Linear Regression Linear regression is used to predict the value of one variable from another variable....

Linear Regression

Linear regression is used to predict the value of one variable from another variable. Since it is based on correlation, it cannot provide causation. In addition, the strength of the relationship between the two variables affects the ability to predict one variable from the other variable; that is, the stronger the relationship between the two variables, the better the ability to do prediction.

What is one instance where you think linear regression would be useful to you in your workplace or chosen major? Please describe including why and how it would be used.?

Need 250 words answer in word fiel

Expert Solution

Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation. The goal of a correlation analysis is to see whether two measurement variables co vary, and to quantify the strength of the relationship between the variables, whereas regression expresses the relationship in the form of an equation.

For example, in students taking a Maths and English test, we could use correlation to determine whether students who are good at Maths tend to be good at English as well, and regression to determine whether the marks in English can be predicted for given marks in Maths.

2In the example, if we are trying to determine whether the marks in English can be predicted for given marks in Maths, then marks in Maths is the independent variable and marks in English is a dependent variable.'

If there are more than one independent variable, then a simple linear regression model is unsuitable for the prediction model.In such cases, a Multiple Regression model is used.

A multiple linear regression model shows the relationship between the dependent variable and multiple (two or more) independent variables.

orchestra answered 2 years ago

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