The multiple regression equation for predicting Y by X1 through X6. A total of n=22 observations were used in the analysis.- Y = Sales per month- X₁ = Amount spent on printed ads- X₂ = Amount spent on text messages- X₃ = Amount spent on radio ads- X₄ = Amount spent on customer service support- X₅ = Amount spent on Internet ads- X₆ = Amount spent on incentives for sales representativesThe regression equation is:\[ \hat{y} = 22.3 + 0.23X_1 + 0.45X_2 + 0.477X_3 + 4.8X_4 + 1.458X_5 + 411.1X_6 \]R-square = 0.57What is the adjusted R² of this model? Round your answer to three decimal places.

The question is about regression.Given :Total no. of observations ( n ) = 22R2 = 0.57Regression…

Answered: The multiple regression equation for…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 6PPS

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
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