13.2.4VO2-max Aerobic Fitness MeasurementsThe data set given in DS 13.2.4 extends the data set used in Problem 12.2.6 to include an individual'sheart rate at rest, percentage body fat, and weight, together with the variables age and VO2-maxconsidered before. Fit a multiple linear regression model to assess whether the new input variablesheart rate at rest, percentage body fat, and weight, together with age, can be used to provide animproved model for VO2-max. What model would you recommend? (This problem is continued inProblem 13.4.3.) 1234567891011121314151617181920212223VO2-max234529554842323334524035452642352938BHeart Rate at Rest6259826160587670687667665057615160636657Age5932456132283636513144734740436840D% Body Fat Weight (lbs)26182181752220010169191932217028193232710182929133824428192516218228128167194219215246171212167162239FGH

As per the question, we are asked to fit a multiple linear regression model to assess whether the…

Answered: The data set given in DS 13.2.4 extends…

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

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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?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
This is only one question
Ch 13. 7: Refer to the Lincolnville School District bus data. First, add a variable to change the type of engine (diesel or gasoline) to a qualitative variable. If the engine type is diesel, then set the qualitative variable to 0. If the engine type is gasoline, then set the qualitative variable to 1. Develop a regression equation using statistical software with maintenance cost as the dependent variable and age, odometer miles, miles since last maintenance, and engine type as the independent variables.
A researcher interested in explaining the income levels of St. Lucian workers, developed the following multiple linear regression model: INC = a + BEDU + YEXP+ ST RN where INC = monthly income, EDU = the number of years of formal education, EXP = the number of years of workforce experience and TRN = the number of weeks spent in job training. A sample of 35 workers was processed using MINITAB and the following is an extract of the output obtained: %3D PredictorCoef StDev t-ratio Constant 3315.7 1371.6 2.41 EDU 116.53 26.07 4.47 EXP 282.96 222.8 1.27 TRN -318.12125.74-2.53 S= 2.05 R-sq = 77.3% R-sq(adj) = 75.2% The coefficient y is significant at the 5% level of significance. Select one: O True False
1. What is the most important predictor in terms of contribution to the dependent variable? 2. Which of the following is not a significant predictor at 5%? 3. Which of the following is the best predictor?
Suppose a study wants to predict the market price of a certain species of turtle (Y) based on the following independent variables indicated in the table. Based from the table, what is the equation of the multiple linear regression? (Round off up to two decimal places. Market Price = 0.07 - 0.40*weight + 1.51*length + 1.41*width + 0.80*age Market Price = - 0.40*weight + 1.51*length + 1.41*width + 0.80*age Market Price = 0.07 + 0.40*weight + 1.51*length + 1.41*width + 0.80*age Market Price = 0.07 - 0.40 + weight + 1.51 + length + 1.41 + width + 0.80 + age
If a scatterplot is created in excel, and a line of regression is fit along with a derived functional form, what does it mean to describe and interpret them? What conclusions would be made about relationships between two recorded variables?
The following is the result of the multiple linear regression analysis in STATISTICA, where the response Y = lung capacity of a person, xage = age of the person in years, xheight = height of the person in inches, = a categorical variable with 2 levels (0 = non- X smoke smoker, 1 = smoker), and xCaesarean = a categorical variable with 2 levels (0 = normal delivery, 1 = %3D %3D Caesarean-section delivery). b* Std.Err. Std.Err. t(720) p-value N=725 Intercept Age Height Smoke Caesarean of b 0.467772 0.017626 of b* -11.8001 0.1372 0.2790 -0.6407 -25.2263 7.7846 28.6552 -5.0142 0.000000 0.000000 0.000000 0.206427 0.026517 0.026340 0.754765 -0.074205 -0.033054 0.009735 0. 127774 0.092146 0.000001 0.022851 0.014799 0.014492 -0.2102 -2.2808 What is the predicted lung capacity of an 14-year old non-smoker whose height is 71 inches born by normal delivery? (final answer to 4 decimal places)
Suppose researchers are interested in exploring the factors which affect depression for Australian adults. The researchers recruited a sample of 99 Australian adults and collected data on several variables which may influence depression. Note that here depression is represented by a score, with higher values representing higher levels of depression. The variables for this study are listed below: Age Gender (0 = female, 1 = male) Stress level Anxiety level Depression Prior to running their analysis (multiple regression), the researchers would like to check the regression assumptions for the data. Which of the assumptions below is not an assumption for multiple regression? A) Sphericity B) Linearity and Additivity C) Equal variances D) Normality E) Metric scales
Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married: 1=married, 0=otherwise), a dummy variable for head of household (Head: 1=yes, 0=no) and a dummy variable for management position (Manager: 1=yes, 0=no). We shall call this Model 1. The coefficient of partial determination (R2Yj.(All variables except j)) of each of the six predictors are, respectively, 0.2807, 0.0386, 0.0317, 0.0141, 0.0958, and 0.1201. Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given. Refer to model 1. Which of the following is the correct null hypothesis to test…
D& T LTD marketing team needed more information about the effectiveness of their 3 main mode ofadvertising. To determine which type is the most effective, the manager collected one week’s datafrom 25 randomly selected stores. For each store, the following variables were recorded:Weekly gross salesWeekly expenditure on direct mailing (Direct)Weekly expenditure on newspaper advertising (Newspaper)Weekly expenditure on television commercials (Television)Following is the regression output based on the above-mentioned data. "image" Required: a. Complete the missing entries from A to H in this outputb. Assess the independent variables significance at 5% levelc. Does the model is significant at 5% level?

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