SPSS How-To — OLS, Correlation, VIF

1. Save the Excel file (e.g., ENGR200_Term_Project_Data.xlsx) to your computer. 2. Start SPSS (via JU download or JU virtual lab). 3. In SPSS: File → Open → Data… 4. At the bottom, change “Files of type” to: Excel (*.xls, *.xlsx, …). 5. Browse to your Excel file, select it, click Open. 6. In the Import dialog: • Check: “Read variable names from the first row of data”. • Click OK. 7. You should now see your variables as columns in Data View and in Variable View.

1. Analyze → Descriptive Statistics → Descriptives… 2. In the Descriptives dialog: • Move Statics_Grade into the Variables box. (Optional) Add other numeric variables (HS_GPA, SAT_Math, etc.) if you want. 3. Click Options… • Check: Mean, Std. deviation, Minimum, Maximum. • (You may leave N already checked.) • Click Continue. 4. Click OK. 5. In the Output Viewer, SPSS will show a table with N, Mean, Std. deviation, Minimum, Maximum for Statics_Grade (and any other variables you included).

Option A — via Frequencies (recommended for this class): 1. Analyze → Descriptive Statistics → Frequencies… 2. Move Statics_Grade into the Variable(s) box. 3. Uncheck “Display frequency tables” (to avoid a long table). 4. Click Charts… • Select Histogram. • (Optional) Check “Show normal curve” if you want it drawn on top. • Click Continue. 5. Click OK. 6. In the Output Viewer, SPSS will show a histogram of Statics_Grade. Option B — via Graphs: 1. Graphs → Chart Builder… 2. In the gallery, choose Histogram (simple). 3. Drag the simple histogram icon into the large preview box. 4. Drag Statics_Grade into the x-axis. 5. Click OK to create the histogram.

1. In the top menu: Analyze → Correlate → Bivariate… 2. In the Bivariate Correlations dialog: • Select all numeric variables you want in the matrix (e.g., HS_GPA, SAT_Math, SAT_Verbal, Study_Hours_per_week, Attendance_%, Class_Participation, Statics_Grade). • Move them into the Variables box. 3. Keep “Pearson” checked under Correlation Coefficients. 4. Usually leave “Two-tailed” under Test of Significance. 5. Optionally uncheck “Flag significant correlations” if you only want coefficients. 6. Click OK. 7. SPSS Output Viewer will show the correlation matrix table. You can right-click the table → Copy or Copy Special… → paste into Word.

1. Analyze → Regression → Linear… 2. In the Linear Regression dialog: • Move Statics_Grade into the Dependent box. • Move Study_Hours_per_week into the Independent(s) box. 3. Ensure Method is set to: Enter. 4. Click Statistics… • Check: Estimates • Check: Model fit • (Optional) Check: Confidence intervals • (You do NOT need Collinearity diagnostics if you only have one predictor.) • Click Continue. 5. Optional residuals and plots: • Click Save… → under Residuals, check: Standardized → Continue. • Click Plots… → X: *ZPRED*, Y: *ZRESID* → Add → Continue. 6. Click OK. 7. Output Viewer will show: • Model Summary (R, R², adjusted R², Std. Error). • ANOVA table (F and significance). • Coefficients table: Intercept (Constant) and Study_Hours_per_week with β, Std. Error, t, Sig. • A residuals vs predicted plot if requested, and a new residual variable in the data window.

1. Analyze → Regression → Linear… 2. In the Linear Regression dialog: • Move Statics_Grade into the Dependent box. • Move HS_GPA, SAT_Math, SAT_Verbal, Study_Hours_per_week, Attendance_%, Class_Participation into the Independent(s) box. 3. Ensure Method is: Enter (standard OLS with all predictors in the model). 4. Click Statistics… Under Regression Coefficients / Model: • Check: Estimates • Check: Model fit • (Optional) Check: Confidence intervals Under Collinearity diagnostics: • Check: Collinearity diagnostics (this is what gives Tolerance and VIF). Click Continue. 5. Optional residuals and plots: • Click Save…: – Under Predicted Values: check Standardized (ZPRED). – Under Residuals: check Standardized (ZRESID). – Click Continue. • Click Plots…: – X: *ZPRED* Y: *ZRESID* → Add – (Optional) Check “Histogram” or “Normal probability plot.” – Click Continue. 6. Click OK. 7. Output Viewer will now show: • Model Summary: R, R², adjusted R², Std. Error. • ANOVA table: Regression and Residual SS, df, MS, F. • Coefficients table: – “Unstandardized Coefficients”: B (β), Std. Error – “Standardized Coefficients”: Beta (optional) – t and Sig. – At the far right: Tolerance and VIF columns (from Collinearity diagnostics). • If Save… was used: residual plots and new variables (ZPRED, ZRESID) in the data window.

1. In the Linear Regression dialog (Analyze → Regression → Linear…): • After setting Dependent and Independent(s), click Save… 2. In the Save dialog: • Under Predicted Values: check Standardized (creates *ZPRED). • Under Residuals: check Standardized (creates *ZRESID). • (Optional) Under Distances: you may check Mahalanobis or Cook's if needed. • Click Continue. 3. For a residual vs fitted plot: • Click Plots… • Set X: *ZPRED*, Y: *ZRESID*. • Click Add. • (Optional) Check “Histogram” or “Normal probability plot” for residuals. • Click Continue. 4. Click OK to run the regression. 5. SPSS will: • Create new columns in the data set (e.g., ZPRED, ZRESID). • Add plots under the regression output in the Output Viewer.

Option A — Copy/paste individual items: 1. In the SPSS Output Viewer, right-click a table (e.g., Descriptives, Correlations, Coefficients) or chart. 2. Choose Copy (or Copy Special…). 3. In Word, Paste (Ctrl+V). If you used Copy Special, you can choose image or formatted table. Option B — Export the entire output: 1. In the SPSS Output Viewer: File → Export… 2. Choose Document type: Word / PDF / HTML as needed. 3. Choose which output objects to export (usually “All visible” is fine). 4. Choose a filename and location. 5. Click OK. 6. Open the exported Word/PDF file and trim/edit the tables and figures you want in your report.