Linear regression (also called simple regression) is one of the most common techniques of regression analysis. Multiple regression is a broader class of regression analysis, which encompasses both ...

Multiple Linear Regression: Multiple linear regression describes the correlation between two or more independent variables and a dependent variable, also using a straight regression line.

With a wealth of data at their disposal, the team decided to use multiple linear regression analysis to determine which factors were most strongly correlated with employee turnover.

Linear regression is a type of data analysis that considers the linear relationship between a dependent variable and one or more independent variables. It is typically used to visually show the ...

For example, you might want to predict an employee's salary based on age, height, years of experience, and so on. There are approximately a dozen common regression techniques. The most basic technique ...

Logistic regression is preferrable over a simpler statistical test such as chi-squared test or Fisher’s exact test as it can incorporate more than one explanatory variable and deals with possible ...

Using Linear Regression Because much economic data has cycles, multiple trends and non-linearity, simple linear regression is often inappropriate for time-series work, according to Yale University.

Multiple regression models with survey data Regression becomes a more useful tool when researchers want to look at multiple factors simultaneously. If we want to know whether the racial divide ...