Regression analysis in Excel: Guide to data analysis

The regression analysis is an essential tool for investigating and quantifying the relationships between different variables. In this guide, I will show you how to perform a regression analysis in Excel to model a linear functional relationship between various performance characteristics of a company and its revenue. We are particularly interested in determining which of the characteristics has the strongest impact on revenue. Follow this process step by step with me.

Main Insights

• Performing a regression analysis in Excel to determine relationships between variables.
• Interpreting the goodness of fit of the regression analysis based on the coefficient of determination.
• Evaluating statistical significance using p-values and F-values.
• Calculating the relative importance of independent variables using the beta coefficient.

Step-by-Step Guide

To successfully perform the regression analysis in Excel, follow these steps:

Step 1: Prepare and Insert Data

Before conducting the regression analysis, you must enter all relevant data into Excel. This includes the list of performance characteristics (such as delivery, value for money, customer service, etc.) and the revenue you wish to investigate.

Step 2: Select Independent and Dependent Variables

Identify the dependent variable – in this case, revenue – and the independent variables you wish to analyze. Highlight the data for the independent variables (X1 to X6) and the dependent variable.

Step 3: Activate Data Analysis Tool

Activate the Data Analysis Tool in Excel from the ribbon. Go to "Data" and look for the "Data Analysis" icon, which provides you with various statistical functions.

Step 4: Choose Regression

Select the "Regression" option from the list of available analysis tools. This allows you to conduct a regression analysis.

Step 5: Configure Regression Input

In the regression input, select the range for your dependent variable (revenue) and the independent variables (performance characteristics). Make sure to enable labels so that the variables are correctly identified.

Step 6: Define Output Range

Define the output range where you want to see the results of your analysis. You can place this directly to the right of your dataset to keep the data organized.

Step 7: Verify Confidence Level

Ensure that the confidence level is set to 95%, which is a standard value in most statistical analyses. This relates to the confidence in the results of your analysis.

Step 8: Interpret Results

Once the analysis is complete, the results will be displayed in Excel. Begin by interpreting the coefficient of determination (R²), which ranges from 0 to 1. A value of 0.041 indicates that only 4% of the variance in revenue is explained by the independent variables.

Step 9: Evaluate Statistical Significance

Check the p-values and F-value of your analysis. A p-value below 0.05 indicates high statistical significance, meaning that the results are valuable in the context of your hypotheses.

Step 10: Calculate Relative Importance of Variables

Calculate the beta coefficient for each independent variable. This coefficient indicates how strongly an independent variable influences the dependent variables. Higher values signal a greater relative importance.

Summary

In this guide, you learned how to conduct a regression analysis in Excel. You learned how to prepare your data, perform the analysis, and interpret the results. The insights from this technique are crucial for identifying the factors that influence your company's revenue.