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# Correlation Matrix and Multiple Regression Output

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I am having a difficult time with this question.

Variables Defined:
Lottery: How many times a sampled customer has purchased California lottery tickets in the past two months
Education: How many years of education the sampled customer has completed
Age: The age of the sampled customer in years
Children: How many children there are in the sampled customer's immediate family
Income: The annual income of the sampled customer (measured in \$thousands of dollars)
Not CA?: A one was assigned if the customer lived in Nevada or other state; a zero was assigned if the customer lived in California

I. Develop a correlation matrix for all variables (10 points).

A. Which explanatory variables should be good predictors of the response variable in the regression model? Support your answer by citing the appropriate correlation output.
B. Is there evidence of potential multi-collinearity? Support your answer by citing the appropriate correlation output.

II. Develop the multiple regression output for the full model (40 points).

A. Does the full model need to be revised? Explain using the appropriate statistical tests and measures.
B. If the model needs revision, perform the necessary steps required to revise the model. Discuss, if necessary, improvements or changes seen when comparing the full model to any revised model.
C. Clearly interpret the coefficients for your final model.
D. Would you use your final model for prediction description? Briefly, why or why not?

PROVIDE THE MEGASTAT OUTPUT IN YOUR DOCUMENT.

The data is included.

https://brainmass.com/statistics/regression-analysis/correlation-matrix-multiple-regression-output-540694

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#### Solution Summary

Correlation Matrix and Multiple Regression Output are examined.

\$2.19

## Correlation Matrix: Interpret Data for JAX, DFW, & LAX Models

See attached files.

Provide a correlation matrix.

Use the data to obtain the 'best' multiple regression model.

Can you please provide a short write-up in Word that interprets your output discussing

- which independent variables are most strongly related to price,
- which are least strongly related to price,
- if the directions of the correlations make sense,
- if there appears to be any multicollinearity,