4. The following regression was performed to analyze a relationship between cred

Question

4. The following regression was performed to analyze a relationship between credit score and late payments. Use the regression output below to answer the following: a) Which is the dependent variable? Explain your reasoning. b) What is the forecast formula? c) Comment on the goodness of fit of this formula; refer to the R Square and P-value in your explanation. Use your formula to forecast expected late payments when the credit score is 400. d) MMARY OUTPUT Regression Statistics 0.867721831 0.752941176 Multiple R Adjusted R Square 0.629411765 Standard Error Observations 1.449137675 ANOVA F Significance F 12.8 6.095238 0.132278169 MS 12.8 Regression Residual Total 17 intercept xcrecit score Coetic ents -Standard Error-Stat-p.value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 23.7 4.594017849 5.158883 0.035581 3.93353656 43.4664634 3.93353656 43.4664634 -0.04 0.016201852-2.46885 0.132278 -0.109710942 0.02971094 -0.1097109 0.029710942

Explanation / Answer

a) Here responce variabler is late payments.

Becasue here we want predict late payments using credit score .

b) what is forcast formuala is given by

late payments= 23.7 -0.04 *Credit Score

is the forcast formuala

c) model explained 75% variation which very good and model is significant .

that why the model is very good .

d) predict the score

late payments= 23.7 -0.04 *Credit Score

= 23.7 -0.04 *200

=15.7

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