# 7️⃣Driver Analysis

Understand the factors that influence KPIs. Through the Driver Analysis feature, you can easily identify the crucial factors influencing the KPIs you want to explore.

## 1. **What is Driver Analysis?**

**What is Driver Analysis?**

Driver Analysis is possible only for numerical KPIs. If you haven't registered a numerical KPI yet, go to KPIs Management and register one!

There are times when we want to know which factors influence sales the most. By using the **Driver Analysis** feature, you can determine which factors have the most influence on user-defined KPIs, such as sales.

Driver Analysis is a regression analysis function that finds the most important factors (variables) for the chosen KPI.

**2. How to Use Driver Analysis?**

**2. How to Use Driver Analysis?**

Here's a basic description of the Driver Analysis interface.

Each section of the interface has the following functionalities:

**Filter**

This has the same function as the dashboard filter. You can select or exclude desired variables to view Driver Analysis results. By clicking on the `Variables`

tab to the right of the filter, you can include or exclude derived (automatically generated) variables for the analysis.

Derived (automatically generated) variables are HEARTCOUNT's unique feature that auto-processes numerical variables into categories to discover non-linear patterns.

Click here for a detailed explanation of this feature.

**Variable Selection & Auto-play**

**[All]**allows you to see the results integrated from simple and multiple regression analysis. Simple regression can be viewed under**[1 Variable]**, and the results of multiple regression analysis can be detailed under**[2 Variables]**.The factor analysis results explain the difference of the KPI in order of Adjusted $R^2$, showing which variable can explain how much (%) of the KPI's variance and the corresponding visualization.

When you click the play ▶︎ button, a video like the one below will appear.

$R^2$

$R^2$ represents the coefficient of determination, a statistic that reflects the **proportion of the variance in the dependent variable (KPI) that can be explained by the independent variable**. For example, if the selected KPI, [Sales], is best explained by $R^2$ being 0.56 with the highest values from the variables [Profit_bin] and [Sub-category], it means that 56% of the variance in [Sales] can be explained by [Profit_bin] and [Sub-category].

**Adjusted**$R^2$

Adjusted $R^2$ denotes the adjusted coefficient of determination, which modifies the $R^2$ value considering the number of records and variables used in the analysis. This metric **compensates for the inherent issue of the coefficient of determination increasing indiscriminately** with an increase in independent variables.

When using Driver Analysis with two variables, it's essential to interpret both the coefficient of determination and the adjusted coefficient of determination.

**P-Value**

The P-Value indicates the probability that the analysis results occurred by chance. Typically, if the P-Value is **less than 0.05**, it's considered **"statistically significant."** Conversely, a P-Value exceeding 0.05 suggests a higher likelihood of randomness, and the relationship is deemed **"not statistically significant."**

**Records**

The number of records used in the analysis (excluding records with irrelevant values).

**Smart Link**

Use the Smart Link to move to the Drill-Down and Explainer page for the specific variable.

**Visualization Screen & Control Window**

The topmost result in Driver Analysis is visually displayed on the visualization screen. You can visually verify the results through scatter plots, bar graphs, etc. For a detailed description of the visualization screen, click here, and for the control window, click here.

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