Description

This function is calculating the Regression analysis of the given data.
This analysis is very useful for the analyzation of large amounts of data and making predictions.
This analysis give the result in three table values.

1.Regression statistics :
It contains multiple R, R Square, Adjusted R Square, Standard Error and observations.
R square gives the fittness 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 returnun 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 fom the predicted data points.

Examples

54 - 8 63 - 6 75 - 3 82 - 1

Unit sales - Ads - population 4000 - 12000 - 300000 5200 - 13150 - 411000 6800 - 14090 - 500000 8000 - 11900 - 650000 10000 - 15000 - 800000