Introduction to Statistics and Data Analysis
5th Edition
ISBN: 9781305115347
Author: Roxy Peck; Chris Olsen; Jay L. Devore
Publisher: Brooks Cole
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Chapter 13, Problem 58CR
a.
To determine
Estimate the population regression line.
b.
To determine
Compute the estimated standard deviation,
Conduct a test to check whether the y intercept of the population regression line differs from zero.
c.
To determine
Compute the 95% confidence interval for
Check whether the result indicates that
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For a given set of x and y data values, assume that the regression model assumptions are valid and that a 90% confidence interval for ₁ is given by (-2.2, -0.1).
Which of the following statements are true?
i) At a = 0.10, there is a significant linear relationship between x and y.
ii) In the scatterplot of x and y, the values of y tend to decrease as the values of x increase.
iii) Based on this confidence interval, we would reject the null hypothesis of no linear relationship at any significance level a ≤ 0.1.
Select one:
a. i)
O b. i) and ii)
O c. ii)
d. i), ii), and iii)
The equation Y= α+ βx+ε describes a population regression line. Which of the statements regarding this equation is true.
A. ε is the coefficient of standard deviation
B. α,β are sample statistics that can be calculated by the ordinary least squares method
C. α,β are usually unknown since they are population quantities
D. None of the statements above are true
In simple linear regression, at what value of the independent variable, X, will the 95% confidence interval for the average value of Y be narrowest? At what value will the 95% prediction interval for the value of Y for a sin gle new observation be narrowest?
Chapter 13 Solutions
Introduction to Statistics and Data Analysis
Ch. 13.1 - Prob. 1ECh. 13.1 - The flow rate in a device used for air quality...Ch. 13.1 - The paper Predicting Yolk Height, Yolk Width,...Ch. 13.1 - Prob. 4ECh. 13.1 - Suppose that a simple linear regression model is...Ch. 13.1 - a. Explain the difference between the line y x...Ch. 13.1 - Prob. 7ECh. 13.1 - Hormone replacement therapy (HRT) is thought to...Ch. 13.1 - Prob. 9ECh. 13.1 - A simple linear regression model was used to...
Ch. 13.1 - Consider the accompanying data on x = Advertising...Ch. 13.2 - What is the difference between and b? What is the...Ch. 13.2 - The largest commercial fishing enterprise in the...Ch. 13.2 - Prob. 14ECh. 13.2 - Prob. 15ECh. 13.2 - Prob. 16ECh. 13.2 - An experiment to study the relationship between x...Ch. 13.2 - The paper The Effects of Split Keyboard Geometry...Ch. 13.2 - The authors of the paper Decreased Brain Volume in...Ch. 13.2 - Do taller adults make more money? The authors of...Ch. 13.2 - Researchers studying pleasant touch sensations...Ch. 13.2 - Prob. 22ECh. 13.2 - Prob. 23ECh. 13.2 - Consider the accompanying data on x = Research and...Ch. 13.2 - Prob. 25ECh. 13.2 - In anthropological studies, an important...Ch. 13.3 - The graphs accompanying this exercise are based on...Ch. 13.3 - Prob. 28ECh. 13.3 - Prob. 29ECh. 13.3 - The article Vital Dimensions in Volume Perception:...Ch. 13.3 - Prob. 31ECh. 13.3 - An investigation of the relationship between x =...Ch. 13.4 - Prob. 33ECh. 13.4 - Prob. 34ECh. 13.4 - Prob. 35ECh. 13.4 - Prob. 36ECh. 13.4 - A subset of data read from a graph that appeared...Ch. 13.4 - Prob. 38ECh. 13.4 - Prob. 39ECh. 13.4 - Prob. 40ECh. 13.4 - The shelf life of packaged food depends on many...Ch. 13.4 - For the cereal data of the previous exercise, the...Ch. 13.4 - The article Performance Test Conducted for a Gas...Ch. 13.5 - Prob. 44ECh. 13.5 - Prob. 45ECh. 13.5 - A sample of n = 353 college faculty members was...Ch. 13.5 - Prob. 47ECh. 13.5 - Prob. 48ECh. 13.5 - The accompanying summary quantities for x =...Ch. 13.5 - Prob. 50ECh. 13.5 - Prob. 51ECh. 13.6 - Prob. 52ECh. 13 - Prob. 53CRCh. 13 - Prob. 54CRCh. 13 - Prob. 55CRCh. 13 - The article Photocharge Effects in Dye Sensitized...Ch. 13 - Prob. 57CRCh. 13 - Prob. 58CRCh. 13 - Prob. 59CRCh. 13 - Prob. 60CRCh. 13 - Prob. 61CRCh. 13 - The article Improving Fermentation Productivity...Ch. 13 - Prob. 63CRCh. 13 - Prob. 64CRCh. 13 - Prob. 65CRCh. 13 - Prob. 1CRECh. 13 - Prob. 2CRECh. 13 - Prob. 3CRECh. 13 - Prob. 4CRECh. 13 - Prob. 5CRECh. 13 - The accompanying graphical display is similar to...Ch. 13 - Prob. 7CRECh. 13 - Prob. 8CRECh. 13 - Consider the following data on y = Number of songs...Ch. 13 - Many people take ginkgo supplements advertised to...Ch. 13 - Prob. 11CRECh. 13 - Prob. 12CRECh. 13 - Prob. 13CRECh. 13 - Prob. 14CRECh. 13 - The discharge of industrial wastewater into rivers...Ch. 13 - Many people take ginkgo supplements advertised to...Ch. 13 - It is hypothesized that when homing pigeons are...Ch. 13 - Prob. 18CRE
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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_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forward
- Suppose we perform a simple linear regression (SLR) of two variables F1 and F2 against the same out variable (say y). The r-squared value/score of both the SLRS are 0.9678 and 0.95123 respectively for F1 and F2. Then what can you say about the correlation? O a. y is more dependent on F2 than F1 O b. None of these O . The result does not say that y is more dependent on F1 OR F2 O d.y is more dependent on F1 than F2arrow_forwardSuppose we are making predictions of the dependent variable y for specific values of the independent variable x using a simple linear regression model holding the confidence level constant. Let Width (C.I) = the width of the confidence interval for the average value y for a given value of x, and Width (P.I) = the width of the prediction interval for a single value y for a given value of x. Which of the following statements is true? Width (C.I) = 0.5 Width (P.I) Width (C.I) = Width (P.I) Width (C.I) > Width (P.I) Width (C.I) < Width (P.I)arrow_forwardA researcher has developed a regression model from fourteen pairs of data points. He wants to test to determine if the slope is significantly different from zero. He uses a two-tailed test and a = 0.01. The critical tablet value is 2.718 3.012 2.650 O 3.055 O 2.168arrow_forward
- 2. The instructor of a mathematics class collected data to see whether there is a correlation between the number of absences (X) and the student's score on the final exam (Y). The number of absences and score on the final exam were recorded. The following regression equation was obtained: Final score = 92.5317 – 3.7611 (Absences) a) (5 points) What would be the predicted final exam score for a student that had 10 absences? b) (5 points) The student from part (a) actually scored a 60 on the final exam. What is the residual for this student? Show all work.arrow_forwardConsider a regression analysis with n = 47 and three potential independent variables. Suppose that one of the independent variables has a correlation of 0.95 with the dependent variable. Does this imply that this independent variable will have a very large Student’s t statistic in the regression analysis with all three predictor variables?arrow_forwardA set of n = 20 pairs of scores produces a Pearson correlation of r = 0.40 with SS, = 100. What is the standard error of estimate for the regression equation? O√16/18 O √16/1 O √84/18 O √84/1arrow_forward
- 2. We performed a linear regression using 25 observations. From the regression output we find that bo = 5.7, bị = 12.9, x = 11.4, Sz = 3.2 and MSE = 12.25. a. From the least squares line, what is the predicted response when x* = 10.65? y = b. What is the 95% confidence interval for the mean response when x* = 10.65? c. What is the 95% prediction interval for an individual response when x* = 10.65? d. Which interval is wider? The confidence interval or the prediction interval? O a. Confidence Interval b. Prediction Intervalarrow_forwardConsider the following regression equation: Y, = Po + B,X, + Hip where X;, Y, þPo, B1, and u, denote the regressor, the regressand, the intercept coefficient, the slope coefficient, and the error term for the " observation, respectively. When would the error term be homoskedastic? A. The error term is homoskedastic if the variance of the conditional distribution of u, given X is constant for i= 1..n, and in particular does not depend on X, B. The error term is homoskedastic if the variance of the joint distribution of u, and Y, is constant for i= 1. n and in particular does not depend on Y- O C. The error term is homoskedastic if the variance of the conditional distribution of u, given Y is constant for i= 1.n, and in particular does not depend on Y O D. The error term is homoskedastic if the variance of the conditional distribution of u given X, is variable for i=1. n, and in particular depends on Xj. Which of the following statements describes the mathematical implications of…arrow_forwardplease also answer these questions correctly for allarrow_forward
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