What is the purpose of using regression analysis? How may it

Customer Question

What is the purpose of using regression analysis? How may it be used to formulate strategies? Provide examples related to strategy formulation.

How is regression analysis used in forecasting? Provide examples.

What is the purpose of using correlation analysis? How may correlation analysis be used in business decisions or in relation to strategy formultion and implementation? How may orrelation analysis be misused to explain a cause-and effect relationship?

What is the purpose of using regression analysis? How may it be used to formulate strategies? Provide examples related to strategy formulation.

Regresson analysis is used to predict a response variable based on one or more explanatory variables. It can be used to determine which kinds of variables that we have information on can be used to forecast or predict variables that we don't have information on.

For example, I can use general economic indicators like the Dow Jones Industrial average and the consumer price index to forecast demand for my product or service. By using these two input variables, I can predict demand, and also find out what percentage of the variation in demand is due to these two variables.

How is regression analysis used in forecasting? Provide examples.

Regression analysis can be used in forecasting by using time as the independent variable. For example, let's assume that my revenue increases linearly over time. I can use the year as the x variable, and revenue as the y variable. Then I can predict the revenue in a particular year, as long as I don't go too far from the present (or last year of analysis). I can also forecast other things like price of commodities or number of available suppliers.

What is the purpose of using correlation analysis? How may correlation analysis be used in business decisions or in rel

A correlation shows an association between two variables, but not causality. It can be used to show a relationship where there's no claim about one causing the other. For example, I can show a correlation between the price of gas and the number of cars licensed nationwide, without claiming any particular causal effect between the two. Correlation can be used to show associations; for example, if I know that the price of houses is correlated with the number of baths, that data is interesting because I can advise my clients that high-end buyers expect more baths. However, I shouldn't claim a causal connection. It's possible that more baths drive the house prices up, but it's also possible that people who can afford to pay more can also afford to put in more bathrooms. Any time correlation on its own is used to explain cause-and-effect, it's a misuse. To show cause-and-effect, I should use regression analysis, but in addition, the cause-and-effect relationship must be reasonable. For example, I know that parental income is correlated with children's school achievement, but it's probably not reasonable to assume that children's achievement causes parents to become high earners. As to whether or not high parental income causes higher achievement: that remains to be seen. It's possible that both are caused by a third, "lurking" variable that isn't part of the explanatory model.

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