SUMMARY OUTPUT Regression Statistics Multiple R 0.949222357 R Square 0.901023083 Adjusted R Square 0.894819049 Standard Error 2.470075201 Observations 230 ANOVA df SS

The regression summary output you provided gives insights into a statistical model that examines the relationship between a dependent variable (not specified in the output) and several independent variables (Age, Gender, Industry, Job Changer, Job Stayer). Here's a breakdown of the key components: ### 1. **Regression Statistics:** - **Multiple R (0.9492):** This value indicates a strong positive correlation between the observed and predicted values of the dependent variable. Closer to 1 suggests a strong relationship. - **R Square (0.9010):** Approximately 90.1% of the variance in the dependent variable can be explained by the independent variables included in the model. This suggests a very strong fit. - **Adjusted R Square (0.8948):** This statistic adjusts the R Square value for the number of predictors in the model, providing a more accurate measure when comparing models with different numbers of predictors. The close value to R Square reflects that the model is appropriate with the included variables. - **Standard Error (2.47):** This is the standard deviation of the regression errors (residuals), which gives an idea of the average distance that the observed values fall from the regression line. A lower value is generally preferred. - **Observations (230):** The number of data points used in the regression analysis. ### 2. **ANOVA (Analysis of Variance):** - **Regression df (5):** The degrees of freedom associated with the regression model. - **Residual df (225):** The degrees of freedom associated with the residuals, which is obtained by subtracting the number of predictors plus 1 from the total number of observations. - **SS (Sum of Squares):** This measures the variability. The Regression SS (12496.97) represents the explained variance, and the Residual SS (1372.79) represents the unexplained variance. - **MS (Mean Square):** Mean squares are obtained by dividing the sum of squares by their respective degrees of freedom. - **F-statistic (409.65):** This measures the overall significance of the regression model. A high value indicates that the model is statistically significant. - **Significance F (1.5667E-110):** This extremely low p-value indicates that the overall regression model is statistically significant, meaning the independent variables collectively have an effect on the dependent variable. ### 3. **Coefficients:** - Each predictor has its associated coefficient, indicating the expected change in the dependent variable for a one-unit increase in that predictor, holding all other variables constant. - **Age (7.64):** For each additional year, the dependent variable increases by approximately 7.64 units. - **Gender (5.61):** Changing gender (if coded appropriately) is associated with an increase of about 5.61 units in the dependent variable. - **Industry (5.66):** Represents the effect of the industry on the dependent variable, potentially indicating a strong influence. - **Job Changer (10.90):** Significantly, changing jobs increases the dependent variable by about 10.90 units. - **Job Stayer (5.56):** Staying in a job is associated with an increase of about 5.56 units. ### 4. **Statistical Significance of Coefficients:** - The t-statistic and p-value (all extremely low) for each coefficient suggest that all the independent variables are statistically significant contributors to the model. - The 95% confidence intervals for each coefficient indicate the range within which we can be 95% confident the true population parameter lies. ### Conclusion: Overall, this regression analysis suggests a very strong model where Age, Gender, Industry, Job Changer, and Job Stayer are statistically significant predictors of the dependent variable. The high R Square suggests that the model explains most of the variance in the dependent variable, and the results are statistically significant, indicating a reliable model.