1ee 19 20 21 22 23 24 25Price ars)QUESTION 6A medical rescarcher found a significant relationship among a person's age x, chloresterol level x2, sodium level of the blood x3, andsystolic blood pressure y. The regression equation is 97.3+0.700x+21&-299x3, Predict the systolic blood pressure of someone whois 31 years old and has a chloresterol level of 195 milligrares per deciliter and a sodium blood level of 142 milliequivalents per liter.O 171O-24O 128O 85,087QUESTION 7MacBook AirSODIIF3FSF6&4WRTYDHKV

The given regression equation is y' = 97.3+0.700x1 + 218x2 - 299x3

Answered: 1e 19 20 21 22 23 24 25 Price ars)…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Nine pairs of data yield a regression equation of y=0.93x + 19.4, with r= 0.967 and an average y value of 64.70. What is best predicted value for y when x =65? Is it 79.85, 25.74, 89.61, 57.82, 70.55, 96.70, 19.40, or 64.70?
The consumption function captures one of the key relationships in economics. It expresses consumption as a function of disposal income, where disposable income is income after taxes. The attached file "Regression-Dataset1" shows data of average US annual consumption (in $) and disposable income (in $) for the years 2000 to 2016. a)what is the Pearson correlation value between income and consumption? Select one: Ⓒa. 0.978 b. 0.109 c. None of the above d. 0.791
Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative?
Two variable are found to have a strong negative linear correlation. Pick the regression equation that best fits this scenario. y=0.82x−28 ˆy=0.32x−28 y= -0.82x+28 ˆy= -0.32x+28
A car lot wants to predict the # of car accessories they need to sell monthly based on the number of cars sold. The car lot owner randomly selects 12 months of data. The simple linear regression equation is ý = 14 - 2x. The coefficient of determination is 0.6364. What is the correlation coefficient to describe the relationship between these two variables? Multiple Choice
A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is Y = -0.800+ 0.060x₁ +0.900x₂ + e (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? hr (b) How would you interpret ₁ = 0.060, the coefficient of the predictor x₁? O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e.…
when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
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The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why? E Click the icon to view the table of regression equations. Choose the correct answer below. O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2. O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?. OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables. O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to…
Study conductes in patients with HIV. Primary outcome is CD4 coubt(measure stage of disease). Lower CD4=more advanced disease. Wqnt to find association between taking vitamins and supplements and CD4 count. Multiple regression analysis done relating CD4 to use of supplements (1=yes 0=no) and to duration of HIV in years. (# of yeara between diagnosis and study date) y=CD4 Count Y=501.41+12.67 supplements -30 23 duration of HIV. A. What is ezpected CD4 count for patients taking supplements who had HIv for 2.5 years? B. Expected CD4 count not takong supplements with HIv at study start date? C. Expected CD4 coubt for patients not taking supplements with HIV for 2.5 years? D. Uf compare 2 patients, 1 HIV for 5 years longer than other, whats expected difference in CD4 count?

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