How to Approach SPSS Assignment Using Simple Regression Analysis

• Open SPSS and enter three variables: Height, Weight, and Gender.
• Assign appropriate measurement levels: Height and Weight should be scale variables; Gender should be nominal.

Having the data entered correctly is critical for producing reliable regression results.

Creating an Initial Scatterplot

A scatterplot helps in checking whether simple linear regression is appropriate:

• Go to Graphs > Chart Builder > Scatter/Dot.
• Choose Simple Scatter and define it with Height on the X-axis and Weight on the Y-axis.
• Label the chart and assign different symbols for male and female groups to enhance visual clarity.

This plot will help identify outliers and assess whether a linear relationship is plausible.

Running the Regression in SPSS

Once the data appears suitable for linear modeling, the next step is to conduct the regression analysis.

Performing the Regression Procedure

To execute the regression:

• Click on Analyze > Regression > Linear.
• Place Weight in the Dependent box and Height in the Independent(s) box.
• Click OK to generate the output.

SPSS produces multiple tables, including the Model Summary, ANOVA, and Coefficients tables, which are essential for interpreting the regression.

Interpreting the Model Summary and R-Squared

The Model Summary provides:

• R: The correlation coefficient between observed and predicted values.
• R² (R-squared): Indicates the proportion of variance in the dependent variable (Weight) explained by the independent variable (Height). In this example, R² is 0.756, showing a strong relationship.

This means about 75.6% of the variation in weight can be predicted by height.

Analyzing the Output: ANOVA and Coefficients

After understanding the model’s strength, it’s important to verify the statistical significance and extract the regression equation.

Evaluating the ANOVA Table

The ANOVA table tests whether the model significantly improves prediction over just using the mean. It provides:

• F-statistic: Measures the ratio of explained variance to unexplained variance.
• Significance (p-value): A p-value less than 0.05 suggests that the model is statistically significant.

In our case, the p-value is less than 0.000, indicating a significant relationship between height and weight.

Deriving the Regression Equation

From the Coefficients Table, you can find the regression equation:

This equation suggests that for every one-inch increase in height, weight increases by approximately 7.61 pounds.

Both the slope and intercept have p-values below 0.05, confirming their significance in the model.

Enhancing the Regression Output with Plots and Intervals

Adding a fitted line and estimating prediction intervals makes your assignment visually compelling and statistically complete.

Plotting the Fitted Regression Line

To include the regression line on the scatterplot:

• Double-click the scatterplot in the output window.
• Click the Fit Line icon in the toolbar.
• Choose Linear and apply the changes.

This action will insert a best-fit line that reflects the regression equation across the plotted data points.

Adding Confidence and Prediction Intervals

To visualize the uncertainty around your predictions:

• Open the chart editor and click on Fit Line.
• Check the boxes for Confidence Interval and Prediction Interval.

Confidence intervals give a range for the mean weight at a given height, while prediction intervals estimate the range for an individual’s weight.

Additional Features in SPSS for Regression Assignments

Beyond basic regression, SPSS offers additional tools that make your assignment richer in analysis.

Displaying Confidence Intervals for Coefficients

To add confidence intervals for regression coefficients:

• After choosing Analyze > Regression > Linear, click on the Statistics button.
• Select the box for Confidence Intervals and proceed.

The output will now include the 95% confidence intervals for both the intercept and the slope. These intervals give a range within which the true population coefficients are expected to lie.

Saving Predicted Values and Intervals

SPSS also allows you to save predicted values along with confidence and prediction intervals directly into your dataset:

• In the Linear Regression dialog box, click Save.
• Check the options for Unstandardized Predicted Values, Mean Prediction Interval, and Individual Prediction Interval.
• Click Continue, then OK.

These values will appear in your Data View as new variables, allowing further analysis or plotting.

Conclusion

Solving simple regression assignments in SPSS becomes straightforward when you follow a structured approach. Starting with data entry, moving on to scatterplot visualizations, running the regression analysis, interpreting the output, and enhancing it with visual elements like fitted lines and intervals—each step plays a vital role in building a compelling assignment.

This example using height and weight data demonstrated a strong linear relationship. By using SPSS effectively, students can ensure their assignments include all the necessary statistical evidence and visual clarity. Always remember to:

• Validate your data visually before running regression.
• Interpret the R², p-values, and coefficients properly.
• Include plots and intervals for a complete submission.

With these steps, you’ll be better prepared to do your statistics assignment involving regression analysis confidently and accurately.