Regressional analysis question

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In summary, the conversation discusses the determination of the adjusted coefficient of determination, also known as the R2 coefficient, for a dataset that includes sales and population data from 12 stores within a one mile radius of a supermarket. The conversation also mentions the need to determine the number of observations and independent variables in order to calculate the coefficient of determination.
  • #1
adeel
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Ive been posting many questions, hopefully the alst for awhile:

You have collected data on sales and population within a one mile radius on 12 stores of a supermarket. You determined that the adjusted coefficient of determination is 93.85%. Determine the coefficient of determination.


I think there isn't enough info, but I guess I could be missing something
 
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  • #2
I am going to assume "coefficient of determination" is the R2 coefficient. See http://www.csus.edu/indiv/j/jensena/mgmt105/adjustr2.htm . You have to determine what your n (number of observations) and k (number of Independent Variables) are. The problem you posted does not give you these.
 
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  • #3
n = 12 (number of stores)

k = 1---> one independent variable, one dependent (I can asume single regression)

also, its [tex]r^2[/tex] because its a sample
 
  • #4
You're home free then, are you not?
 

What is regression analysis?

Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is used to predict the value of the dependent variable based on the values of the independent variables.

What is the purpose of regression analysis?

The purpose of regression analysis is to identify and quantify the relationship between a dependent variable and one or more independent variables. It can also be used to make predictions and understand the impact of different variables on the outcome.

What types of data are suitable for regression analysis?

Regression analysis is suitable for both continuous and categorical data. For continuous data, linear regression is commonly used, while for categorical data, logistic regression is more appropriate.

What are the assumptions of regression analysis?

The main assumptions of regression analysis include linearity, normality, homoscedasticity, and independence of errors. These assumptions should be checked before conducting a regression analysis to ensure the accuracy and validity of the results.

How do you interpret the results of regression analysis?

The results of regression analysis can be interpreted by looking at the coefficient estimates, p-values, and R-squared value. The coefficient estimates indicate the direction and strength of the relationship between the variables, the p-values determine the statistical significance of the results, and the R-squared value represents the goodness of fit of the model.

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