Need help with regression modeling in Minitab?

In summary, the conversation discusses a project on regression that aims to show the impact of unemployment and interest rates on college enrollment, with a focus on gender. The individual is using Excel and Minitab for the analysis but is unsure about incorporating dummy variables for gender. They are seeking help with the regression modeling in Minitab and have attached an Excel file with the data. The recommended approach is to enter the data into Minitab and run the regression analysis, selecting "Enrollment" as the response variable and "Unemployment" and "Interest Rates" as the predictor variables. A dummy variable for gender can be added by selecting "Options > Categorical Variables". The results of the analysis will be displayed in the Session window.
  • #1
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Hi!
I am trying to do a project under regression, trying to show how the enrollment (male and female) in college is affected by unemployment (of male and female), Interest rates.
I am using Excel and have also tried Minitab but I am unsure how do I factor in the dummy variables (1 or 0) for the gender part.
Can someone help with the regression modelling in Minitab?
I have attached an excel file with the data.
 

Attachments

  • Reg data.xlsx
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  • #2
For this project, you can use Minitab to run the regression analysis. First, you will need to enter your data into Minitab. This can be done by selecting "Data > Enter Data" and selecting the columns with your data. After entering the data, you can then run the regression analysis. To do this, select "Stat > Regression > Regression". In the dialog box that appears, select "Enrollment" as the response variable and "Unemployment" and "Interest Rates" as the predictor variables.Next, you will need to add a dummy variable for the gender variable. To do this, select "Options > Categorical Variables" in the dialog box. Here, you can select the column containing the gender variable and click "Add". This will add a dummy variable for the gender variable in the model. Finally, click "OK" and the regression analysis will be performed. The results of the regression analysis will be displayed in the Session window.I hope this helps!
 

1. What is regression modeling and why is it important?

Regression modeling is a statistical technique used to analyze the relationship between a dependent variable and one or more independent variables. It helps to understand how changes in one variable affect the other. It is important because it can be used to make predictions and identify patterns in data, which can aid in decision making and problem-solving.

2. What are the different types of regression models?

There are several types of regression models, including linear regression, logistic regression, polynomial regression, and multiple regression. Each type of model is suited for different types of data and can provide insights into different relationships between variables.

3. How do I know which type of regression model to use?

The type of regression model to use depends on the type of data you are working with and the research question you are trying to answer. Linear regression is used when the relationship between the variables is linear, while logistic regression is used for binary outcomes. It is important to understand the strengths and limitations of each type of model before choosing one for your analysis.

4. What are the assumptions of regression modeling?

There are several assumptions that must be met for regression modeling to be accurate, including linearity, normality, homoscedasticity, and independence of errors. Violation of these assumptions can lead to biased results and inaccurate predictions. It is important to check these assumptions before interpreting the results of a regression model.

5. How can I evaluate the performance of a regression model?

There are several metrics used to evaluate the performance of a regression model, including the coefficient of determination (R-squared), mean squared error (MSE), and root mean squared error (RMSE). These metrics can help determine how well the model fits the data and how accurately it can make predictions. It is important to consider multiple metrics and compare them to the performance of other models to determine the best fit for your data.

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