Interpret regression: coefficients show impact, p-values significance, R-squared fit. Consider variables' signs, magnitude, control, and interaction effects. Relate findings to management implications.

1. An Academic presentation by
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2. Introduction
Regression analysis is a statistical tool for modelling and analyzing variable relationships. The primary
purpose of regression analysis is to estimate the values of one variable (the dependent variable) using
the values of other variables (the independent variables). The following are some important
characteristics of regression analysis:
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Regression Analysis: An Overview
contd...
Dependent variable Independent variables
Regression model Model fitting

4. Coefficients: Look at the coefficients of the independent variables. These represent the
estimated impact of each independent variable on the dependent variable, holding
other variables constant. A positive coefficient indicates a positive relationship, while a
negative coefficient suggests a negative relationship.
Significance Levels (p-values): Check the p-values associated with each coefficient. A
low p-value (typically below 0.05) indicates that the variable is statistically significant. If
a p-value is high, it suggests that you cannot reject the null hypothesis that the variable
does not affect the dependent variable.
R-squared Value: Examine the R-squared value, which represents the proportion of the
variance in the dependent variable explained by the independent variables. A higher R-
squared indicates a better fit of the model to the primary data collection.

5. Adjusted R-squared: This value adjusts the R-squared for the number of predictors in the
model. It's often considered a more reliable measure when comparing models with
different numbers of predictors.
F-statistic: The F-statistic tests the overall significance of the logistic regression model. A
low p-value for the F-statistic indicates that at least one independent variable is
significantly related to the dependent variable.
Residuals Analysis: Examine the residuals (the differences between the observed and
predicted values). A well-fitted model should have residuals that are randomly
distributed and show no pattern. Patterns in residuals might suggest that the model is
missing some explanatory variables.

6. Multicollinearity: Check for multicollinearity among independent variables. High
correlation between independent variables can make it difficult to isolate the individual
effect of each variable.
Heteroscedasticity: Assess whether the variance of the residuals is constant across all
levels of the independent variables. Heteroscedasticity could indicate that the model's
assumptions are not met.
Substantive Significance: Beyond statistical significance, consider the practical or
managerial significance of your findings. Even if a variable is statistically significant, it
may not be practically significant.
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Normality of Residuals: Verify that the residuals are normally distributed. You can use
quantitative statistical tests or visual inspections (like a Q-Q plot) for this purpose.

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