# Difference between revisions of "Manuals/calci/REGRESSIONANALYSIS"

REGRESSIONANALYSIS (YRange,XRange,ConfidenceLevel,NewTableFlag)

• is the set of dependent variables .
• is the set of independent variables.
• level of Confidence value.
• is either 0 or 1.

## Description

• This function is calculating the Regression analysis of the given data.
• This analysis is very useful for the analyzing the large amounts of data and making predictions.
• Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent and independent variable.
• This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables.
• This analysis give the result in three table values.
1. Regression statistics table.
2. ANOVA table.
3. Residual output.

1.Regression statistics :

• It contains multiple R, R Square, Adjusted R Square, Standard Error and observations.
• R square gives the fitness of the data with the regression line.
• That value is closer to 1 is the better the regression line fits the data.
• Standard Error refers to the estimated standard deviation of the error term. It is called the standard error of the regression.

2.ANOVA table:

• ANOVA is the analysis of variance.
• This table splits in to two components which is Residual and Regression.
```Total sum of squares = Residual (error) sum of squares + Regression (explained) sum of squares.
```
• Also this table gives the probability, T stat, significance of F and P.
• When the significance of F is < 0.05, then the result for the given data is statistically significant.
• When the significance of F is > 0.05, then better to stop using this set of independent variables.
• Then remove a variable with a high P-value and return the regression until Significance F drops below 0.05.
• So the Significance of P value should be <0.05.
• This table containing the regression coefficient values also.

3.Residual output:

• The residuals show you how far away the actual data points are from the predicted data points.