Regression and Correlation are two of the most often used and abused tools in re
ID: 3155914 • Letter: R
Question
Regression and Correlation are two of the most often used and abused tools in research. People are quick to jump to conclusions that if a relationship exists between two variables, then one must cause (causation) the other. There are many reasons why two variables can be related without causality. Please respond to the following: Comparing the amount of money people spend and the amount people save, your analysis revealed an R-squared=0.97. Should you use this for predictive purposes and why? Comparing the number of cops on our streets and the number of reported crimes, your analysis revealed an R-squared=0.40. Should you use this for predictive purposes and why? How could this apply in your profession of working in accoutning?
Explanation / Answer
Interpretation of R Squared:
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Case I : Comparing the amount of moeny people spend and save, we get R squared = 0.97
Inference: A R squared Value of 0.97 shows strong predictability of the model. Hence it can be used for predictive purposes.
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Case II: Comparing the number of cops on streets and number of reported crimes, we get R squared =0.40
Inference: A R sqaured of 0.40 , shows weak predictability fo the model. Hence it CANNOT be used for predictive purposes.
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Application in profession:
The R squared statistical tool is used in forecasting the financial picture of the company and helps in understanding the security's performance with respect to the market index. As it helps in summarizing the relationship between events, it helps in predicting the future financial prospects , expenses and helps in setting up of budgets accordingly.