Number of steps6560555045406062640000068 7066Height (in.)7274 16. More strides Refer to Exercise 12. The equationof the regression line for predicting y = number ofsteps required to walk the length of a school hall-way from x = the height of a student (in inches) isŷ=113.6-0.921x.(a) One student who was 67 inches tall took 46 stepsto walk the length of the hallway. Calculate theresidual for this student.(b) Interpret the residual for this student.17Interpreting Old Faithful Refer to Exercise 9 The

Answered: Image /qna-images/answer/288b682e-8eb7-483e-86f8-c479949a41a8.jpg

Answered: 16. More strides Refer to Exercise 12.…

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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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+ba=-0.759b=20.094r2=0.573049r=-0.757 Use this to predict the number of situps a person who watches 7 hours of TV can do (to one decimal place)
The linear regression equation for predicting systolic blood pressure from age is as follows: y = 42 + 2.5x . Find the residual for a person who is 21 years of age with a systolic blood pressure of 91.9.
10) A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)
The age and height (in cm) of 400 adult women from Bolivia were measured. A researcher wants to know if age has any effect on height. A linear regression is carried out in Minitab and the following output obtained. Coefficients Term Constant Age (a) Write down the regression model. (b) Interpret the regression coefficient for the fitted model. (c) Use the output from Minitab to explain if the age of a participant affects their height. Percent (d) The normal probability plot of the residuals from this regression model is given below. Do the assumptions of the regression model seem reasonable? Justify your answer. 99.9 8 28 22299229 88 Coef SE Coef 152.94 7.69 0.022 0.231 01 -100 T-Value P-Value VIF 19.90 0.000 0.10 0.924 1.00 -50 Normal Probability Plot (response is Height) 0 Residual 50 ***** 100 150
3- Find the equation of the regression line and find the y_ value for the specified x value. Calories and Cholesterol Calories and cholesterol are as follows: Calories X 390 535 720 300 430 500 440 Cholesterol y 43 45 80 50 55 52 60 Find y-hat when x = 600 calories. LO LO
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last 7 years. Construct and interpret a 98% prediction interval for the amount of oil imported by the country and the amount of oil produced by the country is 5,483 thousand barrels per day. The equation of the regression line is y = -1.194x + 16,234.906
You run a linear regression to predict BMI (body mass index) from average minutes of physical activity per day. You calculate the slope of the regression to be -.054. Interpret the slope in terms of the independent and dependent variables.
Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable. Regression Statistics Multiple R 0.963 R Square Adjusted R Square Standard Error Observations 20 ANOVA df SS MS F Significance F Regression 24686354.49 6171589 47.8 2.3289E-08 Residual 129243 Total 26625000…
The equation used to predict college GPA (range 0-4.0) is y = 0.17 +0.51x, +0.002x,, where x, is high school GPA (range 0-4.0) and x, is college board score (range 200-800). Use the multiple regression equation to predict college GPA for a high school GPA of 3.8 and a college board score of 500. The predicted college GPA for a high school GPA of 3.8 and a college board score of 500 is (Round to the nearest tenth as needed.)
A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-1.364 b=32.282 r2=0.398161 r=-0.631 Use this to predict the number of situps a person who watches 4 hour(s) of TV can do, and please round your answer to a whole number.
The regression line for the given data is = 6.91x + 46.26. Determine the residual of a data point for which x = 4 and y = 75.
Select all the charts that violate the conditions for linear regression. These are residual plots so the red line represents the average residual, zero. 미 Od (a) (d) 매 Get help: Video (b) (e) (c) (1)

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