rushing plays doesn’t seem to have a significant linear relationship with number of games won. Three variables taken in the model almost explains 74.8% of the variance of the response variable, number of games won. Which is quite good...
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