After compiling data and studying the relationship between the price of oil and the price of solar stocks using ANOVA, our East Coast Team has refocused on the same task utilizing regression analysis. Regression analysis is the process by which an equation is defined that signifies the linear relationship between two variables. By using regression analysis we will better be able to gauge the effects that each variable has on our model, allowing us to better determine which variables have strong positive correlations and which are insignificant to the model itself. As in any industry, being able to determine which factors have the strongest correlations with their models are important in reaching any decision as to what direction or steps that an organization needs to take in order to succeed within their particular industry. Our goal at this point is to research the relationship between the price of oil and the dollar value of solar stocks.
             Using the time series method to evaluate oil and stock trends for the past decade will not only help to detect trends but will also help to forecast future ones as well; the same could also be done with the stock prices of solar energy companies. Using the time series method to analyze both models, the team will be in a better position to determine how they each reacted when the other experienced various trends such as prosperity, recession, or recovery. Using the time series method also enables us to remove both seasonal and irregular variations that can at times mislead researchers as to the information obtained. Similarly, by performing a regression analysis of the data we will be able to determine which variables are important to the regression equation and which have little impact while establishing the correlation between both oil and solar energy stock prices.
             However, before proceeding with these tests certain conditions have to be met, some of which are listed below:
             · For every ...