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# Use of Simple-Linear and Multiple Regression Analysis

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1. Can you think of an example where analysis of simple-linear and multiple regression analysis can be used? How is regression analysis being used in the financial industry, or how should it be used to formulate strategies?
2. What are examples in which regression analysis is used for forecasting?
3. What is correlation analysis? How can correlation analysis be used in a business decision or examples specifically related to strategy formulation and implementation?
4. What are some primary and secondary sources of data that may be used in correlation and regression analysis?
5. What is the difference between correlation and causation? Give examples

https://brainmass.com/statistics/regression-analysis/use-of-simple-linear-and-multiple-regression-analysis-52429

#### Solution Preview

Q- Can you think of an example where analysis of simple-linear and multiple regression analysis can be used? How is regression analysis being used in the financial industry, or how should it be used to formulate strategies?

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 ...

#### Solution Summary

This explains the use of simple-linear and multiple regression analysis and the difference between correlation and causation in approximately 1400 words.

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