Note: You can use excel to solve the problem and convert it to jpg format. Use least-squares regression to fit a straight line to7111517212329293739y2921291421157713Along with the slope and intercept, compute the standard error of the estimate and thecorrelation coefficient. Plot the data and the regression line. If someone made anadditional measurement of x = 10, y = 10, would you suspect, based on a visualassessment and the standard error, that the measurement was valid or faulty? Justifyyour conclusion.

Answered: Image /qna-images/answer/975070cc-6a90-46eb-b73f-e31eddb3e79f.jpg

Answered: Use least-squares regression to fit a…

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 5PPS

See similar textbooks

Similar questions

Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Find the mean hourly cost when the cell phone described above is used for 240 minutes.
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
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=-0.817 b=32.111 r²=0.877969 r=-0.937 Use this to predict the number of situps a person who watches 13.5 hour(s) of TV can do, and please round your answer to a whole number.
A statistics student wanted to determine if the distance to a destination was related to the price an airline would charge to fly to that destination. The student did a regression of airfare, y, on distance, x, and obtained the printout shown below from a statistical software package. Predictor Coef Constant 83.02 Distance 0.117 Oŷ= 0.117+83.02x Oy=83.02 +0.117x Oy= 49.29 + 83.02x Oŷ= 0.028 + 0.117x Oy=83.02 + 25.25x 1 SE Coef 49.29 0.028 S = 25.25 R-Sq = 63.2 % Which of the following is the correct equation of the least-squares line? Mark this and return t-ratio 1.689 4.179 R-Sq (Adj) = 64.7% Save and Exit р 0.046 0.000 di I tv Next AM Submit Aa
A microcomputer manufacturer has developed a regression model relating his sales (y=$10,000s) with three independent variables. The three independent variables are price per unit(Price in $100s), advertising( ADV in $1000s) and the number of product lines (Lines). Part of the regression results is shown below. Coefficient Standard Error Intercept 1.0211 22.8752 Price(X1) -0.1524 0.1411 ADV (X2) 0.8849 0.2886 Lines(X3) -0.1463 1.5340 Source d.f. S.S. Regression 3 2708.61 Error 14 2840.51 Total 17 5549.12 What has been the sample size (n) for this analysis? Use the above results to find the estimated multiple…
The prelim grades (x) and midterm grades (y) of a sample of 10 MMW students is modeled by the regression line y = 12.0623 + 0.7771x. Estimate the prelim grade if the midterm grade is 83.
Looking at this output in the photo, we know that the proportion of the variation in mileage is accounted for by the linear relationship with the weight of the car is 43.8 % and that the estimate of the regression standard deviationis is 6.78. Determine the equation of the Least Squares Regression Line and what the interpretation of the slope for weight of car is? Then performing a T-test, determine what the value of the t stastitic is if we wanted to test if there is a significant straight-line relationship between the weight and the mileage of a car.
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-0.962 b=32.772 r2=0.502681 r=-0.709 Use this to predict the number of sit ups a person who watches 7.5 hours of TV can do, and please round your answer to a whole number.
The accompanying data are the number of wins and the eamed run averages (mean number of earned nuns allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x= 5 wins E Click the icon to view the table of numbers of wins and earned run average. (b) x= 10 wins (c) x = 19 wins (d) x= 15 wins The equation of the regression line is y =x+ (Round to two decimal places as needed.)

SEE MORE QUESTIONS

Recommended textbooks for you

Glencoe Algebra 1, Student Edition, 9780079039897…

Algebra

ISBN:

9780079039897

Author:

Carter

Publisher:

McGraw Hill

College Algebra

Algebra

ISBN:

9781337282291

Author:

Ron Larson

Publisher:

Cengage Learning

Algebra & Trigonometry with Analytic Geometry

Algebra

ISBN:

9781133382119

Author:

Swokowski

Publisher:

Cengage

Glencoe Algebra 1, Student Edition, 9780079039897…

Algebra

ISBN:

9780079039897

Author:

Carter

Publisher:

McGraw Hill

College Algebra

Algebra

ISBN:

9781337282291

Author:

Ron Larson

Publisher:

Cengage Learning

Algebra & Trigonometry with Analytic Geometry

Algebra

ISBN:

9781133382119

Author:

Swokowski

Publisher:

Cengage

College Algebra

Algebra

ISBN:

9781938168383

Author:

Jay Abramson

Publisher:

OpenStax

College Algebra

Algebra

ISBN:

9781305115545

Author:

James Stewart, Lothar Redlin, Saleem Watson

Publisher:

Cengage Learning

College Algebra (MindTap Course List)

Algebra

ISBN:

9781305652231

Author:

R. David Gustafson, Jeff Hughes

Publisher:

Cengage Learning

SEE MORE TEXTBOOKS