Excel Regression Analysis

Excel Regression Analysis

Regression Analysis comes from the Statistical modeling concept. It is the analysis through which you can determine the relationship between the multiple variables. You can now perform Regression Analysis in Excel, which is very straightforward. You don’t have to start the analysis from scratch specifically, and Excel has an inbuilt method to calculate the regression. You can find the Regression Analysis option in the Data tab. To access the Regression Analysis feature in Excel, you must first add Data Analysis ToolPak add-in to the excel. To describe Regression Analysis more specifically, it estimates the relationship between the dependent and independent variables by producing an equation. The constructed equation has a coefficient representing the relationship between dependent and independent variables.

Speaking of simple linear regression, the value of one variable is utilized for describing the value of another. The variable you describe is the dependent variable, and the variable that predicts the value of the dependent variable is the independent variable. You can say that dependent and independent variables are the two prominent essential terms of Regression Analysis. The dependent variable is the predictor variable, and the independent variable is also known as the explanatory variable. Thus, the factor that you try to understand or predict is the dependent variable and its value changes with respect to the value of the independent variable. The factors that affect the dependent variable and predict their value are the independent variables. 

Before performing Regression Analysis in Excel, make sure you have Data Analysis ToolPak in the Data tab in the analysis section. If it is not enabled, add the add-in to your Excel because you will not be able to perform the Regression Analysis without it. To perform the Regression Analysis on the data collected, go to the Data tab and click on the Data Analysis. A dialogue box appears. From the options available, click on Regression and click OK. A regression dialogue box appears. Input your Y and X ranges that are the dependent and independent variables. Check the labels checkbox if you have added header cells into your range and carefully choose the output option. Check the residual checkbox that gives you the difference between the actual and predictive values. After entering all the details, click OK.

After following all the steps, it will summarize your data for the analysis done. See your sheet for the output provided. This output summary contains the Regression Statistics, ANOVA, and residual output, and you will find all these details on the same page. You have performed the Regression Analysis and have got the output and the statements. Calculations are easy as everything is automated and done by computer. Hence, after the execution of Regression Analysis, you need to understand and interpret the result. Regression Statistics provide information on how linear equations are relatable to your data. In the Regression Statistics table, you will find several terms you must know and understand. 

A correlation coefficient is denoted by Multiple R. This helps you measure the strength of the linear relationship between the variables. The higher the value of the Multiple R, the higher the strength of the relationship between the two variables. Below are the values that indicate the degree of the strength of the relationship.

1 - indicates a strong positive relationship

1 - indicates a strong negative relationship

0 - No relationship at all

R Square denotes the coefficient of determination, and this value represents the goddess of fit. Adjusted R Square is the term that is advanced of the R Square. This allows the adjustments of the independent variables and is used in the various analysis. Standard Error is the measure of goodness of fit, and this Regression equation is more accurate for the smaller numbers. Observation indicates the total number of observations made from your model.

You can now learn all the concepts related to Regression Analysis in Excel in-depth by enrolling in Great Learning’s free Regression Analysis in Excel course. You will get to know the terms required to understand the Regression Analysis. You will first understand the meaning of Analysis and why it is crucial. You will then learn Regression Analysis in-depth, with an appropriate example being demonstrated. Enroll in this free course and attain Regression Analysis certification. Enroll Today!