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Calculus
6th Edition
ISBN: 9781465208880
Author: SMITH KARL J, STRAUSS MONTY J, TODA MAGDALENA DANIELE
Publisher: Kendall Hunt Publishing
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Concept explainers
Question
Chapter 11.7, Problem 31PS
To determine
The least square regression line.
Expert Solution & Answer
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Students have asked these similar questions
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16. Find the least squares regression line for the points (0, 8), (4, 5), (5, 3), (8,-1), and (10,-2). Round numerical
values in your answer to two decimal places.
a. y=-1.07x+2.63
b. y=-1.27x+8.36
c. y=-1.07x+8.36
d.y=-1.07x+10.54
c. y=-1.27x+2.63
5. Write the equation of the least-squares regression line defining any variables used. Round coefficients to 4 decimal places .
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
Chapter 11 Solutions
Calculus
Ch. 11.1 - Prob. 1PSCh. 11.1 - Prob. 2PSCh. 11.1 - Prob. 3PSCh. 11.1 - Prob. 4PSCh. 11.1 - Prob. 5PSCh. 11.1 - Prob. 6PSCh. 11.1 - Prob. 7PSCh. 11.1 - Prob. 8PSCh. 11.1 - Prob. 9PSCh. 11.1 - Prob. 10PS
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Prob. 3PSCh. 11.6 - Prob. 4PSCh. 11.6 - Prob. 5PSCh. 11.6 - Prob. 6PSCh. 11.6 - Prob. 7PSCh. 11.6 - Prob. 8PSCh. 11.6 - Prob. 9PSCh. 11.6 - Prob. 10PSCh. 11.6 - Prob. 11PSCh. 11.6 - Prob. 12PSCh. 11.6 - Prob. 13PSCh. 11.6 - Prob. 14PSCh. 11.6 - Prob. 15PSCh. 11.6 - Prob. 16PSCh. 11.6 - Prob. 17PSCh. 11.6 - Prob. 18PSCh. 11.6 - Prob. 19PSCh. 11.6 - Prob. 20PSCh. 11.6 - Prob. 21PSCh. 11.6 - Prob. 22PSCh. 11.6 - Prob. 23PSCh. 11.6 - Prob. 24PSCh. 11.6 - Prob. 25PSCh. 11.6 - Prob. 26PSCh. 11.6 - Prob. 27PSCh. 11.6 - Prob. 28PSCh. 11.6 - Prob. 29PSCh. 11.6 - Prob. 30PSCh. 11.6 - Prob. 31PSCh. 11.6 - Prob. 32PSCh. 11.6 - Prob. 33PSCh. 11.6 - Prob. 34PSCh. 11.6 - Prob. 35PSCh. 11.6 - Prob. 36PSCh. 11.6 - Prob. 37PSCh. 11.6 - Prob. 38PSCh. 11.6 - Prob. 39PSCh. 11.6 - Prob. 40PSCh. 11.6 - Prob. 41PSCh. 11.6 - Prob. 42PSCh. 11.6 - Prob. 43PSCh. 11.6 - Prob. 44PSCh. 11.6 - Prob. 45PSCh. 11.6 - Prob. 46PSCh. 11.6 - Prob. 47PSCh. 11.6 - Prob. 48PSCh. 11.6 - Prob. 49PSCh. 11.6 - Prob. 50PSCh. 11.6 - 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Prob. 40PSCh. 11.7 - Prob. 41PSCh. 11.7 - Prob. 42PSCh. 11.7 - Prob. 43PSCh. 11.7 - Prob. 44PSCh. 11.7 - Prob. 45PSCh. 11.7 - Prob. 46PSCh. 11.7 - Prob. 47PSCh. 11.7 - Prob. 48PSCh. 11.7 - Prob. 49PSCh. 11.7 - Prob. 50PSCh. 11.7 - Prob. 51PSCh. 11.7 - Prob. 52PSCh. 11.7 - Prob. 53PSCh. 11.7 - Prob. 54PSCh. 11.7 - Prob. 55PSCh. 11.7 - Prob. 56PSCh. 11.7 - Prob. 57PSCh. 11.7 - Prob. 58PSCh. 11.7 - Prob. 59PSCh. 11.7 - Prob. 60PSCh. 11.8 - Prob. 1PSCh. 11.8 - Prob. 2PSCh. 11.8 - Prob. 3PSCh. 11.8 - Prob. 4PSCh. 11.8 - Prob. 5PSCh. 11.8 - Prob. 6PSCh. 11.8 - Prob. 7PSCh. 11.8 - Prob. 8PSCh. 11.8 - Prob. 9PSCh. 11.8 - Prob. 10PSCh. 11.8 - Prob. 11PSCh. 11.8 - Prob. 12PSCh. 11.8 - Prob. 13PSCh. 11.8 - Prob. 14PSCh. 11.8 - Prob. 15PSCh. 11.8 - Prob. 16PSCh. 11.8 - Prob. 17PSCh. 11.8 - Prob. 18PSCh. 11.8 - Prob. 19PSCh. 11.8 - Prob. 20PSCh. 11.8 - Prob. 21PSCh. 11.8 - Prob. 22PSCh. 11.8 - Prob. 23PSCh. 11.8 - Prob. 24PSCh. 11.8 - Prob. 25PSCh. 11.8 - Prob. 26PSCh. 11.8 - Prob. 27PSCh. 11.8 - Prob. 28PSCh. 11.8 - 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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardOlympic 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_forwardIf your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?arrow_forward
- Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable and the number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data is =-114.05+2.17X. On one of the observed days, the temperature was 85 °F and 66 bags of ice were sold. The number of bags of ice predicted to be sold by the LSR line, y, when the temperature is 85 °F is 70. Using the predicted value, compute the residual at this temperature. Answer:arrow_forward5arrow_forwardSuppose the least squares regression line for predicting weight (in pounds) from height (in inches) is given by Weight= -110+3.5*(height) Which of the following statements is correct? l. A person who is 61 inches tall will weigh 103.5 pounds ll. For each additional inch of height, weight will decrease on average by 3.5 pounds. lll. There is a negative linear relationship between height and weight. a) l and lll only b) l and ll only c) ll only d) l only e) ll and lll onlyarrow_forward
- Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (F), as the explanatory variable and the number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data is Bags = -151.05 +2.65Temp. On one of the observed days, the temperature was 82 °F and 68 bags of ice were sold. Determine the number of bags of ice predicted to be sold by the LSR line, Bags, when the temperature is (82\ \text (°F. J\\) Enter your answer as a whole number, rounding if necessary. Bags = 1.11 residual Incorrect Using the predicted value you just found, compute the residual at this temperature. 1.11 Incorrect ice bags ice bagsarrow_forward#3. What is the least squares regression line for the following set of data? y-hat = 2.18x + 27.74 y-hat = 2.14x + 27.44 y-hat = 1.94x + 26.24 y-hat =1.86x + 26.28arrow_forwardA medical experiment on tumor growth gives the following data table. X: 65, 70, 86, 100, 222. Y: 33, 35, 45, 52, 70. The least squares regression line was found. Using technology, it is determined that the total sum of squares (SST) was 898 and the sum of squares of regression (SSR) was 806.2. Calculate R2, rounded to three decimal placesarrow_forward
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Trigonometry
ISBN:9781305652224
Author:Charles P. McKeague, Mark D. Turner
Publisher:Cengage Learning
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Big Ideas Math A Bridge To Success Algebra 1: Stu...
Algebra
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:Houghton Mifflin Harcourt
Algebra & Trigonometry with Analytic Geometry
Algebra
ISBN:9781133382119
Author:Swokowski
Publisher:Cengage
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Linear Algebra: A Modern Introduction
Algebra
ISBN:9781285463247
Author:David Poole
Publisher:Cengage Learning
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College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
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