Elementary Statistics ( 3rd International Edition ) Isbn:9781260092561
3rd Edition
ISBN: 9781259969454
Author: William Navidi Prof.; Barry Monk Professor
Publisher: McGraw-Hill Education
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Chapter 4, Problem 6CQ
To determine
To find the least square regression line for the given data
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The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months:
Month
Number of Accidents
Jan
25
Feb
40
Mar
60
Apr
90
Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places):
ŷ-0-0x
Using least-squares regression, the forecast for the number of accidents that will occur in the month of May = accidents (enter your
response as a whole number).
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months:
Month
Jan
Feb
Mar
Apr
Number of Accidents
25
48
70
90
Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places):
y =
Using least-squares regression, the forecast for the number of accidents that will occur in the month of May =
accidents (enter your response as a whole number).
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) to (c) below.
.....
(a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females.
Find the least-squares regression line for males.
y=x+O
Data for licensed drivers by age and gender.
(Round the slope to three decimal places and round the constant to the nearest integer as needed.)
Find the least-squares regression line for females.
x +
Number of
Number of
Number of Male Fatal
Number of Female Fatal
(Round the slope to three decimal places and round the constant to the nearest integer as needed.)
Licensed Drivers Crashes
Licensed Drivers
Crashes
(b) Interpret the slope of the least-squares regression line for each gender, if appropriate. How might an insurance…
Chapter 4 Solutions
Elementary Statistics ( 3rd International Edition ) Isbn:9781260092561
Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - Prob. 13ECh. 4.1 - Prob. 14ECh. 4.1 - In Exercises 13-16, determine whether the...Ch. 4.1 - In Exercises 13-16, determine whether the...Ch. 4.1 - In Exercises 17-20, compute the correlation...Ch. 4.1 - In Exercises 17-20, compute the correlation...
Ch. 4.1 - In Exercises 17-20, compute the correlation...Ch. 4.1 - In Exercises 17-20, compute the correlation...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - Price of eggs and milk: The following table...Ch. 4.1 - Government funding: The following table presents...Ch. 4.1 - Pass the ball: The following table lists the...Ch. 4.1 - Carbon footprint: Carbon dioxide (CO2) is produced...Ch. 4.1 - Foot temperatures: Foot ulcers are a common...Ch. 4.1 - Mortgage payments: The following table presents...Ch. 4.1 - Blood pressure: A blood pressure measurement...Ch. 4.1 - Prob. 38ECh. 4.1 - Police and crime: In a survey of cities in the...Ch. 4.1 - Age and education: A survey of U.S. adults showed...Ch. 4.1 - Whats the correlation? In a sample of adults, the...Ch. 4.1 - Prob. 42ECh. 4.1 - Changing means and standard deviations: A small...Ch. 4.2 - In Exercises 5-7, fill in each blank with the...Ch. 4.2 - In Exercises 5-7, fill in each blank with the...Ch. 4.2 - In Exercises 5-7, fill in each blank with the...Ch. 4.2 - Prob. 8ECh. 4.2 - Prob. 9ECh. 4.2 - Prob. 10ECh. 4.2 - Prob. 11ECh. 4.2 - Prob. 12ECh. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - Compute the least-squares regression he for...Ch. 4.2 - Compute the least-squares regression he for...Ch. 4.2 - In a hypothetical study of the relationship...Ch. 4.2 - Assume in a study of educational level in years...Ch. 4.2 - Price of eggs and milk: The following table...Ch. 4.2 - Government funding: The following table presents...Ch. 4.2 - Pass the ball: The following table lists the...Ch. 4.2 - Carbon footprint: Carbon dioxide (CO2) is produced...Ch. 4.2 - Foot temperatures: Foot ulcers are a common...Ch. 4.2 - Mortgage payments: The following table presents...Ch. 4.2 - Blood pressure: A blood pressure measurement...Ch. 4.2 - Butterfly wings: Do larger butterflies live...Ch. 4.2 - Interpreting technology: The following display...Ch. 4.2 - Interpreting technology: The following display...Ch. 4.2 - Interpreting technology: The following MINITAB...Ch. 4.2 - Interpreting technology: The following MINITAB...Ch. 4.2 - Prob. 33ECh. 4.2 - Prob. 34ECh. 4.2 - Least-squares regression line for z-scores: The...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - Prob. 10ECh. 4.3 - Prob. 11ECh. 4.3 - In Exercises 11-14, determine whether the...Ch. 4.3 - Prob. 13ECh. 4.3 - In Exercises 11-14, determine whether the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - Prob. 19ECh. 4.3 - Prob. 20ECh. 4.3 - Prob. 21ECh. 4.3 - Prob. 22ECh. 4.3 - Hot enough for you? The following table presents...Ch. 4.3 - Presidents and first ladies: The presents the ages...Ch. 4.3 - Mutant genes: In a study to determine whether the...Ch. 4.3 - Imports and exports: The following table presents...Ch. 4.3 - Energy consumption: The following table presents...Ch. 4.3 - Cost of health care: The following table presents...Ch. 4.3 - Prob. 29ECh. 4.3 - Prob. 30ECh. 4.3 - Prob. 31ECh. 4.3 - Transforming a variable: The following table...Ch. 4.3 - Prob. 33ECh. 4.3 - Prob. 34ECh. 4 - Compute the correlation coefficient for the...Ch. 4 - The number of theaters showing the movie Monsters...Ch. 4 - Use the data in Exercise 2 to compute the...Ch. 4 - A scatterplot has a correlation of r=1. Describe...Ch. 4 - Prob. 5CQCh. 4 - Prob. 6CQCh. 4 - Use the least-squares regression line computed in...Ch. 4 - Use the least-squares regression line computed in...Ch. 4 - Prob. 9CQCh. 4 - A scatterplot has a least-squares regression line...Ch. 4 - Prob. 11CQCh. 4 - Prob. 12CQCh. 4 - A sample of students was studied to determine the...Ch. 4 - In a scatter-plot; the point (-2, 7) is...Ch. 4 - The correlation coefficient for a data set is...Ch. 4 - Prob. 1RECh. 4 - Prob. 2RECh. 4 - Hows your mileage? Weight (in tons) and fuel...Ch. 4 - Prob. 4RECh. 4 - Energy efficiency: A sample of 10 households was...Ch. 4 - Energy efficiency: Using the data in Exercise 5:...Ch. 4 - Prob. 7RECh. 4 - Prob. 8RECh. 4 - Prob. 9RECh. 4 - Prob. 10RECh. 4 - Baby weights: The average gestational age (time...Ch. 4 - Commute times: Every morning, Tania leaves for...Ch. 4 - Prob. 13RECh. 4 - Prob. 14RECh. 4 - Prob. 15RECh. 4 - Describe an example which two variables are...Ch. 4 - Two variables x and y have a positive association...Ch. 4 - Prob. 3WAICh. 4 - Prob. 4WAICh. 4 - Prob. 5WAICh. 4 - Prob. 6WAICh. 4 - Prob. 7WAICh. 4 - Prob. 8WAICh. 4 - Prob. 9WAICh. 4 - The following table, reproduced from the chapter...Ch. 4 - Prob. 2CSCh. 4 - Prob. 3CSCh. 4 - Prob. 4CSCh. 4 - Prob. 5CSCh. 4 - Prob. 6CSCh. 4 - Prob. 7CSCh. 4 - Prob. 8CSCh. 4 - Prob. 9CSCh. 4 - Prob. 10CSCh. 4 - Prob. 11CSCh. 4 - Prob. 12CSCh. 4 - Prob. 13CSCh. 4 - If we are going to use data from this year to...Ch. 4 - Prob. 15CS
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardCompute the least-squares regression line for predicting the right foot temperature from the left foot temperature. Round the slope and y-Intercept values to four decimal places.arrow_forward
- The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Jan Feb Mar Apr Number of Accidents 25 45 70 95 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): y = 0 + 23.5 x Using least-squares regression, the forecast for the number of accidents that will occur in the month of May = accidents (enter your response as a whole number).arrow_forwardThe weight (in pounds) and height (in inches) for a child were measured every few months over a two-year Using technology, what is the slope of the least-squares regression line and what is its interpretation? period. The results are given in the table. The slope is 1.98, which means for each additional 35 inch in height, the child's weight will increase by 1.98 Weight (x) 8. 12 18 24 30 32 37 40 30 32 33 36 38 pounds. Helght (v) 22 23 26 35 The slope is 1.98, which means for each additional inch in height, the child's weight is predicted to increase by 1.98 pounds. The slope is 0.50, which means for each additional pound in weight, the child's height will increase by 0.5 inches. The slope is 0.50, which means for each additional pound in weight, the child's height is predicted to increase by 0.5 inches.arrow_forwardThe scatterplot below summarizes husbands’ and wives’ heights in a random sample of 170 married couples in Britain, where both partners’ ages are below 65 years. Summary output of the least squares fit for predicting wife’s height from husband’s height is also provided in the table. b) Write the equation of the regression line for predicting wife's height from husband's height and round to 4 decimal places.y =____ + * _____husband's height d) Given that RR = 0.08, what is the value of R2R2 for this data set? Round to 3 decimal placesarrow_forward
- To predict blood alcohol content of a student who drank 5 beers will be about?arrow_forwardThe average gestational age (time from conception to birth) of a newborn infant is about 40 weeks. The following data presents the gestational age in weeks and corresponding mean birth weight in pounds for female infants born in Canada. Find the least-squares regression line for predicting the birth weight from the gestational age. You should be able to grab the data in the table, copy it, and paste it in Excel. You would then select the data in Excel, Insert a Scatter Chart and then under Trendline, check the two boxes at the bottom to display the equation and the R-squared. Gestational Age Birth Weight 36 6.1 37 6.6 38 7.0 39 7.4 40 7.7 41 7.9 42 8.0 43 8.1 A. y= 3.2787x + 15.402 B. y= 15.402x + 3.2787 C. y= 0.2857x - 3.9357 D. Y= 3.9357X + 0.2857arrow_forwardWe have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: regress sales salary . Source Model Residual Total sales salary _cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 t Number of obs F(1, 207) Prob> F R-squared Adj R-squared Root MSE P>|t| 1.74 0.084 5.72 0.000 209 3.02 -.1252934 3757.543 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Suppose a CEO of a company…arrow_forward
- We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…arrow_forwardIsabelle is a crime scene investigator. She found a footprint at the site of a recent murder and believes the footprint belongs to the culprit. To help identify possible suspects, she is investigating the relationship between a person's height and the length of his or her footprint. She consulted her agency's database and found cases in which detectives had recorded the length of people's footprints, x, and their heights (in centimetres), y. The least squares regression line of this data set is: y = 2.488x + 114.001 omplete the following sentence: The least squares regression line predicts that someone whose footprint is one centimetre longer should be centimetres taller.arrow_forwardEach year from 1974 to 1990 the Gallup Polling Organization asked how happy people were in their marriage. The percentage of people that said “very happy” was plotted over time. Use the summary statistics below to compute the least squares regression line to predict the percent of those that are “very happy” based on the year. a) y = 759.0 - 0.3502x b) y = 2098 - 11.79x c) y = -629.3 - 0.3502x d) y = 1866 - 1.790xarrow_forward
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