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

Answer:

• A regression equation  says whether the anticipated variable changes with the reliant variable . In relapse the examination in every case direct to the assessment of the R square , F test translation of beta variable lastly the regression equation.
• When the regression is conducted an F value and significance level of F esteem  is calculated if the F esteem  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 esteem is likewise determined it tends to be shown as the percent of difference in the result variable that is clarified by an anticipated variable . After these it is critical to get the beta variable it very well may be negative or positive and have a t esteem . The beta variable is the level of progress of result variable for each 1 unit of progress in expectation factors .If beta coefficient is negative then every 1 unit increment in the prediction variable the result variable will decrease by the beta worth. Additionally in the event that the beta coefficient is sure, at that point each 1 unit increment in the forecast variable the result variable will increment by the beta coefficient esteem.
• The P esteem for each term tests the invalid theory that the coefficient is zero methods no impact , a low P esteem demonstrate that you can reject the null hypothesis,the P esteem give u a thought regarding which terms to keep in the regression  model .R squared says how close the information are to the fitted regression line . In the event that we get 0 % it says that model clarify none of the variability of the reponse information around it's mean.