27) The total blood cholesterol concentrations, in mg/dL, are shown below for a randomsample of six people. In the table below, x is the age of the subject and y is the subject'stotal cholesterol concentration. Find the 95% prediction interval when x = 58 years.27)x (age)364958696371204256194257245y (mg/dL)209A) Regression line: y = 1.82x +122.5Standard error of the estimate: sest= 16.51184Prediction interval for y(58): 159

27) Excel Procedure for Regression: Enter the data for x and y in Excel sheet.Go to DataClick on…

Answered: 27) The total blood cholesterol…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

A multiple regression includes two regressors: Yi = b0 + b1X1i +b2X2i + ui. What is the expected change in Y if X1 increases by 8 unitsand X2 is unchanged? What is the expected change in Y if X2 decreasesby 3 units and X1 is unchanged? What is the expected change in Y if X1increases by 4 units and X2 decreases by 7 units?
1. Prior to being hired, the ve salespersons for a computer store were given a standard sales aptitude test. For each individual, the score achieved on the aptitude test and the number of computer systems sold during the first 3 months of their employment are shown in the attached image. (a) Determine the least-squares regression line and interpret its slope. (b) Estimate, for a new employee who scores 60 on the sales aptitude test, the number of units the new employee will sell in her first 3 months with the company. (c) Find the standard error of estimate. (d) Find the r-square of the model.
To investigate the relationship between the selling price of a house, yy, in dollars, and the size of the house xx, in square feet, a local builder collected data on a random sample of 120 houses from a certain region. Assume that the conditions for inference for the slope of a regression line are met. The resulting 95 percent confidence interval for the population slope of the regression line relating price and size is given by (62,99). The local builder claims that the selling price of houses from the region increases by $104 for every extra square foot of space in the house. Which of the following best describes the conclusion that can be reached about this claim based on the confidence interval? A)The claim is supported by the interval, since the interval does not contain the value 0. B)The claim is supported by the interval, since all values in the interval are positive. C)The claim is supported by the interval, since the interval does not contain the…
State the four conditions required for making regression inferences.
(1) For neighborhoods with more than 66% of schools participating in free school lunch program, the estimated regression model is: crime_rate = 142.5737 - 1.9215(Prop_change_income) - 38.8741(0) - 19.20521(0) = 142.5737 - 1.9215(Prop_change_income) (2) For neighborhoods with 33% to 66% of schools participating in free school lunch program, the estimated regression model is: crime_rate = 142.5737 - 1.9215(Prop_change_income) - 38.8741(0) - 19.20521(1) = 123.36849 - 1.9215(Prop_change_income) (3) For neighborhoods with less than 33% of schools participating in free school lunch program, the estimated regression model is: crime_rate = 142.5737 - 1.9215(Prop_change_income) - 38.8741(1) - 19.20521(0) = 103.69959 - 1.9215(Prop_change_income) Questions: 1. What is difference between models in 1,2,and 3? 2. Which estimated model 1,2, or 3 results in the line with the highest estimated crime rates (the highest positioned line)?
(1) For neighborhoods with more than 66% of schools participating in free school lunch program, the estimated regression model is: crime_rate = 142.5737 - 1.9215(Prop_change_income) - 38.8741(0) - 19.20521(0) = 142.5737 - 1.9215(Prop_change_income) (2) For neighborhoods with 33% to 66% of schools participating in free school lunch program, the estimated regression model is: crime_rate = 142.5737 - 1.9215(Prop_change_income) - 38.8741(0) - 19.20521(1) = 123.36849 - 1.9215(Prop_change_income) (3) For neighborhoods with less than 33% of schools participating in free school lunch program, the estimated regression model is: crime_rate = 142.5737 - 1.9215(Prop_change_income) - 38.8741(1) - 19.20521(0) = 103.69959 - 1.9215(Prop_change_income) Questions: 1. What is difference between models in 1,2,and 3? 2. Which estimated model 1,2, or 3 results in the line with the highest estimated crime rates (the highest positioned line)? 3. Test whether the crime rate in neighborhoods with 33% to 66% of…
A zoologist selected 12 black bears in a Canadian habitat at random to examine the relationship between the age in years, xx, and the weight in tens of pounds, yy. The 95 percent confidence interval for estimating the population slope of the linear regression line predicting weight in tens of pounds based on the age in years is given by 1.272±0.5701.272±0.570. Assume that the conditions for inference for the slope of the regression equation are met. Which of the following is the correct interpretation of the interval? A)We are 95 percent confident that the mean increase in the weight of a black bear for each one-year increase in the age of the bear is between 7.0 and 18.4 pounds. B)We are 95 percent confident that an increase of one year in the age of an individual black bear will result in an increase in the black bear’s weight of between 7.0 and 18.4 pounds. C)We are 95 percent confident that for every one-year increase in the age of black bears in the sample,…
For variables x1, x2, x3 and y satisfying the assumptions for multiple linear regression inferences, the population regression equation is y = 27 – 4.7x1 + 2.3x2 + 5.8 x3. For samples of size 20 and given values of the predictor variables, the distribution of the estimates of β1 for all possible sample regression planes is a _________ distribution with mean _________ and standard deviation _______.
1.The head of the production department of a RM Electronic Company wants to determine the relationship between the number of workers who assemble the product and the number of units assembled per day. d.Test whether the correlation is significant at 5% level of significance. e.Determine the regression line. f.Predict the number of units used if there are 20 workers. g.How many workers is needed to produce 1000 units per day?
The data in Table 11.17 are given for 9 patients with aplastic anemia [11]. 11.6 Test for the statistical significance of the regression line using the t test.
8)Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 11 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.86, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 86000 and the sum of squared errors (SSE) is 14000. From this information, what is MSE/MST? .5000 NONE OF THE OTHERS .2000 .3000 .4000
Find the simple regression line y=α+βx for the pairs of points belonging to the independent and dependent variables (xi,yi) , respectively. Also, interpret the result by calculating the Pearson correlation coefficient.

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS