The data set for a random sample of 10 soccer player's heights compared to their shoe sizes is summarized in the table.Shoe Size (U.S.) Height (inches)7.58128.5910.51112.51310.56370.5727169.57075647268Part A: What is the equation of the least-squares regression line?Part B: If a soccer player wears a size 10 and is 68 inches tall, what is their residual? Show all mathematical work.Part C: Interpret the meaning of the residual from part B. (

Answered: The data set for a random sample of 10…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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A researcher records data on 7 adult pairs' heights (in inches) to compare the physical characteristics of brothers and sisters. Brother Sister 71 69 68 64 6 65 67 63 70 65 71 62 66 62 Mean 68.4285 64.2857 SD 2.2253 2.4299 r=0.4050 What would the least-squares regression equation be for predicting the brother's height from the sister's? A. brother's height = 0.037+44.58 * sister's height B. brother's height = 44.58 + 0.371* sister's height C. brother's height = 20.71 + 0.029 * sister's height D. brother's height = 3.28- 40.68 * sister's height If the sister's height is the same as the mean (64.2857 inches), what would the brother's predicted height be A. 68.4285 (the same as the mean as well) B. 62.1234 C. 70.8990 D. None of the above. Which of the following would be correct? A. The pair of means, (68.4285, 64.2857), lies on the linear regression line. B. The effectiveness of the linear regression model is about 16%, C. The effectiveness of the linear regression model is 10096. D. Both…
What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman's weight? The time on the phone and weight for 8 women are shown in the table below. Time 46 60 43 39 18 20 35 18 Pounds 138 142 135 116 119 98 125 112 r2r2 = (Round to two decimal places) Interpret r2r2 : There is a 71% chance that the regression line will be a good predictor for women's weight based on their time spent on the phone. Given any group of women who all weight the same amount, 71% of all of these women will weigh the predicted amount. There is a large variation in women's weight, but if you only look at women with a fixed weight, this variation on average is reduced by 71%. 71% of all women will have the average weight. The equation of the linear regression line is: ˆyy^ = + xx (Please show your answers to two decimal places)
The following is a table showing the price of car insurance based on how much driving experience that an insurance holder has. Use the years of driving experience as your explanatory (independent) variable and the monthly insurance premium as your response (dependent) variable. Years of driving experience 0 1 2 3 4 5 6 7 8 Cost of monthly insurance premium 67 63 62 59 50 42 39 34 32 a) What is the least squares regression line for this bivariate dataset? (write in y=mx+b form) b) What is the correlation coefficient? c) Find the residual cost of someone with 5 years of driving experience (difference between predicted and observed values) d) Interpret the y-intercept and slope of the regression line in context of the problem.
A chemical engineer is investigating the effect of process operating temperature on product yield. The study results in the following data: Temperature Yield 100 57.71 110 63.17 120 76.30 130 73.30 140 89.07 150 91.17 160 170 114.11 106.33 180 190 114.38 124.07 You can use Minitab to answer the following questions. However, you should be able to calculate the slope and intercept of the least squares regression model by hand, which requires only the mear and standard deviations of X and Y, and the correlation coefficient (here r = 0.9751). 1. what is the mean temperature? 155 150 130 145 Submit Answer Tries 0/2
Name Her Age His Age Barack and Michelle Obama 45 47 George W. and Laura Bush 54 54 Bill and Hillary Clinton 45 46 Ronald and Nancy Reagan 55 64 Gerald and Betty Ford 59 69 Lyndon and Lady Bird Johnson 56 61 John and Jacqueline Kennedy 31 43 Dwight and Mamie Eisenhower 50 55 (c) Remove the outlier and compute the least-squares regression line for predicting the president's age from first lady's age. Round the slope and y -intercept values to at least four decimal places. Regression line equation: =y
How strongly do physical characteristics of sisters and brothers correlate? Here are data on the heights (in inches) of 12 adult pairs: Brother 71 68 66 67 70 71 70 73 72 65 66 70 Sister 69 64 65 63 65 62 65 64 66 59 62 64 (a) Use your calculator or software to find the correlation and the equation of the least-squares line for predicting sister’s height from brother’s height. Make a scatterplot of the data and add the regression line to your plot. Damien is 70 inches tall. Predict the height of his sister Tonya. Based on the scatterplot and the correlation r, do you expect your prediction to be very accurate? Why?
The following table represents the average price (in dollars) for a dozen eggs and a gallon of milk for five years. Dozen Eggs 1.14 1.29 1.28 1.31 1.36 Gallon of Milk 3.25 3.12 3.15 3.26 3.33 Determine the least-squares regression line. Select one: O a. ĝ = 0.1978r - 2.9926 O b. ý = 2.9926r +0.1798 O c. û = 2.9926r – 0.1798 O d. ŷ = 0.1978r + 2.9926
50
Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances Demand for Guitars 3 4 5 8 6 5 9 6 8 8 6 9 This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): Y=0+0x
This table shows the average heights for American boys in 1990. Age (years) Height (cm) birth 50.8 2 83.8 3 91.4 5 106.6 7 119.3 10 137.1 14 157.5 Decide which variable should be the independent variable and which should be the dependent variable. Draw a scatter plot of the data. Does it appear from inspection that there is a relationship between the variables? Why or why not? Calculate the least-squares line. Put the equation in the form of: ŷ = a + bx Find the correlation coefficient. Is it significant? Find the estimated average height for a one-year-old. Find the estimated average height for an eleven-year-old. Does it appear that a line is the best way to fit the data? Why or why not? Are there any outliers in the data? Use the least squares line to estimate the average height for a sixty-two-year-old man. Do you think that youranswer is reasonable? Why or why not? 10. What is the slope of the least-squares (best-fit) line?…

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