Question

Create the printout necessary for conducting a SLR analysis of your project data. Use y=price as your dependent variable and x=mileage/size as your independent variable. Copy and paste the printout here:

Least Squares Linear Regression of Asking Predictor Variables               Coefficient            Std Error                    T              P Constant                 22790.9               1314.55                17.34     0.0000 Mileage              -0.09109               0.03153                -2.89       0.0051 R²                              0.1026               Mean Square Error (MSE) 1.102E+07 Adjusted R²              0.0903               Standard Deviation             3319.84 AICc                        1220.5 PRESS                   8.47E+08

PART II – Model Interpretations - Answer the following questions about your regression model.

1. Is the interpretation of your y-intercept estimate practical for your model? Why or why not? (4 points)

2. Interpret the slope interpretation in the words of the problem. (4 points)

3. Interpret the standard deviation of your model in the words of the problem. (4 points)

4. Interpret the Coefficient of Determination (R-squared) in the words of the problem.

(4 points)

5. Give the following information for testing whether your independent variable is a useful linear predictor of your dependent variable. Make sure you conduct the appropriate one-tailed test for your variables. (6 points)

Test: H_o:                                           Test Statistic: ____________

            H_a:                                           P-value: _____________

6. State the appropriate conclusion for your test in the words of the problem. You choose the α to test at. (4 points)

Answer 1

Interpretation of y-intercept estimate isn't practical for the model because there will be no price if the mileage per size is zero.

Slope Interpretation: If the mileage per size is increase or decrease by one unit then price will decrease or increase by 0.09109 units respectively.

Standard deviation Interpretation: the average distance that the observed values fall from the regression line is 3319.84 units of price.

R- Square Interpretation: The model explain the 10.26% of the variability of the response data around its mean.

Ho: Test Statistic = -2.89

Ha: P-value = 0.0051

Since the p-value is 0.0051, which is less than 0.01. we have sufficient evidence to reject the null hypothesis at 1% level of significance. so, Ho is rejected.

Hence There is negative relationship between the price and mileage/size.