Intro Stats, Books a la Carte Edition (5th Edition)
5th Edition
ISBN: 9780134210285
Author: Richard D. De Veaux, Paul Velleman, David E. Bock
Publisher: PEARSON
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Chapter 9.4, Problem 3JC
To determine
Explain whether the conclusion of the researcher that “There has been no change over time in the strength of Atlantic hurricanes.” Is a proper interpretation of the regression model.
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You want to estimate a model on car production (units) based on the previous year data on the number of cars sold (units), price of cars ($/unit), and total sales of cars ($). The regression
would be car production on the number of cars sold, price of cars, total sales of cars, and a constant. Describe the problem with this model.
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a. what is the regression equation?
b. The best-predicted temperature when a bug is chirping at 3000 chirps per minute is?
In a 2020 report, Lea, an employee at the Ministry of Health, discussed the relationship between mean
annual temperature (x) and the mortality rate (y) for a type of skin cancer (Melanoma) in women at a Health
Conference in Jamacia. The data below contains the mean annual temperature (in degrees Celsius) and
Mortality Index for Melanoma. Use the data presented on these two variables to answer the following
questions.
Mortality
Temperature
102.5 104.5 100.4 95.9 87.0 95.0 88.6 89.2
51.3 49.9 50.0 49.2 48.5 47.8 47.3 45.1
(i) Plot a scatter diagram for the data above
(ii) Find the equation of the least squares regression line
(iii) Insert the regression line on the scatter diagram
(iv) State the coordinates of the y-intercept and the value of the gradient or slope.
(v) Calculate the correlation coefficient and interpret the result.
(vi) Calculate the coefficient of determination and interpret the result.
(vi) Use the equation in (ii) to determine the mortality rate you would expect to…
Chapter 9 Solutions
Intro Stats, Books a la Carte Edition (5th Edition)
Ch. 9.4 - Recall the regression example in Chapter 7 to...Ch. 9.4 - Prob. 2JCCh. 9.4 - Prob. 3JCCh. 9 - Housing prices The following regression model was...Ch. 9 - Candy sales A candy maker surveyed chocolate bars...Ch. 9 - Prob. 3ECh. 9 - Prob. 4ECh. 9 - Prob. 5ECh. 9 - Prob. 6ECh. 9 - Movie profits once more Look back at the...
Ch. 9 - Prob. 8ECh. 9 - Prob. 9ECh. 9 - More indicators For each of these potential...Ch. 9 - Interpretations A regression performed to predict...Ch. 9 - Prob. 12ECh. 9 - Prob. 13ECh. 9 - Scottish hill races Hill runningraces up and down...Ch. 9 - Prob. 15ECh. 9 - Candy bars per serving: calories A student...Ch. 9 - Prob. 17ECh. 9 - More hill races Here is the regression for the...Ch. 9 - Prob. 19ECh. 9 - Home prices II Here are some diagnostic plots for...Ch. 9 - Admin performance The AFL-CIO has undertaken a...Ch. 9 - GPA and SATs A large section of Stat 101 was asked...Ch. 9 - Prob. 23ECh. 9 - Breakfast cereals We saw in Chapter 7 that the...Ch. 9 - Breakfast cereals again We saw a model in Exercise...Ch. 9 - Prob. 26ECh. 9 - Hand dexterity Researchers studied the dexterity...Ch. 9 - Candy bars with nuts The data on candy bars per...Ch. 9 - Scottish hill races, men and women The Scottish...Ch. 9 - Scottish hill races, men and women climbing The...
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardDoes Table 1 represent a linear function? If so, finda linear equation that models the data.arrow_forwardDoes a linear, exponential, or logarithmic model best fit the data in Table 2? Find the model.arrow_forward
- Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?arrow_forwardTable 6 shows the year and the number ofpeople unemployed in a particular city for several years. Determine whether the trend appears linear. If so, and assuming the trend continues, in what year will the number of unemployed reach 5 people?arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.0 278.0 253.1 258.8 279.0 258.0 274.4 262.2 Height 1785.3 1771.2 1675.9 1646.3 1859.2 1710.4 1789.2 1737.2 The regression equation is y=+x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.1 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? OA. The result is exactly the same as the actual height of 1776 mm. OB. The result is very different from the actual height of 1776 mm. OC. The result is close to the actual height of 1776 mm. OD. The result does not make sense given the context of the data.arrow_forward
- Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.0 278.0 252.7 259.0 278.9 257.8 274.1 262.3 Height 1785.0 1770.9 1676.3 1646.0 1859.3 1710.1 1789.3 1737.2 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) ←arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 252.9 258.7 279.2 258.0 274.2 262.3 Height 1785.0 1771.0 1675.9 1646.2 1858.8 1709.6 1788.7 1736.6 The regression equation is ŷ= + (x y= (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) YouTube no New Helluva Boss Recommended Zarrow_forward
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