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A regression coefficient represents the mean change in the dependent variable for each unit change in the independent variable. In regression analysis, it is used to determine the strength and direction of the relationship between variables.

R-squared is the proportion of variance in the dependent variable that can be predicted from the independent variable(s). It is used in regression analysis to assess the goodness-of-fit of the model.

Adjusted R-squared adjusts the R-squared value for the number of predictors in the model. It is used in regression to provide a more accurate measure of goodness-of-fit when there are multiple predictors.

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, potentially distorting the results. It's important to detect and address to ensure the model's reliability.

The standard error of the estimate is a measure of the accuracy of predictions made with a regression line, representing the average distance between the observed points and the regression line.

The F-statistic is used in the context of ANOVA within regression analysis to test if the variance explained by the model as a whole is significantly greater than the variance unexplained.

The p-value in regression indicates the probability of observing the data if the null hypothesis is true. It is used to determine the statistical significance of the coefficients.

A confidence interval in regression is a range within which the true value of the coefficient is expected to fall with a certain probability. It is used to indicate the reliability of the coefficient estimates.

A dummy variable is a binary variable created to represent a categorical variable in regression. It is used to include categorical predictors in a regression model.

Variable transformation involves applying a mathematical operation to a variable to meet the assumptions of regression, such as linearity or normality.

Outliers are data points that deviate significantly from the other observations. In regression, they can greatly affect the slope of the regression line, and thus need to be examined carefully.

Leverage is a measure of how far an independent variable value is from the mean of those variables. High leverage points can have a disproportionate impact on the regression equation.

Influential points are data points that exert a large influence on the regression analysis outcome. They are critical to identify as they can distort the regression model.

Homoscedasticity refers to the assumption that the residuals (errors) have constant variance at all levels of the independent variables. It's crucial for valid statistical inferences in regression.

Logistic regression is used when the dependent variable is binary or categorical. It predicts the probability of the occurrence of an event by fitting data to a logit function.

OLS is a method for estimating the parameters in a regression model. It minimizes the sum of the squared differences between observed and predicted values.

An interaction effect occurs when the effect of one independent variable on the dependent variable differs depending on the level of another independent variable. It is modeled by including a product term in the regression.

Linearity is the assumption that there's a straight-line relationship between independent and dependent variables. It's essential for the correct specification of the regression model.

In regression, independent variables or predictors are the variables used to predict or explain variations in the dependent variable. Their selection is crucial for model accuracy.