Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.818 and the leastsquares regression line is y = 0.137x + 13.622. Complete parts (a) and (b) below.27 25 75 26.75 25.25 27.75 26.25 26.25 27. 25 27 25 27.25 27.25 DHeight, xHead Circumference, y 17.5 17.1 17.2 17.1 17.4 17.217.2 17.4 17.3 17.3 17.3(a) Compute the coefficient of determination, R?R2 =% (Round to one decimal place as needed.)(b) Interpret the coefficient of determination and comment on the adequacy of the linear model.Approximately % of the variation inV is explained by the least-squares regression model. According to the residual plot, the linear modelappears to be(Round to one decimal place as needed.)arch

Answered: Suppose a doctor measures the height,…

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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Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.941 and the least squares regression line is y = 0.244x + 10.794. Complete parts a and b below. Height, x Head Circumference, y 27.00 25.75 26.25 17.3 25.75 27.50 17.5 26.25 17.1 26.00 27.00 17.4 17.4 17.1 17.1 17.1 (a) Compute the coefficient of determination, R?. R2 =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variation in height is explained by the least-squares regression model. (Round to one decimal place as needed.)
The accompanying data represent the number of days absent, x, and the final exam score, y, for a sample of college students in a general education course at a large state university. Complete parts (a) through (e) below. Click the icon to view the absence count and final exam score data Click the icon to view a table of critical values for the correlation coefficient. ..... he final exar (a) Find the least-squares Reference Absences and final exam scores (Round to three decimal Critical Values for Correlation Coefficient answer box (b) Interpret the slope an (Round to three decimal No. of 9. absences, x 0 1 2 3 4 5 6 7 3 0.997 t appropriate O A. For every addition 0.950 Final 86.7 83.6 80.2 78.3 73.5 64.7 72.2 65.9 65.9 O B. For every addition erage final e exam score, y 88.4 5 0.878 O C. The average final 0.811 terpret the sl 0.754 O D. It is not appropria 8 0.707 Print Done (c) Predict the final exam 6. 0.666 y =(Round to two de 10 0.632 11 0.602 Compute the residual. 12 0.576 (Round…
104. What is the least squares regression line for the following summary data? y-hat =1.99x + 3.11 y-hat = 2.01x + 3.55 y-hat = 2.16x + 2.47 y-hat =2.02x + 2.73
Suppose a doctor measures the height, x, and head circumference, y, of 11 children and obtains the data below. The correlation coefficient is 0.883 and the least squares regression line is y = 0.134x + 13.693. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.4 27.75 25.25 26 25.25 27.25 26.75 26 27.25 27 27.25 26.75 17.1 17.3 17.0 17.4 17.2 17.2 17.3 17.3 17.4 17.3 ..... (a) Compute the coefficient of determination, R?. R? % (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Approximately % of the variation in is explained by the least-squares gression model. (Round to one decimal place as needed.)
o pis pe Suppose a doctor measures the height, x, and head circumference, y. of 11 children and obtains the data below. The correlation coefficient is 0.899 and the least squares regression line is y = 0.185x+ 12.276. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.4 17.1 17.2 16.9 17.4 17.1 17.1 17.3 17.3 17.3 17.4 27.75 25.75 26.75 25.75 28 26.5 25.75 26.75 27 27.25 27.25 (a) Compute the coefficient of determination, R?. R2 = % (Round to one decimal place as needed) (b) Interpret the coefficient of determination k Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. Enter your answer in each of the answer boxes. Save for Later DUE RTTT:DY PIVT Score: o:5SYO Tor Tv attempts 3:35 PA- a 2/22/202 Type here to search insert prt sc 4+ backsp %23 6. 8.
The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 124 143 158 160 196 Number of Bids 12 13 15 16 20 Table Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal places.
8. You collect data on people's height and study the relationship between gender and height. A regression of the height on a binary variable (Female), which takes a value of one for females and zero otherwise, yields the following result: Height = 71.0- 4.84 x Female, R2 = 0.40, SER = 2.0 (0.3) (0.57) (a) What is the sample average male height? (b) What is the sample average female height? I (c) How to interpret the slope coefficient -4.84? (d) Is the error term in the regression more likely to be heteroskedastic or homoscedastic? Why? R English (United States) D'Focus Page 9 of 10 920 words 100%
The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS
The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 20 23 26 28 48 Number of Bids 1 4 6 7 9 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: According to the estimated linear model, if the value of the independent variable is increased by one unit, then the change in the dependent variable y^ is given by? Step 4 of 6:…
4. To assess the effect of state right to work laws on union membership (which do not require membership in unions as a precondition for employment), the following regression results were obtained from the data for 50 states in the United States for 1982: PVT, = 19.8066 – 9.3917 RTW, t= (17.0352) (-5.1086) R = 0.3522 where PVT = percentage of private sector employees in unions in 1982 RTW = 1 if right-to-work law exists, 0 otherwise (in 1982, 20 states had right- to-work laws). %3D A priori, what is the expected relationship between PVT and RTW? Do the regression results support this expected relationship? Interpret the regression results. What was the average percent of private sector employees in unions in the states that did not have the right-to-work laws?
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.858 and the least squares regression line is y = 0.228x +11.187. Complete parts (a) and (b) below. Height, x 27.5 25.75 26.5 25.5 27.25 26.25 25.75 27.25 27 27.25 27 Head Circumference, y 17.4 17.2 17.2 16.9 17.6 17.1 17.1 17.4 17.4 17.3 17.3 (a) Compute the coefficient of determination, R². R² =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. According to the residual plot, the linear model appears to be
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation Ŷ=42.80-0.53x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)

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