2. Another variable the enthusiast recorded was the weight of the car in pounds. Theregression with horsepower is given below.Simple linear regression results:Dependent Variable: WeightIndependent Variable: HPWeight=2084.29 + 6.938 HPSample size: 425R (correlation coefficient) = 0.647R-sq = 0.418Estimate of error standard deviation: 581.01Parameter estimates:Parameter Estimate Std. Err. AlternativeIntercept 2084.29 90.14+0Slope6.9380.398#0DF T-Stat P-value423 23.12 <0.0001XXXX423 XXXXa. Interpret the slope of this regression line if possible. If not, explain.b. Interpret the intercept of this regression line if possible. If not, explain.c. If the amount of power produced by the engine is 190 HP, what is the expectedweight for this car?d. If the weight is 3,400 pounds for a car with engine power of 190 HP, what is theresidual of weight?e. What percentage of the variability in weight can be explained by the regressionline relationship with engine power?f.If we use weight to estimate the engine power in HP, what percentage of thevariability in engine power can be explained by the regression line relationshipwith weight?g. Comparing your results from Q1.h and Q2.f, if you want to predict the enginepower of a car, which variable you would prefer to choose? Retail price or weight?Explain.

Here dependent variable (y) is the weight of the car and the independent variable (x) is…

Answered: a. Interpret the slope of this…

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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The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40inches. Is the result close to the actual weight of 352pounds? Use a significance level of 0.05.a. Chest size (inches) 41 54 44 55 39 51 Weight (pounds) 328 528 418 580 296 503 a.What is the regression equation? b. The best predicted weight for a bear with a chest size of 39 inches is _______pounds.
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
. Determine the regression equation using values you create for x and y for at least 10 pairs of data. Show the regression equation, correlation coefficient, and coefficient of determination. Then switch the x and y values for each data point. Based on that, again show the regression equation, correlation coefficient, and coefficient of determination. Discuss the similarities and differences between the results.
Which of the following statements is true? 1. The R (ie., R-squared) tells us the percentage of the variation in the dependent variable (cost) that is explained by variation in the independent variable (activity) II The R (e. R-squared) varies from 0% to 100%, and the lower the percentage, the better the fit of the data to a straight line. ONeither statement is true. Only statement I is true. Only statement II is tru. Both statements are true.
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 50 inches. Is the result close to the actual weight of 427 pounds? Use a significance level of 0.05. Chest size (inches) 49 51 53 61 57 45 Weight (pounds) 368 382 420 481 457 287 What is the regression equation? y=enter your response here+enter your response herex (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 50 inches? The best predicted weight for a bear with a chest size of 50 inches is enter your response here pounds. (Round to one decimal place as needed.)
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x 0 2 2 3 5 5 Test score, y 38 45 52 49 61 73 (a)x= 2 hours (b)x=4.5 hours (c)x=13 hours (d)2.5 hours Find the regression…
For variables X and Y we have that the correlation between them is 0.9. Also the standard deviation of Y is 8, while the standard deviation of X is 2. What is the slope of the regression equation that attempts to predict Y from X? Use two decimal places in your answer. Your Answer:
A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a. For each 1-cm increase in height, weight tends to increase by about 0.32 kg b. There is no association between weight and height c. Height accounts for about 32% of the total variability in weight d. The correlation between weight and height is about 0.32
1.
The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, y = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 66 68 71 86 78 85 62 82 79 61 Grades on Final 90 63 82 96 72 83 79 91 84 65

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