Introduction To Statistics And Data Analysis
6th Edition
ISBN: 9781337793612
Author: PECK, Roxy.
Publisher: Cengage Learning,
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Question
Chapter 14.3, Problem 48E
a.
To determine
Check whether the given model is useful for predicting the maximum heart rate at the 0.01 level of significance.
b.
To determine
Calculate the 95% confidence interval for
c.
To determine
Estimate the maximum heart rate using the 99% interval.
d.
To determine
Calculate the estimated maximum heart rate using the 90% interval.
e.
To determine
Check whether the 90% prediction interval is to be wider or narrower than the interval computed in Part (d).
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
A particular article presented data on y = tar content (grains/100 ft³) of a gas stream as a function of x₁ = rotor speed (rev/min) and x₂ = gas
inlet temperature (°F). The following regression model using X₁, X2, X3 = ×₂² and ×4 = X₁X₂ was suggested.
(mean y value) = 86.5 – 0.121x₁ +5.07x2 - 0.0706x3 + 0.001x4
(a) According to this model, what is the mean y value (in grains/100 ft³) if x₁ = 3,400 and x₂ = 55.
grains/100 ft³
(b) For this particular model, does it make sense to interpret the value of ₂ as the average change in tar content associated with a 1-degree
increase in gas inlet temperature when rotor speed is held constant? Explain.
Yes, since there are no other terms involving X2.
O Yes, since there are other terms involving X₂.
● No, since there are other terms involving X2.
O No, since there are no other terms involving X2.
The cotton aphid poses a threat to cotton crops
in Iraq. The accompanying data on
y = infestation rate (aphids/100 leaves)
X1 = mean temperature (°C)
x, = mean relative humidity
appeared in the article “Estimation of the Economic
Threshold of Infestation for Cotton Aphid" (Mesopotamia
Journal of Agriculture [1982]: 71–75). Use the data to find
the estimated regression equation and assess the utility of
the multiple regression model
y = a + Bjx1 + Bx2 + e
y
X1
X2
y
X1
X2
61
21.0
57.0
77
24.8
48.0
87
28.3
41.5
93
26.0
56.0
98
27.5
58.0
100
27.1
31.0
104
26.8
36.5
118
29.0
41.0
102
28.3
40.0
74
34.0
25.0
63
30.5
34.0
43
28.3
13.0
27
30.8
37.0
19
31.0
19.0
14
33.6
20.0
23
31.8
17.0
30
31.3
21.0
25
33.5
18.5
67
33.0
24.5
40
34.5
16.0
34.3
6.0
21
34.3
26.0
18
33.0
21.0
23
26.5
26.0
42
32.0
28.0
56
27.3
24.5
60
27.8
39.0
59
25.8
29.0
82
25.0
41.0
89
18.5
53.5
77
26.0
51.0
102
19.0
48.0
108
18.0
70.0
97
16.3
79.5
Given: significance level = 0.05
Required:
1. Regression Equation
2. F and…
The estimated least-squares regression equation for profit (in $100,000) is: y' = 2.5 + 0.054t
Sales for time period 32 (t = 32) are 5% below the average of yearly sales.
Forecast sales, including the seasonal component, for t = 32.
Chapter 14 Solutions
Introduction To Statistics And Data Analysis
Ch. 14.1 - Prob. 1ECh. 14.1 - The authors of the paper Weight-Bearing Activity...Ch. 14.1 - Prob. 3ECh. 14.1 - Prob. 4ECh. 14.1 - Prob. 5ECh. 14.1 - Prob. 6ECh. 14.1 - Prob. 7ECh. 14.1 - Prob. 8ECh. 14.1 - Prob. 9ECh. 14.1 - The relationship between yield of maize (a type of...
Ch. 14.1 - Prob. 11ECh. 14.1 - A manufacturer of wood stoves collected data on y...Ch. 14.1 - Prob. 13ECh. 14.1 - Prob. 14ECh. 14.1 - Prob. 15ECh. 14.2 - Prob. 16ECh. 14.2 - State as much information as you can about the...Ch. 14.2 - Prob. 18ECh. 14.2 - Prob. 19ECh. 14.2 - Prob. 20ECh. 14.2 - The ability of ecologists to identify regions of...Ch. 14.2 - Prob. 22ECh. 14.2 - Prob. 23ECh. 14.2 - Prob. 24ECh. 14.2 - Prob. 25ECh. 14.2 - Prob. 26ECh. 14.2 - This exercise requires the use of a statistical...Ch. 14.2 - Prob. 28ECh. 14.2 - The article The Undrained Strength of Some Thawed...Ch. 14.2 - Prob. 30ECh. 14.2 - Prob. 31ECh. 14.2 - Prob. 32ECh. 14.2 - Prob. 33ECh. 14.2 - This exercise requires the use of a statistical...Ch. 14.2 - This exercise requires the use of a statistical...Ch. 14.3 - Prob. 36ECh. 14.3 - Prob. 37ECh. 14.3 - When Coastal power stations take in large amounts...Ch. 14.3 - Prob. 39ECh. 14.3 - The article first introduced in Exercise 14.28 of...Ch. 14.3 - Data from a random sample of 107 students taking a...Ch. 14.3 - Benevolence payments are monies collected by a...Ch. 14.3 - Prob. 43ECh. 14.3 - Prob. 44ECh. 14.3 - Prob. 45ECh. 14.3 - Prob. 46ECh. 14.3 - Exercise 14.26 gave data on fish weight, length,...Ch. 14.3 - Prob. 48ECh. 14.3 - Prob. 49ECh. 14.3 - Prob. 50ECh. 14.4 - Prob. 51ECh. 14.4 - Prob. 52ECh. 14.4 - The article The Analysis and Selection of...Ch. 14.4 - Prob. 54ECh. 14.4 - Prob. 55ECh. 14.4 - Prob. 57ECh. 14.4 - Prob. 58ECh. 14.4 - Prob. 59ECh. 14.4 - Prob. 60ECh. 14.4 - This exercise requires use of a statistical...Ch. 14.4 - Prob. 62ECh. 14 - Prob. 63CRCh. 14 - Prob. 64CRCh. 14 - The accompanying data on y = Glucose concentration...Ch. 14 - Much interest in management circles has focused on...Ch. 14 - Prob. 67CRCh. 14 - Prob. 68CRCh. 14 - Prob. 69CRCh. 14 - A study of pregnant grey seals resulted in n = 25...Ch. 14 - Prob. 71CRCh. 14 - Prob. 72CRCh. 14 - This exercise requires the use of a statistical...
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- A. Do these data provide sufficient evidence that there is a positive linear relationship between the two variables? B. What does R^2 imply? C. Using the regression model, predict the blood pressure level associated with a sound pressure of 7.5 decibels.arrow_forwardA particular article presented data on y = tar content (grains/100 ft3) of a gas stream as a function of x1 = rotor speed (rev/min) and x2 = gas inlet temperature (°F). The following regression model using x1, x2, x3 = x22 and x4 = x1x2 was suggested. (mean y value) = 86.5 − 0.124x1 + 5.07x2 − 0.0708x3 + 0.001x4 a.) According to this model, what is the mean y value (in grains/100 ft3) if x1 = 3,200 and x2 = 55. grains/100 ft3arrow_forwardThe following table gives the ages of female students in school and the corresponding Body Mass index (BMI) of 8 randomly selected students.(α=0.05) Age24223032211925 BMI27283029282726 Determine whether ages and BMI are significantly related.Determine the coefficient of linear determination and Intepret.Set up a linear regression equation to predict body mass index from age of students.Predict the body mass index of student who has the age of 26 years.arrow_forward
- also compute the regression equation in which you predict Y using X as the predictor variablearrow_forwardAssume a person got score of 32.5 on Test A and a score of 95.25 on Test B. Using the regression equation (B' = 2.3A + 9.5), what is the error of prediction for this person?arrow_forwardThe relationship between yield of maize, date of planting, and planting density was investigated in an article. Let the variables be defined as follows. y = percent maize yield x = planting date (days after April 20) z = planting density (plants/ha) The following regression model with both quadratic terms where x₁ = x, X₂ = Z, X3 = x² and x4 = 2² provides a good description of the relationship between y and the independent variables. y =a +B₁x₁ + B₂X₂ + B3X3+B₁x₁ + e (a) If a = 21.07, B₁ = 0.653, B₂ = 0.0022, B3 = -0.0207, and B4 = 0.00002, what is the population regression function? y = 509 X (b) Use the regression function in Part (a) to determine the mean yield for a plot planted on May 7 with a density of 41,182 plants/ha. (Give the exact answer.) (c) Would the mean yield be higher for a planting date of May 7 or May 23 (for the same density)? The mean yield would be higher for [May 7 You may need to use the appropriate table in Appendix A to answer this question.arrow_forward
- A) A linear regression has a =6 and b=5 what is y predicted as when x=9? B) A linear regression has b=3 and a=4.What is the predicted Y for x=7?arrow_forwardA trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is Y = -0.800+ 0.060x₁ +0.900x₂ + e (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? hr (b) How would you interpret ₁ = 0.060, the coefficient of the predictor x₁? O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e.…arrow_forwardData was gathered from a random sample of young mothers between the ages of 15 and 19 years, and the relationship between the mother's age (measured in years) and the baby's birth weight (measured in grams) was observed to be linear, with r= 0.88. Further, the regression equation to predict a baby's birth welght based on the mother's age was found to be: Predicted birth weight = -1163.45 + 245.15(age). Based on this information, which one of the following statements is incorrect? O The predicted birth weight for a baby whose mother is 15 years old is 4840.7 grams. O If weight was measured in pounds instead of grams, the value of rwould still be 0.88. O If we switch the variables so that age is the response variable and birth weight is the explanatory variable, r would still be 0,88. O If a mother's age is 25 years, we would not want to use the regression equation to predict the baby's birth weight since this would be considered extrapolation. O Becauseris 0.88, we would consider the…arrow_forward
- The equation used to predict college GPA (range 0-4.0) is y = 0.17 +0.51x, +0.002x,, where x, is high school GPA (range 0-4.0) and x, is college board score (range 200-800). Use the multiple regression equation to predict college GPA for a high school GPA of 3.8 and a college board score of 500. The predicted college GPA for a high school GPA of 3.8 and a college board score of 500 is (Round to the nearest tenth as needed.)arrow_forwardRun a simple linear regression in SPSS to know if previous experience (‘prevexp’: Previous Experience-months) significantly predicts current salary(‘salary’: Current Salary) in the work force . Use α =.05 Does Previous Experience significantly predict Current Salary? Report Beta(β), and the p-value (p).arrow_forwardhe following estimated regression model was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).ŷ = 30 + 0.7x1 + 3x2Also provided are SST = 1200 and SSE = 384. The yearly income of a 24-year-old female individual isarrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons Inc
Probability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage Learning
Statistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning
Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSON
The Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. Freeman
Introduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman
MATLAB: An Introduction with Applications
Statistics
ISBN:9781119256830
Author:Amos Gilat
Publisher:John Wiley & Sons Inc
Probability and Statistics for Engineering and th...
Statistics
ISBN:9781305251809
Author:Jay L. Devore
Publisher:Cengage Learning
Statistics for The Behavioral Sciences (MindTap C...
Statistics
ISBN:9781305504912
Author:Frederick J Gravetter, Larry B. Wallnau
Publisher:Cengage Learning
Elementary Statistics: Picturing the World (7th E...
Statistics
ISBN:9780134683416
Author:Ron Larson, Betsy Farber
Publisher:PEARSON
The Basic Practice of Statistics
Statistics
ISBN:9781319042578
Author:David S. Moore, William I. Notz, Michael A. Fligner
Publisher:W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:9781319013387
Author:David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:W. H. Freeman
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY