Please assist to solve quesions #1-#6 from the attached image.Need to use matrix to solve Ordinary Least Squares (OLS) RegressionThanks! Find the linear regression equation explaining dog's life span (y) as a linear function of weight(w) and blood pressure (p):ý = bo+bưu + b2p.Also compute the inferential statistics discussed in lecture.BreedWeight w (lb)Blood pressure p (mmHg)Life span y (years)Chihuahua Beagle Border Collie4237712815131. Set up the data matrix X = #1and construct X in a way such that B2. Compute (XTX)-¹ = #2(1)3714612Afghan Hound5114911Arrange the components of the vectors asChihuahuaBeagleBorder CollieAfghan Hound[bo] is obtained in accordance with Eq. (1).3. Using the normal equation, compute the regression coefficients boand b₂ = #5= #3 b₁ = #44. Write down the regression equation, and predict the life span of a dog whose weight is30lb and average blood pressure is 128 mmHg. Expected life span = #6 years.

Given Information: Consider the given data: Weight (x1) Blood Pressure (x2) Life span (y) 4 77…

Find the linear regression equation explaining dog's life span (y) as a linear function of weight (w) and blood pressure (p): ý = bo + b1 + b2p. Also compute the inferential statistics discussed in lecture. Breed Weight w (lb) Blood pressure p (mmHg) Life span y (years) 1. Set up the data matrix X = #1 2. Compute (XTX)-1 Chihuahua Beagle Border Collie 4 23 77 128 15 13 = (1) 37 146 12 #2 Afghan Hound 51 149 11 and construct X in a way such that B = [bo] is obtained in accordance with Eq. (1). Arrange the components of the vectors as 3. Using the normal equation, compute the regression coefficients bo = and b₂ = #5 Chihuahua Beagle Border Collie Afghan Hound] = #3 b₁ #4 4. Write down the regression equation, and predict the life span of a dog whose weight is 30lb and average blood pressure is 128 mmHg. Expected life span #6 years.

Find the linear regression equation explaining dog's life span (y) as a linear function of weight (w) and blood pressure (p): ý = bo + b1 + b2p. Also compute the inferential statistics discussed in lecture. Breed Weight w (lb) Blood pressure p (mmHg) Life span y (years) 1. Set up the data matrix X = #1 2. Compute (XTX)-1 Chihuahua Beagle Border Collie 4 23 77 128 15 13 = (1) 37 146 12 #2 Afghan Hound 51 149 11 and construct X in a way such that B = [bo] is obtained in accordance with Eq. (1). Arrange the components of the vectors as 3. Using the normal equation, compute the regression coefficients bo = and b₂ = #5 Chihuahua Beagle Border Collie Afghan Hound] = #3 b₁ #4 4. Write down the regression equation, and predict the life span of a dog whose weight is 30lb and average blood pressure is 128 mmHg. Expected life span #6 years.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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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?