Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and independent variables. We can use it to assess the strength of the relationship between variables and for modeling the future relationship between them.

## Data Analysis Toolpak

The Data Analysis ToolPak is an Excel add-in that provides data analysis tools for financial, statistical, and engineering data analysis

• Click the File tab, click Options, and then click the Add-Ins category.
• Select Analysis ToolPak and click on the Go button.
• Check Analysis ToolPak and click on OK. • On the Data tab, in the Analysis group, you can now click on Data Analysis. Gain expertise in Business analytics tools ## Run Regression Analysis

In Excel, we use regression analysis to estimate the relationships between two or more variables. There are two basic terms that you need to be familiar with:

The Dependent Variable is the factor you are trying to predict.

The Independent Variable is the factor that might influence the dependent variable.

Consider the following data where we have a number of COVID cases and masks sold in a particular month. • Go to the Data tab > Analysis group > Data analysis.
• Select Regression and click OK.

The following argument window will open. Select the Input Y Range as the number of masks sold and Input X Range as COVID cases. Check the residuals and click OK.

You will get the following output:  ## Interpret Regression Analysis Output

Let us now understand the meaning of each of the terms in the output. We will divide the output into four major parts for our understanding.

### Summary Output

The summary output tells you how well the calculated linear regression equation fits your data source. The Multiple R is the Correlation Coefficient that measures the strength of a linear relationship between two variables. The larger the absolute value, the stronger is the relationship.

• 1 means a strong positive relationship
• -1 means a strong negative relationship
• 0 means no relationship at all

R Square signifies the Coefficient of Determination, which shows the goodness of fit. It shows how many points fall on the regression line. In our example, the value of R square is 0.96, which is an excellent fit. In other words, 96% of the dependent variables (y-values) are explained by the independent variables (x-values).

Adjusted R Square is the modified version of R square that adjusts for predictors that are not significant to the regression model.

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Mastery to Data Analytics Basics is a Click Away! Standard Error is another goodness-of-fit measure that shows the precision of your regression analysis.

### ANOVA

ANOVA stands for Analysis of Variance. It gives information about the levels of variability within your regression model. • Df is the number of degrees of freedom associated with the sources of variance.
• SS is the sum of squares. The smaller the Residual SS viz a viz the Total SS, the better the fitment of your model with the data.
• MS is the mean square.
• F is the F statistic or F-test for the null hypothesis. It is very effectively used to test the overall model significance.
• Significance F is the P-value of F.

## Regression Graph In Excel

You can quickly visualize the relationship between the two variables by creating a graph. To create a linear regression graph, follow these steps:

• Select the two variable columns of your data, including the headers.
• Go to Insert tab > Charts group > Scatter Plot.   You will get a scatter plot in your worksheet. • Now to add the trend line, right-click on any point and select Add Trend line. Gain expertise in the latest Business analytics tools and techniques with the Business Analyst Master's Program. Enroll now!

## Conclusion

That’s how you do Regression analysis in Excel. You should also know the fact that Microsoft Excel is not a statistical program.

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