zion 11 of 15 >Suppose a researcher collects data on the gas mileage, measured in miles per gallon, for both city and highway driving for35 cars. The data is plotted with city gas mileage on the horizontal axis and highway gas mileage on the vertical axis. The1965 Triumph Herald is an outlier in the x-direction.What must be true about this car?O This car is an influential observation.M The highway gas mileage for this car is much higher or much lower than the rest of the cars in the data set.O The absolute value of the residual of this car is large.O The city gas mileage for this car is much higher or lower thanrest of the cars in the data set.O The highway gas mileage for this car is much higher or lower than other cars in the data set that have similarcity gas mileage.about us careers privacy policy terms of use contact us helpUS 9 O 4:23

Given: Sample size = 35 X = City gas mileage Y = Highway gas mileage There is an outlier in the x…

Answered: Suppose a researcher collects data on…

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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Judge and Cable (2010) reported the results of a study demonstrating a negative relationship between weight and income for a group of women professionals. To simply the weight variable, the women are classified into five categories that measure actual weight relative to height, from 1 = thinnest to 5 = heaviest. Income figures are annual income (in thousands), rounded to the nearest $1,000. Weight (X) Income (Y) 1 125 2 78 4 49 3 63 5 35 2 84 5 38 3 51 1 93 4 44 b. Compute the Pearson correlation for these data. c. What percentage of variance for the income is predicted from the weight for the women group?
An engineer wants to determine how the weight of a car, x, affects gas mileage, y. The following data represent the weights of various cars and their miles per ga Car A B 2685 2935 3330 3700 4250 23 CD E Weight (pounds), x Miles per Gallon, y 24.2 21.4 19.8 11.5 ..... Choose the best interpretation for the slope. O A. The slope indicates the mean change in miles per gallon for an increase of 1 pound in weight. O B. The slope indicates the mean weight. O C. The slope indicates the mean miles per gallon. O D. The slope indicates the ratio between the mean weight and the mean miles per gallon. O E. It is not appropriate to interpret the slope because it is not equal to zero. Choose the best interpretation for the y-intercept. O A. The y-intercept indicates the miles per gallon for a new car O B. The y-intercept indicates the mean miles for a car that weighs 0 pounds. O C. The y-intercept indicates the miles per gallon of the lightest car in the population. O D. The y-intercept indicates…
c. Identify the feature of the data that would be missed if part (b) was completed without constructing the scatterplot. Choose the correct answer below. O A. The scatterplot reveals a distinct pattern that is not a straight-line pattern. O B. The scatterplot reveals a distinct pattern that is a straight-line pattern with negative slope. O C. The scatterplot reveals a distinct pattern that is a straight-line pattern with positive slope. O D. The scatterplot does not reveal a distinct pattern. Using the linear correlation coefficient found in the previous step, determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables. Choose the correct answer below. O A. There is insufficient evidence to support the claim of a nonlinear correlation between the two variables O B. There is insufficient evidence to support the claim of a linear correlation between the two variables O C. There is sufficient evidence to support the claim of a…
1. A researcher is interested in knowing if there is a relationship between the number of ounces in the bottle and the price of liquid laundry detergent. Ounces Price (dollars) 16 3.99 32 4.99 64 5.99 200 10.99 Using the data in the above table, provide the following information: a.What are your conclusions and observations about the relationship between the two variables? b.What is the value of the intercept a? c.What is the value of the slope b? d. Write the linear regression equation
Mrs. Pearce conducted a survey in her class on the ages of famous people. The table below shows the results of her survey. Which equations could best represent the line-of-best-fit for the given data set? y  =  1.19x − 4.3 ⊝ y  =  0.81x + 4.56 ⊝ y  =  −1.19x + 4.3 ⊝ y  =  −0.81x − 4.56 ⊝
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.4 8.5 6.6 3.6 2.3 2.5 2.1 0.6 y 13.4 11.4 9.3 7.2 5.9 5.9 5.6 4.3 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.85x + 3.88. Use this equation to answer the following: Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 10 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 9 thousand automatic weapons? Answer = Answer= Round to 3 decimal places.
Answer 5-6
Below is the data for speed vs fuel consumption. The first set is speed(x); the second is fuel (y). if x = 10, y = 21 if x = 20 y =13, if x=30 y=10 if x = 40 y =8 if x = 50 y=7 if x =60 y = 5.9 if x = 70 y = 6.3 if x = 80 y = 6.95 if x = 90 y = 7.57 if x = 100 y = 8.27 if x = 110 y = 9.03 if x = 120 y = 9.87 if x = 130 y = 10.79 if x = 140 y = 11.77 and if x = 150 y =12.83. The equation of the LSR line is y^ = 11.058 - .015x The average of (x, y) are (80, 9.9) respectively. Find another point using the LSR equation, and graph the line
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We want to predict the selling price of a house in Newburg Park, Florida, based on the distance the house lies from the beach. Suppose that we're given the data in the table below. These data detail the distance from the beach (x, in miles) and the selling price (y, in thousands of dollars) for each of a sample of sixteen homes sold in Newburg Park in the past year. The data are plotted in the scatter plot in Figure 1. Also given is the product of the distance from the beach and the house price for each of the sixteen houses. (These products, written in the column labelled "xy", may aid in calculations.) Distance from the beach, x (in miles) 7.7 18.5 6.3 11.9 11.0 14.6 11.3 3.0 Selling price, y (in thousands of dollars) 290.8 215.0 304.4 269.9 283.2 203.4 212.4 263.2 197.3 272.2 215.1 241.6 312.3 221.5 279.1 236.1 10.9 4.9 12.5 5.8 3.6 5.7 9.4 8.1 Send data to calculator xy 2239.16 3977.5 1917.72 3211.81 3115.2 2969.64 2400.12 789.6 2150.57 1333.78 2688.75 1401.28 1124.28 1262.55…
For which pair of variables is it impossible to determine the direction of the relationship? a. Age and number of tickets issued for traffic violations b. Family income and BMI c. Race/ethnicity and attending a classical music performance in the last year d. Occupational prestige and grocery budget
2. Which model-linear, quadratic, or exponential-seems most appropriate for this scatter plot? X

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