Dick's Sporting Goods Case Study

Dick’s Sporting Goods is known for selling all varitey sport equements like exerciseing meashon, fishing good, hunting products and all sport accessories as well as supplyies. The company was founded in 1948 by Richard Stack. He was currently woking at the Army/Navy store in his hometown which is New York. By the end of World War ll. His grandmother gave him $300 and he rented a store to start the first Dick’s Sporting Goods at that time. Slowly this compay stated to take off and he was expanding more and opeing sevral stores. Now Dick’s has about 610 stores in 48 states as of 2015 mainly in the eastern side of the United States.
Steps for Regression
Regression measures a statistical strength between dependent variable and independent variables
Whenever we are doing multiple regression that means that there is more than one independent variable to perdict the Y or the dependent. The main idea is to see the relationshp between a indpendent variabls and the depndent. A fitted regression is implecating a line that minimizes the sum of squared. After finding out all the variabls that is need to contunue the work, the first step we need to do in the Mnitab is a Correlation Matrix. The reason we are doing this step is to see if any of the variabls are correlated and we do not want them to be so. The Correlation Matrix will let us know that if there is any collinearity that may be between the independet variables. Collinearity could be said that there is an issue of two or more independet variabls are very hgihly correlated with each other as well as the coefficients having large standard erros as a consequence with also smaller t statistics. When two or more independet variables correlate very highly that means it will not make the forcast better in fact it will make it less accurate as a result. So the most preferable action to take when this happen would be to get
Which is relationship between a variable over various time intervals. The covariance error does not equal to zero as well. Visual inspection could be done with the 4-1 plot. We can use the Durbin Watson test to detect it, as well as LM test. If the variance is not equal to 1 then we will have serial correlation. In this case we won’t even be able to use the F-test and T-test because it will be unreliable and result will be inefficient. But getting a better data and variables will be helpful. For my company which is Dick’s Sporting good some of the data that I wanted were hard to find so I used the best one I can find that is available. A potential fix for temporary solution might be by adding lag but we still need to come up with a better one.
The third problem is Heteroscedasticity, this issue can be detected by visual inspection using the graph 4-1 plot. Another test we can use for Heteroscedasticity is the KB test. It can couse the model to have the smallest variance and that will be very inefficient. Possible fixes are to get better data and ignore if it’s not significant. Converting to logarithms is also helpful to make it