**Contents**show

## How is multiple regression used to predict a variable?

As a predictive analysis, the multiple linear regression is **used to explain the relationship between one continuous dependent variable and two or more independent variables**. The independent variables can be continuous or categorical (dummy coded as appropriate).

## What is a multiple regression analysis used for?

Multiple regression analysis allows researchers to assess the strength of the relationship between an outcome (the dependent variable) and several predictor variables as well as the importance of each of the predictors to the relationship, often with the effect of other predictors statistically eliminated.

## How do you present multiple regression results?

Still, in presenting the results for any multiple regression equation, it should always be clear from the table: (1) **what the dependent variable is**; (2) what the independent variables are; (3) the values of the partial slope coefficients (either unstandardized, standardized, or both); and (4) the details of any test of …

## When should a regression model not be used to make a prediction?

Never do a regression analysis unless you have **already found at least a moderately strong correlation between the two variables**. (A good rule of thumb is it should be at or beyond either positive or negative 0.50.)

## How do you predict a regression equation?

The line of regression of Y on X is given by **Y = a + bX** where a and b are unknown constants known as intercept and slope of the equation. This is used to predict the unknown value of variable Y when value of variable X is known.

## Which is an example of multiple regression?

For example, if you’re doing a multiple regression to try to **predict blood pressure** (the dependent variable) from independent variables such as height, weight, age, and hours of exercise per week, you’d also want to include sex as one of your independent variables.

## What are the five assumptions of linear multiple regression?

**Linearity: The relationship between X and the mean of Y is linear**. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other. Normality: For any fixed value of X, Y is normally distributed.