2. The data in the table represent the weights of various domestic cars and their miles per gallon in the city for the 2008 modelyear. For these data, the least-squares regression line is y = – 0.005x+ 38.154. A twelfth car weighs 3,425 pounds and gets12 miles per gallon. Compute the coefficient of determination of the expanded data set. What effect does the addition of thetwelfth car to the data set have on R?3 Click the icon to view the data table.%3DThe coefficient of determination of the expanded data set is R =(Round to one decimal place as needed.)How does the addition of the twelfth car to the data set affect R?O A. It increasesR?.O B. It decreases R.O C. It does not affect R.3: Data TableWeight (pounds), Miles perGallon, yCar 13,7640663,535Car 222Car 321Car 43,17721Car 52,576Car 63,72520909'3,775Car 7Car 861-21Car 93,311Car 102,993Car 112,75925

Coefficient of Determination: It is a statistical measure commonly used in the testing of hypotheses…

Answered: 2. The data in the table represent the…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 15PPS

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Read the following description of a data set. The owner of a bicycle store wonders if the employees who take the longest lunch breaks also make the fewest bicycle sales. One day, she notes the length of each salesperson's lunch break (in minutes), x, as well as the number of bicycles he or she sold that day, y. The least squares regression line of this data set is: ŷ = -0.297x + 10.889 Complete the following sentence: If a salesperson spends one additional minute at lunch, the least squares regression line predicts he or she will sell | fewer bicycles that day. Submit е
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The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.984. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y = - 0.0066x + 43.3954. Complete parts (a) and (b) below. Click the icon to view the data table. (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? Data Table The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Full data set % of the variance in is by the linear model. Miles per Miles per Weight (pounds), x Weight (pounds), x Car Car (Round to one decimal place as needed.) Gallon, y Gallon, y…
Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (F), as the explanatory variable and the number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data is Bags = -151.05 +2.65Temp. On one of the observed days, the temperature was 82 °F and 68 bags of ice were sold. Determine the number of bags of ice predicted to be sold by the LSR line, Bags, when the temperature is (82\ \text (°F. J\\) Enter your answer as a whole number, rounding if necessary. Bags = 1.11 residual Incorrect Using the predicted value you just found, compute the residual at this temperature. 1.11 Incorrect ice bags ice bags
Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable and the number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data is =-114.05+2.17X. On one of the observed days, the temperature was 85 °F and 66 bags of ice were sold. The number of bags of ice predicted to be sold by the LSR line, y, when the temperature is 85 °F is 70. Using the predicted value, compute the residual at this temperature. Answer:
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…
Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable and the number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data is Y = -151.05 + 2.65 X. On one of the observed days, the temperature was 82 °F and 68 bags of ice were sold. Determine the number of bags of ice predicted to be sold by the LSR line, Y, when the temperature is 82 °F. Enter your answer as a whole number, rounding if necessary. ice bags Using the predicted value you just found, compute the residual at this temperature. residual = ice bags
Suppose a new point was added to your data: a wine that is 20% alcohol that contains 0calories. How will that affect the value of r and the slope of the regression line? (Nocalculation needed)
To monitor and improve its productivity, a company made an investigation and found out that the factor that affects the productivity the most is the absenteeism. The company data analytics department have collected data about the two variables (Productivity and Absenteeism) for the 12 past years as shown in the table below. Now the purpose of the company is to determine, through regression analysis, whether the productivity is statistically affected by the absenteeism level or not. Year Absenteeism Productivity (in number of absent worker) (in Million AED) 1 204 342 2 352 336 3 154 406 4 206 410 5 422 278 6 530 214 7 750 138 8 482 268 9 374 262 10 120 356 11 188 396 12 634 152 Questions: Construct a scatter diagram for the data about productivity and absenteeism then interpret the possible relationship that can be found. Construct a simple regression model to predict the…
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 large city hospital conducted a study to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employees. A sample of 10 employees was selected and the following data were collected. Develop a scatter diagram for these data. Does a linear relationship appear reasonable? Explain. Develop the least squares estimated regression equation that relates the distance to work to the number of days absent. Predict the number of days absent for an employee that lives 5 miles from the hospital.

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