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# Correlation and Regression Analysis and Descriptive Models

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1. How can correlation and regression analysis be used to make strategic decisions in a dynamic competitive business environment filled with risk and uncertainty (Consider the relations, either descriptive or predictive that can be accomplished with correlation regression analysis)?

2. What is the difference between a descriptive model that is developed using multiple regression analysis with time-series data and a model that is considered predictive?

3. How can multiple regression and time-series data be employed in financial forecasting by a company? Research if such forecasting models are being used within your own company, or one with which familiar. Are they (Please provide a 1 or 2-sentence response about what found).

4. How can one avoid misusing time-series analysis and multiple regression analysis within a business setting? If one does not know for certain that the data fits a normal distribution, is there a problem?

5. Why is the use of ranking of data and results of particular use in the study of consumer behavior? Once the ranking is determined, can the ranking and a specific behavior possibly be linked through multiple regression analysis?

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#### Solution Preview

1. How can correlation and regression analysis be used to make strategic decisions in a dynamic competitive business environment filled with risk and uncertainty (Consider the relations, either descriptive or predictive that can be accomplished with correlation regression analysis)?

The Regression Analysis is the part of Statistics that analyzes the relationship between quantitative variables. It helps predict the reaction of a variable when a related variable varies. The objective here is to determine how the predicted or dependent variable y (the variable to be estimated) reacts to the variations of the predicator or independent variables.
The objective of Regression analysis is to build a mathematical model that will help make accurate predictions about the impact of variable variations.
It is obvious that in most cases there are more than one independent variables that can cause the variations of a dependent variable.
When building a regression model, if more than one independent variable is being considered, we call it a multiple regression analysis, if only one independent variable is being considered, the analysis is a simple linear regression.
In our quest for that model, we will start with the techniques that enable us to find the relatedness between two variables. When building a regression model, if more than one independent variable is being considered, we call it a multiple regression analysis, if only one independent variable is being considered, the analysis is a simple linear regression.

Example: there is more than one factor that can explain the changes in the volume of cars sold by a given carmaker. Among other factors, we can name the price of the cars, the gas mileage, the warranty, the comfort, the reliability, the population growth, the competing companies, and so on. But the importance of all those factors in the variation of the dependent variable is disproportional. So in some cases, it is more beneficial to concentrate on one factor versus analyzing all the competing factors.

A good and reliable business decision making is always founded on a clear knowledge on how a change in one variable can affect all the other variables that are in one way or another associated to it.
How will commercial banks react to a change in the interest rate by the Federal Reserve?
How does that change affect our mortgages?
How does an increase in the price of gas affect the volume of cars sold in the economy?
How do changes in the prices of a given input affect the cost of the output?

Thus Bank or financial institution can use it to determine the impact of interest rate on
the mortgages. It can also be used to formulate strategy by knowing the effect of one variable on other.

Correlation, also called correlation ...

#### Solution Summary

The solution describes correlation and regression analysis as well as the application of the descriptive model.

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