he ideà is that you take it for the first time well before the deadline, review

ID: 2907439 • Letter: H

Question

he ideà is that you take it for the first time well before the deadline, review questions you were unsure about, then retake the quiz before the deadline ab: Complete the Technology Project on page 550. See also the paragraphs on Using Technology on pages 508 and 524 . Complete the Lab Quiz. Be prepared to answer the following questions: 1. Which of the other variables is the best predictor for the number of wins? 2. What is the corresponding regression equation? a Send to Binder Reflect in ePortfolio ) Download : Print : Activity Details You have viewed this topic Last Visited Jul 24, 2018 11:17 AM 25 AB

Explanation / Answer

Solution:-

a) Best Predictors:

for the given data , the Regression Model is,

Wins = B_0 + B_1 Losses + B_2 POW + B_3 RS + B_4 RA + B_5 D

where, B_0, B_1, B_2 , B_3, B_4, B_5 are the regression coefficients.

and POW - Proportion of Wins ,

RS - run Scored,

RA - run Allowed,

D - Run scored - Run Allowed

We have to check which predictors are the best predictors,

here , Ho : B_0 = B_1 = B_2 = B_3 = B_4 = B_5 =0

i.e . All variables are insignificant

V/s

H1 : at least one of the regression coefficient is not zero.

i.e . variables are significant.

Using the excel we run analysis :

Coefficients

Standard Error

t Stat

P-value

Intercept

109.076

31.2641

3.489

0.001815

losses

-0.7495

0.182072

-4.117

0.000367

Proportion of Wins

48.4133

29.8967

1.6195

0.11791

Run Scored

-0.003503

10.005598

-0.626

0.537192

Run Allowed

0.014491

0.006995

2.072

0.04877

RS-RA

from the table losses and Run Allowed are statistically significant.

because the p-value < 0.05

we reject null hypothesis at 5% level of significance.

and conclude that , the variables losses and run allowed B are statistically significant.

Therefor , Losses and Run Allowed are the best predictors for the model.

b) the regression equation :

using the estimated coefficents the fitted model is,

Wins = B_0 + B_1 Losses + B_4 RA

Wins = 109.076 - 0.7495 Losses + 0.014491 RA