An engineer wants to determine how the weight of a gas-powered car, x, affects the gas mileage, y. Would it be reasonable to use the least-squares regression line to predict the miles per gallon of a hybrid gas and electric car? Why and why not?

An engineer wants to determine how the weight of a gas-powered car, x, affects the gas mileage, y. Would it be reasonable to use the least-squares regression line to predict the miles per gallon of a hybrid gas and electric car? Why and why not?

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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s and their miles per gallon in the city for the most recent model An engineer wants to determine how the weight of a gas-powere year. Complete parts (a) through (d) below. E Click here to view the weight and gas mileage data. Car weight and MPG (a) Find the least-squares regression line treating weight as the Weight (pounds), x Miles per Gallon, y ý=x+ () (Round the x coefficient to five decimal places as needed. Roun 3670 18 3853 16 (b) Interpret the slope and y-intercept, if appropriate. Choose the (Use the answer from part a to find this answer.) 2621 26 3460 19 O A. For every pound added to the weight of the car, gas mile 3233 22 -intercept. 2867 24 O B. A weightless car will get miles per gallon, on average 3683 16 2548 25 O C. For every pound added to the weight of the car, gas mile er gallon, on average. 3489 19 O D. It is not appropriate to interpret the slope or the y-interce 3761 16 (c) A certain gas-powered car weighs 3643 pounds and gets 17 r ht? The estimated average…
Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of company speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow: YEAR 1 2 3 4 5 6 7 B 9 10 11 12 UNIT SALES (THOUSANDS) 398 698 898 y bar 1,304 1,163 1,195 898 1,200 982 1,235 875 811 PRICE $ PER UNIT 283 209 213 220 209 200 220 209 230 213 216 243 ADVERTISING ($000) 621 821 1,200 1,405 1,210 1,304 875 1,200 691 875 691 691 a. Perform a regression analysis based on these data using Excel. Note: Negative values should be indicated by a minus sign. Round your answers to 4 decimal places. Price advertising
Researchers measured the percent body fat and the preferred amount of salt (percent weight/volume) for several children. Here are data for seven children salt: Preferred amount of salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 20 30 22 30 38 23 30 Using your calculator or software, what is the equation of the least-squares regression line for predicting percent body fat from preferred amount of salt? (a) yˆ = 24.2 + 6.0 x (b) yˆ = 0.1 5 + 0.0 1x (c) yˆ = 6.0 + 24.2 x
A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y = 74.80 + 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with O rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…
David's Landscaping has collected data on home values (in thousands of $) and expenditures (in thousands of $) on landscaping with the hope of developing a predictive model to help marketing to potential new clients. Data for 14 households may be found in the file Landscape. Click on the datafile logo to reference the data. (NEED ANSWERS FOR C, D, and E) c. Use the least squares method to develop the estimated regression equation (to 5 decimals). d. For every additional $1000 in home value, estimate how much additional will be spent on landscaping (to 2 decimals). e. Use the equation estimated in part (c) to predict the landscaping expenditures for a home valued at $575,000 (to the nearest whole number).
To predict blood alcohol content of a student who drank 5 beers will be about?
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y=nothingx+(nothing) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) A. A weightless car will get nothing miles per gallon, on average. It is not appropriate to interpret the slope. B. For every pound added to the weight of the car, gas mileage in the city will decrease by nothing mile(s) per gallon, on…
Biologist Theodore Garland, Jr. studied the relationship between running speeds and morphology of 49 species of cursorial mammals (mammals adapted to or specialized for running). One of the relationships he investigated was maximal sprint speed in kilometers per hour and the ratio of metatarsal-to-femur length. A least-squares regression on the data he collected produces the equation ŷ = 37.67 + 33.18x where x is metatarsal-to-femur ratio and y is predicted maximal sprint speed in kilometers per hour. The standard error of the intercept is 5.69 and the standard error of the slope is 7.94. Construct a 96% confidence interval for the slope of the population regression line. Give your answers precise to at least two decimal places. contact us help 6:42 PM povecy polcy terms of use careers A E O 4») 18 -క90.4 58 12/14/2020 a 17 |耳即 delets prt sc insert 112 19 18 + 16 backspace f5 fA
A music critic was interested in whether particular variables measured on a song change over time. Two variables the critic considered were a song’s Tempo (in bpm) and a song’s Danceability. We will use the songs written before the year 2000 from the original SpotifySample data set. The data set that you will use to complete this investigation is called SpotifyB2000 and consists of 483 songs. Write the least-squares regression line equation describing Year and Danceability usingproper notation and values. Interpret the slope of the regression line for Year and Danceability in context. Would the interpretation of the y-intercept for Year and Danceability be meaningful? Ifso, interpret it. If not, state why not in one sentence. Calculate and record the coefficient of determination value r2for Year and Danceabilityand interpret this value in context. State the hypotheses for the test of the slope. Write the p-value found in the output from (n), and use the p-value provided in the…
3. In a study of 1991 model cars, a researcher computed the least-squares regression line of price (in dollars) on horsepower. He obtained the following equation for this line. Price = -6677 + 175 × Horsepower
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…
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. ..... (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. x + (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) A. For every pound added to the weight of the car, gas mileage in the city will decrease by mile(s) per gallon, on average. A weightless car will get miles per gallon, on average. O B. A weightless car will get miles per gallon, on…