Reference no: EM131187687
Please answer question 1-4 and feedback for question 5. Also at least 100 word responds for each question.
1. Explain if correlation causes causality.
2. Yes, we generally prefer a multiple regression model in any analysis. This is because the outcome of a dependent variable is generally impacted by a number of explanatory (independent) variables. So, to be able to correctly predict the outcome, we have to build a regression model that includes all the relevant independent variables. The next issue is how do we build such a multiple regression model with relevant independent variables? For doing that we run separate simple regression variables with each independent variable we think is relevant. The simple regression model then helps us to determine which of the independent variables are able to explain the changes in the dependent variable. The ones that have significant explanatory power are then combined to develop the multiple regression model.
3. How does a multiple regression differ from a simple linear regression and why the use of a multiple regression is generally preferred over a simple linear regression?
4. What multiple regression does is correctly explained. But the example mentioned is still a scenario where we are using a simple regression model as gender is the only explanatory variable with two outcomes male and female. That does not make it a multiple regression. What we have to include in a multiple regression model are multiple explanatory variables, So think how we can add another explanatory variable in the example that can help in developing a multiple regression model. I am sure that we can readily find one as test scores can depend on a number of other factors
5. You provided a great explanation of why past data is still relevant over time. You are correct that recorded data is important to have in the business world, so it can be reviewed and utilized for future decision making. Say you're a business owner trying to determine your slowest time of the year for staffing purposes. The more data you have to review, the better equip you are to make an educated decision. Suppose a small business owner wants to determine her slowest time of year so she can plan her sales or even determine store hours. The more information she has to review, the better she can decide what decision will provide the best course for the business.
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