Simple Regression Analysis
The data with which various operations are carried out are the two set of numerical data derived from stock indexes. The first category of data is the stock price of Coca-cola (COKE), and indexes of Dow Jones Industrial Averages. The task for this project is to determine whether it is possible to determine the stock price of COKE, provided that we know the values of DJIA.
The answer to the main question of the module (can you predict what the price of your company’s stock will do from the DJIA?) is undoubtedly “YES!” The only problem with this answer is that, as in probability theory, we can never know for sure the exact values of COKE that would occur if we try to predict.
Using the simple regression analysis, we only can determine properties of this particular linear regression model, and based on these properties we can make different assertions, taking into account the error that we make every time when predicting. Since the real correlation between the two sets of data is almost perfect (r=0.994; rounded), the graphical plot of this model falls on almost a straight line. This value of the coefficient of correlation means that the values are very strongly correlated, and the positive sign means that greater values of independent variable x (the DJIA indexes in our case) correspond to the greater values of y (stock prices of Coca-cola). Thus, with the help of linear regression model, we can predict the approximate values of COKE with the help of DJIA.
The predicted values of y (COKE stocks), which the machine would generate, would fall on the LS line mentioned above. This means that for every value of DJIA that is equal to some k, the values of COKE stocks would always equal some l=yk. However, this is far from being true, because, for example, all apples with a certain diameter do not weigh exactly the same. In reality, the values of the dependent variable would slightly differ from one another even if the value of the independent variable x stays the same. 
As for the future of the stock, it is impossible to predict precisely the stock prices. Too many indicators influence the price, including (not limited to) public relations of the company, their sales, multiple financial indicators (quick ratio, debt-to-equity, etc), price dynamics and many more. If the price always goes down, the company is more likely to fail. But if the price always goes up, it is more likely to prosper.
With the help of this <b>simple linear regression model</b>, statisticians can easily determine the values of stock prices of Coca-cola, given the values of DJIA indexes.