# Driver Analysis ## 1. **What is Driver Analysis?** {% hint style="info" %} Driver Analysis is possible only for numerical KPIs. If you haven't registered a numerical KPI yet, go to KPIs Management and register one! {% endhint %} 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.
Learn more about HEARTCOUNT's regression analysis Regression analysis is a data analysis method used in statistics to estimate and explain relationships between variables. By setting an influencing variable and an influenced variable (KPI), you can understand the relationship between the two. Analyzing the relationship between one variable and a KPI is called simple regression analysis, and when seeking to clarify the relationship between multiple variables and a KPI, it's called multiple regression analysis. In HEARTCOUNT, we use both simple and multiple regression analysis methods to present significant results in order of importance. 👉 Click here to learn more about regression 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:
1. **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. 2. **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.
3. $$R^2$$ $$R^2$$ 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].
4. **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. {% hint style="info" %} When using Driver Analysis with two variables, it's essential to interpret both the coefficient of determination and the adjusted coefficient of determination. {% endhint %} 5. **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."** 6. **Records** The number of records used in the analysis (excluding records with irrelevant values). 7. **Smart Link** Use the Smart Link to move to the [Drill-Down](https://public.heartcount.io/visual-discovery/drill-down) and [Explainer](https://public.heartcount.io/advanced-analysis/explainer) page for the specific variable. 8. **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](https://public.heartcount.io/visual-discovery/smart-plot), and for the control window, click here. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://public.heartcount.io/advanced-analysis/driver-analysis.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.