Listed below are altitudes (thousands of feet) and outside air temperatures ("F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and(c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when makingpredictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet).32-53816222129-27Altitude31Temperature6040-41a. Find the explained variation.(Round to two decimal places as needed.)

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Answered: Listed below are alttudes (thousands of…

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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Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude Temperature 2 55 a. Find the explained variation. 8 40 10370.30 (Round to two decimal places as needed.) b. Find the unexplained variation. (Round to five decimal places as needed.) 13 25 20 - 3 28 - 26 31 41 34 - 53
The accompanying table lists systolic blood pressures (mm Hg) and diastolic blood pressures (mm Hg) of adult females. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for a systolic blood pressure of 118 mm Hg using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Systolic Diastolic 127 69 103 65 130 73 104 64 157 74 97 51 155 90 111 69 124 68 112 75 103 60 128 67
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Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 10 14 19 29 31 33 D Temperature 55 31 27 - 1 - 27 - 41 - 54 a. Find the explained variation. (Round to two decimal places as needed.) b. Find the unexplained variation. (Round to five decimal places as needed.) c. Find the indicated prediction interval. °F
Consider a regression that uses X = # of 60 pound bench press repetitions to predict Y = maximum bench press amount in pounds…..for the ith student among a sample of high school athletes. The estimated predicted sample regression is as follows Maxi= 68.91 + .6885Repsi *the ei is left off for simplicity The table below lists the actual data from which the regression above was estimated. For example, a student in the study who trained by doing 12 “60 pound reps” accomplished a maximum bench press amount of 76 pounds. Xi Yi 12 76 15 82 17 79 3 71 Predict the average maximum bench press for a student that does 17 “60 pound reps” a) a. around 68.85 pounds b) b. around 85.00 pounds c) c. around 80.61 pounds d) d. around 68.91 pounds
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A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to…
The mean height of Americanwomen in their twenties is about 64.3 inches, and the standard deviation is about 3.9 inches. The mean height of menthe same age is about 69.9 inches, with standard deviationabout 3.1 inches. Suppose that the correlation between theheights of husbands and wives is about r 0.5.(a) What are the slope and intercept of the regressionline of the husband’s height on the wife’s height in youngcouples?(b) Draw a graph of this regression line for heights of wivesbetween 56 and 72 inches. Predict the height of the husbandof a woman who is 67 inches tall, and plot the wife’s heightand predicted husband’s height on your graph.(c) You don’t expect this prediction for a single couple to bevery accurate. Why not?
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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+b a=-0.759 b=37.685 r2=0.674041 r=-0.821 Assume the correlation is significant, and use this to predict the number of situps a person who watches 11 hours of TV can do (to one decimal place)
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Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. The decrease in blood pressure y will vary about this subpopulation mean. What is the distribution of y for this subpopulation?

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