Multiple Linear Regression is a powerful statistical technique that models the relationship between a dependent variable and multiple independent variables. It is used in business intelligence to make predictions and gain insights. It also plays a key role in understanding how various factors influence outcomes. In this blog, let us explore Multiple Linear Regression and its visualization.
Table of Contents:
What is Multiple Linear Regression in Power BI?
Multiple Linear Regression in Power BI is a modified or extended version of simple linear regression that is used to model the relationship between a single dependent variable (Y) and two or more independent variables (X1, X2, X3,…Xn). This function aims to find the best-fitting line that reduces the difference between actual and predicted values.
The general equation of Multiple Linear Regression:
Y = B0 + B1X1 + B2X2 + ..... + BnXn + e
Y = Dependent variable
X1.....Xn are independent variables
B0 is the intercept
B1...Bn are Regression coefficients
e = Error term
Why Multiple Linear Regression is Important in Power BI?
Multiple Linear Regression in Power BI is important because it helps in:
- Predictive Analysis: MLR helps users predict future outcomes based on past data.
- Decision-Making: Many businesses use MLR to make decisions by understanding how an independent variable affects a dependent variable.
- Improve Performance: The company can improve its performance by identifying factors and building strategies according to those.
- Risk Management: MLR helps in examining areas of risk through data analysis.
Let’s Perform Multiple Linear Regression with the help of the DAX formula.
Using DAX Functions
We can implement Multiple Linear Regression with the help of DAX formulas in Power BI, which helps to analyze the relationship between one dependent variable and multiple independent variables.
Step 1: Load the Data into Power BI
Step 2: Clean the Data
If the table consists of data that includes Null or duplicate values, kindly clean your data before applying the DAX formula.
Here, the dataset has all relevant values cleaned.
Step 3: Write a DAX formula
Open the Data view, click on New Table, and write the formula below to calculate multiple linear regression.
Linest_Elentra =
LINEST(
elantra[ElantraSales],
elantra[Unemployment],
elantra[CPI_all],
elantra[CPI_energy],
elantra[Queries],
elantra[Year],
elantra[Month]
)
Step 4: Result
Explanation: Here, the LINEST() is a function that is used to calculate the statistics for a straight line that fits the given data using the least squares method.
Visualising Regression Results
Let’s see how our results look when we visualize them with the help of different graphs.
F-Statistics and Degree of Freedom
A simple table to display the F-statistic and its associated degrees of freedom.
Regression Sum of Squares and Residual Sum of Squares
Steps to Create a Horizontal Bar Chart
Go to Visualization pane>Select Horizontal Bar Chart>Drag values in the field.
A stacked bar chart is used to visualize the part of variance explained by the model and compare it to the unexplained variance. This will help to assess the model’s goodness of fit
Sum of Intercept vs Sum of Slope 3 vs Sum of F statistic
Steps to Create a Pie Chart:
Go to Visualization pane>Select Pie chart>Drag values in the field.
The pie chart is used to visualize the proportion of the intercept, slope, and F-statistic values. Note that these are not categorical variables.
Key Assumptions of Multiple Linear Regression in Power BI
- Linearity: A Linear relationship between independent and dependent variables must exist.
- Multicollinearity: Independent variables should not be highly correlated.
- Homoscedasticity: The Variance of residuals should be constant across all levels.
- Normality of Residuals: Residuals should follow a normal distribution.
- R-squared: Measures how well independent variables explain the variance of the dependent variable.
- Adjusted R-squared: The Number of predictors in the model is adjusted.
- P-value: Determines the statistical significance of predictors.
- Mean Squared Error: Represents the average squared difference between actual and predicted values
Use Case of Multiple Linear Regression in Power BI
- Sales Forecasting: Future sales are predicted based on advertising spend and other market trends.
- Customer Churn Prediction: Customer retention is identified by identifying key factors.
- Risk Assessment: Evaluate financial risk based on economic conditions and company performance measures.
- Pricing Prediction: Predict the pricing models based on historical data.
Challenges and Limitations of Multiple Linear Regression in Power BI
- Overfitting: If there are too many independent variables, this can lead to overfitting in a model.
- Multicollinearity: High correlation between predictors can disturb the result.
- Data Quality: Low data quality can lead to an inaccurate model.
- Multiple Factors: Understanding the impact of numerous factors requires expertise.
Conclusion
Multiple linear regression is a powerful analytical tool in Power BI that helps in extracting insights and making data-driven decisions. Working on MLR may improve performance and reduce the risk of loss. However, ensuring the quality of data and validating assumptions are important for accurate results. Power BI, combined with DAX functions, makes MLR usage very easy and helps users to uncover hidden data very efficiently. In this article, you have gained knowledge about Multiple Linear Regression and its visualization practices.
To learn more about Power BI and its functions, check out this Power BI Course and also explore Power BI Interview Questions prepared by industry experts.
Multiple Linear Regression in Power BI – FAQs
Q1. What is Multiple Linear Regression used for?
Multiple Linear Regression is used to model a relationship between a dependent variable and predictions for insights.
Q2. Can I perform Multiple Linear Regression in Power BI?
Yes, you can perform multiple regression with the help of the Linest() function in Power BI.
Q3. What is the difference between simple and multiple linear regression?
Simple Linear Regression has one independent variable, while Multiple Linear Regression has two or more independent variables.
Q4. How do I check if my regression model is accurate?
You can check the regression model with the help of R-squared, adjusted R-squared, p-values, and Mean Squared Error.
Q5. Can I visualize regression results in Power BI?
Yes, you can visualize the regression results by using graphs like a bar chart, a column chart, etc.