Introductory Statistics (10th Edition)
10th Edition
ISBN: 9780321989178
Author: Neil A. Weiss
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Textbook Question
Chapter 15.2, Problem 79E
In Exercises 15.70–15.80, use the technology of your choice to do the following tasks.
- a. Decide whether you can reasonably apply the regression t-test. If so, then also do part (b).
- b. Decide, at the 5% significance level, whether the data provide sufficient evidence to conclude that the predictor variable is useful for predicting the response variable.
15.79 Shortleaf Pines. The data from Exercise 15.43 for volume, in cubic feet, and diameter at breast height, in inches, for 70 shortleaf pines are on the WeissStats site.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
A researcher wants to predict the effect of the number of times a person eats every day and the number of times they exercise on BMI. What statistical test would work best?
A. Pearson's R
B. Spearman Rho
C. Linear regression
D. Multiple regression
The data from the table below gives a regression that is
a) reliable.
b) unreliable.
c) unable to determine the reliability.
Provide a detailed interpretation analysis of the regression results below. Pay
attention to the main tested variables, model fit characteristics (R 2 ) and number of
respondents in the sample. State what is dependent and what are independent
variables. Was the hypotheses rejected or confirmed?
a) H1: If a woman has a job she will have less children born
b) H2: The younger a woman got married the more children she has
c) H3: Education positively affects number of children to have
Chapter 15 Solutions
Introductory Statistics (10th Edition)
Ch. 15.1 - Suppose that x and y are predictor and response...Ch. 15.1 - Prob. 2ECh. 15.1 - Prob. 3ECh. 15.1 - Prob. 4ECh. 15.1 - Prob. 5ECh. 15.1 - In Exercises 15.315.6, assume that the variables...Ch. 15.1 - The difference between an observed value and a...Ch. 15.1 - Identify two graphs used in a residual analysis to...Ch. 15.1 - Which graph used in a residual analysis provides...Ch. 15.1 - Figure 15.8 shows three residual plots and a...
Ch. 15.1 - Figure 15.9 on the next page shows three residual...Ch. 15.1 - In Exercises 15.1215.21, we repeat the data and...Ch. 15.1 - In Exercises 15.1215.21, we repeat the data and...Ch. 15.1 - Prob. 14ECh. 15.1 - Prob. 15ECh. 15.1 - Prob. 16ECh. 15.1 - Prob. 17ECh. 15.1 - Prob. 18ECh. 15.1 - Prob. 19ECh. 15.1 - Prob. 20ECh. 15.1 - Prob. 21ECh. 15.1 - Prob. 22ECh. 15.1 - Prob. 23ECh. 15.1 - Prob. 24ECh. 15.1 - Prob. 25ECh. 15.1 - In Exercises 15.2215.27, we repeat the information...Ch. 15.1 - Prob. 27ECh. 15.1 - Prob. 28ECh. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - Prob. 30ECh. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - Prob. 38ECh. 15.1 - Prob. 39ECh. 15.1 - Prob. 40ECh. 15.1 - Prob. 41ECh. 15.1 - Prob. 42ECh. 15.1 - Prob. 43ECh. 15.2 - Explain why the predictor variable is useless as a...Ch. 15.2 - Prob. 45ECh. 15.2 - Prob. 46ECh. 15.2 - In this section, we used the statistic b1 as a...Ch. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - Prob. 49ECh. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - Prob. 52ECh. 15.2 - Prob. 53ECh. 15.2 - Prob. 54ECh. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - Prob. 56ECh. 15.2 - Prob. 57ECh. 15.2 - Prob. 58ECh. 15.2 - In Exercises 15.5815.63, we repeat the information...Ch. 15.2 - Prob. 60ECh. 15.2 - In Exercises 15.5815.63, we repeat the information...Ch. 15.2 - Prob. 62ECh. 15.2 - In Exercises 15.5815.63, we repeat the information...Ch. 15.2 - Prob. 64ECh. 15.2 - In each of Exercises 15.6415.69, apply Procedure...Ch. 15.2 - In each of Exercises 15.6415.69, apply Procedure...Ch. 15.2 - Prob. 67ECh. 15.2 - Prob. 68ECh. 15.2 - Prob. 69ECh. 15.2 - Prob. 70ECh. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - Prob. 73ECh. 15.2 - Prob. 74ECh. 15.2 - Prob. 75ECh. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - Prob. 77ECh. 15.2 - Prob. 78ECh. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - Prob. 80ECh. 15.3 - Without doing any calculations, fill in the blank....Ch. 15.3 - Prob. 82ECh. 15.3 - Prob. 83ECh. 15.3 - Prob. 84ECh. 15.3 - In Exercises 15.8215.91, we repeat the data from...Ch. 15.3 - Prob. 86ECh. 15.3 - Prob. 87ECh. 15.3 - In Exercises 15.8215.91, we repeat the data from...Ch. 15.3 - Prob. 89ECh. 15.3 - Prob. 90ECh. 15.3 - Prob. 91ECh. 15.3 - Prob. 92ECh. 15.3 - In Exercises 15.9215.97, presume that the...Ch. 15.3 - In Exercises 15.9215.97, presume that the...Ch. 15.3 - In Exercises 15.9215.9, presume that the...Ch. 15.3 - Prob. 96ECh. 15.3 - In Exercises 15.9215.97, presume that the...Ch. 15.3 - Prob. 98ECh. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - Prob. 103ECh. 15.3 - Prob. 104ECh. 15.3 - Prob. 105ECh. 15.3 - Prob. 106ECh. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - Prob. 108ECh. 15.3 - Margin of Error in Regression. In Exercises 15.109...Ch. 15.3 - Refer to the confidence interval and prediction...Ch. 15.4 - Identify the statistic used to estimate the...Ch. 15.4 - Prob. 112ECh. 15.4 - Suppose that, for a sample of pairs of...Ch. 15.4 - Prob. 114ECh. 15.4 - Prob. 115ECh. 15.4 - Prob. 116ECh. 15.4 - Prob. 117ECh. 15.4 - Prob. 118ECh. 15.4 - Prob. 119ECh. 15.4 - Prob. 120ECh. 15.4 - Prob. 121ECh. 15.4 - Prob. 122ECh. 15.4 - Prob. 123ECh. 15.4 - Prob. 124ECh. 15.4 - Prob. 125ECh. 15.4 - Prob. 126ECh. 15.4 - Prob. 127ECh. 15.4 - Prob. 128ECh. 15.4 - Prob. 129ECh. 15.4 - Prob. 130ECh. 15.4 - Prob. 131ECh. 15.4 - Prob. 132ECh. 15.4 - Prob. 133ECh. 15.4 - In each of Exercises 15.13415.144, use the...Ch. 15.4 - In each of Exercises 15.13415.144, use the...Ch. 15.4 - Prob. 136ECh. 15.4 - Prob. 137ECh. 15.4 - Prob. 138ECh. 15.4 - Prob. 139ECh. 15.4 - Prob. 140ECh. 15.4 - In each of Exercises 15.13415.144, use the...Ch. 15.4 - Prob. 142ECh. 15.4 - Prob. 143ECh. 15.4 - Prob. 144ECh. 15 - Prob. 1RPCh. 15 - Suppose that x and y are two variables of a...Ch. 15 - What two plots did we use in this chapter to...Ch. 15 - Regarding analysis of residuals, decide in each...Ch. 15 - Suppose that you perform a hypothesis test for the...Ch. 15 - Prob. 6RPCh. 15 - Prob. 7RPCh. 15 - Prob. 8RPCh. 15 - Prob. 9RPCh. 15 - Identify the relationship between two variables...Ch. 15 - Graduation Rates. Graduation ratethe percentage of...Ch. 15 - Prob. 12RPCh. 15 - Prob. 13RPCh. 15 - For Problems 1417, presume that the variables...Ch. 15 - For Problems 1417, presume that the variables...Ch. 15 - For Problems 1417, presume that the variables...Ch. 15 - Prob. 17RPCh. 15 - In Problems 1820, use the technology of your...Ch. 15 - In Problems 1820, use the technology of your...Ch. 15 - In Problems 1820, use the technology of your...Ch. 15 - Recall from Chapter 1 (see page 34) that the Focus...Ch. 15 - At the beginning of this chapter, we presented...
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
- Price ($/gal) Demand (million of gal.) 1 790 1.2 700 1.4 640 1.6 580 1.95 497 2.2 450 2.4 430 2.6 420 2.8 390 3 360 Note: there is some randomization in the above data to account for price fluctuations. Make sure to check that you input the correct data in your device. Perform the following work • Assume that Supply has a quadratic relationship with the price. Find this relationship (the help buttons contain an article to compute trend-lines in Excel): -21.935p² + 207.365p+339.085 Round your answer to 3 decimal places S(p) - Supply (million of gal.) 511 550 600 641 660 680 700 720 735 786 • Assume that the Demand has a quadratic relationship with the price. Find this relationship (the help button links to an article to compute trend-lines in Excel): 85.561p2 - 543.789p + 1236.729 Round your answer to 3 decimal places D(p) = 1.65 Use the trendlines to find the price corresponding to the equlibrium price between supply and demand: X $ per gallon Round your answer to 2 decimal placesarrow_forwardIn doing regression analysis in MS Excel, what are the three sets of data you see from Regression result? Select all that apply. A. Probability Output B. Residual Output C. ANOVA D. Summary Output E. OVERALL Fitarrow_forwardResearchers are interested in whether or not shoe size could predict height. Data was collected from 25 students. Students reported their height and shoe size. (Source: Intro to Statistics by Gould & Ryan). Height = 51.46 + 1.728 (Shoe Size) 74 72 5 6 7 8 9 10 11 12 13 Shoe Size a. From the scatterplot points, estimate a range of heights for students with a size 8 shoe. b. Use the equation of the regression line to calculate the predicted height of a student with a size 8 shoe. Do not round your answer. c. The data values are fairly close to the line, so we can say we there is a moderate association. Can we be very confident, moderately confident, or not at all confident that this prediction is close to the actual mileage? Explain. Height (in inches)arrow_forward
- The data used to fit the model in Exercise 1 are presented in the following table, along with the residuals and the fitted values. Plot the residuals versus the fitted values. Does the plot indicate that the linear model is reasonable? Explain. Strength Manganese Thickness Residual Fitted Value 47.7 7.4 8.0 -0.111 47.811 50.9 8.8 10.0 -0.709 51.609 51.7 8.8 10.0 0.091 51.609 51.9 8.8 10.0 0.291 51.609 50.0 8.1 7.1 -0.517 50.517 50.5 8.1 9.0 0.790 49.710 50.0 8.1 7.1 -0.517 50.517 49.7 8.1 9.0 -0.010 49.710 50.6 8.1 9.0 0.890 49.710 47.7 7.2 7.2 0.214 47.486 47.1 7.3 7.8 -0.464 47.564 45.0 7.3 11.8 -0.864 45.864 47.6 7.3 8.0 0.121 47.479 45.7 7.3 11.8 -0.164 45.864 47.0 7.3 8.7 -0.181 47.181 45.7 7.3 11.7 -0.206 45.906 48.8 7.3 8.7 1.619 47.181 45.8 7.3 7.8 -1.764 47.564 48.5 7.3 9.0 1.446 47.054 48.6 7.6 7.8 0.040 48.560arrow_forwardIn Exercises, assume that the variables under consideration satisfy the assumptions for regression inferences. Based on a sample of data points, what is the best estimate of the population regression line?arrow_forwardWaterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the nearest fire station. This information will be used in setting rates for insurance coverage. For a sample of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire damage, in thousands of dollars (y). The MegaStat output is reported below. ANOVA table Source SS df MS F Regression 1,865.5782 1 1,865.5782 39.56 Residual 1,320.4934 28 47.1605 Total 3,186.0716 29 Regression output Variables Coefficients Std. Error t(df=28) Intercept 13.7523 3.0957 3.672 Distance–X 6.3449 1.0088 6.29 a-1. Write out the regression equation. (Round your answers to 3 decimal places.) a-2. Is there a direct or indirect relationship between the distance from the fire station and the amount of fire damage? How much damage would…arrow_forward
- Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the nearest fire station. This information will be used in setting rates for insurance coverage. For a sample of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire damage, in thousands of dollars (y). The MegaStat output is reported below. ANOVA table Source SS df MS F Regression 1,850.5782 1 1,850.5782 39.69 Residual 1,305.4934 28 46.6248 Total 3,156.0716 29 Regression output Variables Coefficients Std. Error t(df=28) Intercept 12.6882 3.1699 2.82 Distance–X 5.121 8.504 6.3 c-1. Determine the coefficient of determination. (Round your answer to 3 decimal places.) c-2. Fill in the blank below. (Round your answer to one decimal place.) d-1. Determine the correlation coefficient. (Round…arrow_forwardWaterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the nearest fire station. This information will be used in setting rates for insurance coverage. For a sample of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire damage, in thousands of dollars (y). The MegaStat output is reported below. ANOVA table Source SS df MS F Regression 1,815.5782 1 1,815.5782 40.82 Residual 1,245.4934 28 44.4819 Total 3,061.0716 29 Regression output Variables Coefficients Std. Error t(df=28) Intercept 13.3334 3.0617 3.556 Distance–X 4.7711 0.7468 6.39 Click here for the Excel Data File a-1. Write out the regression equation. (Round your answers to 3 decimal places.) a-2. Is there a direct or indirect relationship between the distance from the fire station and…arrow_forwardWaterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the nearest fire station. This information will be used in setting rates for insurance coverage. For a sample of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire damage, in thousands of dollars (y). The MegaStat output is reported below. ANOVA table Source SS df MS F Regression 1,850.5782 1 1,850.5782 39.69 Residual 1,305.4934 28 46.6248 Total 3,156.0716 29 Regression output Variables Coefficients Std. Error t(df=28) Intercept 12.6882 3.1699 2.82 Distance–X 5.121 8.504 6.3 Click here for the Excel Data File a-1. Write out the regression equation. (Round your answers to 3 decimal places.) a-2. Is there a direct or indirect relationship between the distance from the fire station and…arrow_forward
- Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the nearest fire station. This information will be used in setting rates for insurance coverage. For a sample of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire damage, in thousands of dollars (y). The MegaStat output is reported below. ANOVA table Source SS df MS F Regression 1,850.5782 1 1,850.5782 39.69 Residual 1,305.4934 28 46.6248 Total 3,156.0716 29 Regression output Variables Coefficients Std. Error t(df=28) Intercept 12.6882 3.1699 2.82 Distance–X 5.121 8.504 6.3 Click here for the Excel Data File d-1. Determine the correlation coefficient. (Round your answer to 3 decimal places.) d-2. Choose the right option. d-3. How did you determine the sign of the correlation…arrow_forwardWaterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the nearest fire station. This information will be used in setting rates for insurance coverage. For a sample of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire damage, in thousands of dollars (y). The MegaStat output is reported below. ANOVA table Source SS df MS F Regression 1,850.5782 1 1,850.5782 39.69 Residual 1,305.4934 28 46.6248 Total 3,156.0716 29 Regression output Variables Coefficients Std. Error t(df=28) Intercept 12.6882 3.1699 2.82 Distance–X 5.121 8.504 6.3 e-1. State the decision rule for 0.01 significance level: H0 : ρ = 0; H1 : ρ ≠ 0. (Negative value should be indicated by a minus sign. Round your answers to 3 decimal places.) e-2. Compute the value of the test…arrow_forwarda) Write out the regression equation. b) Fill in the missing values *, **, *** and ****. c) Use the p-value approach to determine if ? is significant at the 5% significance levelarrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw Hill
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
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