Question.4865 - SPSS Assignment 3 Directions: For the third SPSS/Excel assignment, you will be running a standard multiple regression on a given dataset. The below requirements must be emailed to me at aaron.bartula@untdallas.edu as a Microsoft Word document. In the Modules tab, scroll down to the section titled Data Sets. Using the dataset entitled "sleep", complete the following. Note, if you are using SPSS you will select the file sleep.sav; if you are using Microsoft Excel you will use the datafile sleep_data.xls. Procedure: For the below analysis, you will be using “totSAS” for your dependent variable (Sleepiness), and “sex”, “age”, “fitrate” and “depress”, as the independent variables from the survey dataset. Part 1: Run the Standard Multiple Regression Model • Using the above five variables, run a standard multiple regression in either SPSS or Excel. • Copy and paste the results into a Word document. Part 2: Determine Model Fit • Looking at the correlation table, determine if any variables should be excluded due to high correlation factors. Make sure the table is copy and pasted into the Word document and your interpretation. • Looking at the VIF coefficient, determine if collinearity is an issue with the model. Report the VIF coefficient and your interpretation in the Word document. • Report the R ² value and interpret the results. Part 3: Testing for Significance • Evaluate each independent variable’s coefficient and determine which are statically significant. • Interpret the results for each independent variable. • Email the newly created Word document to my UNT Dallas email address (aaron.bartula@untdallas.edu) by 11:59pm on the scheduled due date.
Answer Below:
SPSS xxxxxxxxxx Part xxxx The xxxxxxxx is xxxxx means xxxx approximately xx the xxxxxxxx is xxxxxxxxxx is xxxxxxxxx by xxx independent xxxxxxxxx sex xxx physical xxxxxxx and xxxx Depression xxxx is x moderate xxx for xxx model xxxx the xxxxxxxxxxx table xx can xxxxxxx that xxx the xxxx of xxx independent xxxxxxxx is xxxx than xxx VIF xx independent xxxxxxxx Sex xx age xx physical xxxxxxx is xxxx Depression xx All xxx values xxx below xxxxx suggests xxxx collinearity xx not xx issue xx this xxxxx Part xxx the xxxxxxxxxxx variables xxx statistically xxxxxxxxxxx sex x - x is xxxxxxxxxxxxx significant xxxxx the xx value xx less xxxx age x - x is xxxxxxxxxxxxx significant xxxxx the xx value xx less xxxx physical xxxxxxx B x p xx statistically xxxxxxxxxxx since xxx p- xxxxx is xxxx than xxxx Depression x p xx statistically xxxxxxxxxxx since xxx p- xxxxx is xxxx than xxxx independent xxxxxxxx contributes xxxxxxxxxxxxx to xxx model xx predicting xxxxxxxxxx Sex xxx and xxxxxxxx fitness xxxx negative xxxxxx on xxxxxxxxxx and xxxx Depression xxx positive xxxxxx on xxxxxxxxxxMore Articles From Quantitative data analysis
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