Markets and Their Analysis

Our results from performing the Correlation helped distinguish and eliminate variables that did not have such a strong relation that others did with Net Income.
T-tests indicated the directional relation of each individual variable according to the sorted Net Income.
From the above analysis, we can conclude that the independent variables have close relation with Net Income, whether positive or negative. Hence it has proven that all of our alternative hypotheses to be true. Our narrowing down to the final six variables proved that most of the variations in Net Income are influenced by the variations in these independent variables.
It also indicated that, there is a 95% probability that the Net Income will lie within the range of 22.245 + (1.96 * 292.767) = [596.068; 551.578] based on the Standard Error of Estimate (SEE).
Using a 99% confidence level probability we determined that the Net Income will lie within the range of 22.245 + (2.576 * 292.767) = [776.413; 731.923] based on the Standard Error of Estimate (SEE).
The scatter diagram above shows the correlation between Net Income and the independent variables.
Conclusion:
Using the financial tools available to us we arrived at conclude that our alternative hypothesis is accurate. After a thorough analysis of the data we found significant correlation between Net Income and the independent variables. Alternatively we conclude that ROA, ROE, Operating Income, Core Earnings, Cash Flow, and Gross Profit are the variables most likely to drive the Net Incomes of companies in the US.
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