Question

Discuss the statistics that must be evaluated when reviewing the regression analysis output. Provide examples of what the values represent and an explanation of why they are important.

Answer 1

A regression analysis says whether the predicted variable changes with the dependent variable . In regression the analysis always direct to the evaluation of the R square , F test interpretation of beta variable and finally the regression equation .  

When the regression is conducted an F value and significance level of F value is calculated if the F value is statistically significant the model explains a significant amount of variance in the outcome variable . Regularly the value is significant when p<0.05 .Like this a R² value is also calculated it can be indicated as the percent of variance in the outcome variable that is explained by a predicted variable . After these it is important to obtain the beta variable it can be negative or positive and have a t value . The beta variable is the degree of change of outcome variable for every 1 unit of change in prediction variables .If beta coefficient is negative then every 1 unit increase in the prediction variable the outcome variable will decrease by the beta value . Also if the beta coefficient is positive then every 1 unit increase in the prediction variable the outcome variable will increase by the beta coefficient value .

The P value for each term tests the null hypothesis that the coefficient is zero means no effect , a low P value indicate that you can reject the null hypothesis ,the P value give u an idea about which terms to keep in the regression model .R squared says how close the data are to the fitted regression line . If we get 0 % it says that model explain none of the variability of the response data around it's mean.