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- is the set of dependent variables .
- is the set of independent variables.
- level of Confidence value.
- is either 0 or 1.
- 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.
- Regression statistics table.
- ANOVA table.
- 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.
- 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.
- The residuals show you how far away the actual data points are from the predicted data points.