# Report Properties
Owner: asevilla
Created: Dec 2, 2014
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Extra Credit Technology Assignment, Aida Sevilla

The data collected is from 500 randomly selected undergraduate college student. The data collected included Gender, Class, Hours, Work, Loans, and Credit Card Debt. The cloumns used in this multiple regression model are Loans, Hours, Work, and Credit Card Debt.

Data set 1. Multiple Regression data   [Info]

The data above was used to create a multiple regression in order to predict a students education loan debt, using the response variables of Hours Enrolled, Hours Worked, and the Credit Card Debt amount of the students.

Result 1: Multiple Linear Regression   [Info]
Multiple linear regression results:
Dependent Variable: Loans
Independent Variable(s): Hours, Work, CC Debt

Parameter estimates:
 Variable Estimate Std. Err. Tstat P-value Intercept -2178.9375 1344.9358 -1.6201051 0.1058 Hours 206.29686 79.88948 2.5822783 0.0101 Work 221.02538 25.609632 8.630556 <0.0001 CC Debt 0.75576794 0.10423718 7.2504644 <0.0001

Analysis of variance table for multiple regression model:
 Source DF SS MS F-stat P-value Model 3 3.76480384E9 1.25493466E9 45.66489 <0.0001 Error 496 1.36307692E10 2.748139E7 Total 499 1.73955727E10

Summary of fit:
Root MSE: 5242.2695
R-squared: 0.2164

Regression model equation : Loans= -2179+206.3(Hours)+0.76(Credit Card Debt)+221(Work)

The significance of the overall model is suitable because the p-values is <0.0001 and less than the level of significance of 0.05

The significance of each predictor variable: Hours p-value = 0.0101 which is less than the level of significance of .05

CC debt p-value= <.0001 which is significantly less than the level of significance of 0.05

Work(hours) p-value = <.0001 which again is less than the level of significance of 0.05

The Adjusted R-squared = 0.2117 while the original R-squared is 0.2164

The adjusted r-square is calculated and used in this case. This is in order to account for the effect that happens when more variables are added to the multiple regression model. The model predits student loan debt, but it only account for about 21.2% of significance to the loan debt each student has. There could be a more accurate model made using other more significant variables. We can rject the null hypothesis since the model P-value for the predictor variables are less than .05.