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# Driver 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?

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.
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.

## 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. 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.
1. 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.
1. 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].
1. 4.
$R^2$
$R^2$
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.
1. 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."
1. 6.
Records
The number of records used in the analysis (excluding records with irrelevant values).
1. 7.