QUESTION 6Not yet answered Marked out of 7.00 P Flag questionConsider the linear regression model Y; = Bo + B1 X; + U¡ for each iindividual i (measured in $10,000) and Y; represents the home size (measured in square feet). We run an OLS regression and get:1,...,n with n = 1,000. X; represents the annual income of= 43.2, SE( 1m)Bon = 700, SE(.n) = 7.4.= 10.2,Suppose that we want to test Ho : B1O against H1 : ß1 # 0 at 1% significance level. Assuming that the sample size is large enough, whichone of the following is true about the p-value of this test?43.2The p-value can be computed as Ó(--1), where O is the standard Normal CDF10.2а.b. None of the answersc. The p-value can be computed as 20(-|), where is the standard Normal CDFd. The p-value can be computed as (), where O is the standard Normal CDF10.2

# Given : Hypothesis testing H0: beta1=0 , H1: beta1 is not equal zero Beta1^=43.2 & standard…

Answered: Consider the linear regression model Y;…

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.3: Modeling Data With Power Functions

Problem 3TU

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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?
XYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ Corporation in the given month. Month Stock price January 2011 43.71 February 2011 44.22 March 2011 44.44 April 2011 45.17 May 2011 45.97 a. Find the equation of the regression line. Round the regression coefficients to three decimal places. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict the stock price to be in January 2012? January 2013?
Special Rounding Instructions For this exercise set, round all regression parameters to three decimal places, but round all other answers to two decimal places unless otherwise indicated. Postal RatesThe table below shows the cost s, in cents, of a domestic first-class postage stamp in the United States tyears after 1900. t=time,inyearssince1900 s=costofstamp 19 2 32 3 58 4 71 8 78 15 85 22 95 32 102 37 109 44 116 47 a.Use exponential regression to model s as an exponential function of t. b.What cost does your model give for a 1988 stamp? Report your answer to the nearest cent. The actual cost was 25cents. c.Plot the data and the exponential model.
Special Rounding Instructions For this exercise set, round all regression parameters to three decimal places, but round all other answers to two decimal places unless otherwise indicated. Gray Wolves in WisconsinGray wolves were among the first mammals protected under the Endangered Species Act in the 1970s. Wolves recolonized in Wisconsin beginning in 1980.Their population grew reliably after 1985 as follows: Year Wolves Year Wolves 1985 15 1993 40 1986 16 1994 57 1987 18 1995 83 1988 28 1996 99 1989 31 1997 145 1990 34 1998 178 1991 40 1999 197 1992 45 2000 266 a. Explain why an exponential model may be appropriate. b. Are these data exactly exponential? Explain. c. Find an exponential model for these data. d. Plot the data and the exponential model. e. Comment on your graph in part d. Which data points are below or above the number predicted by the exponential model?
Remainder Round all answers to two decimal places unless otherwise indicated. Gross Domestic Product U.S. gross domestic product, in trillions of dollars, is given in the table below. Date Gross domestic product 2010 15.0 2011 15.5 2012 16.2 2013 16.7 a.Find the equation of the regression line, and explain the meaning of its slope. Round regression line parameters to two decimal places. b.Plot the data points and the regression line. c.When would you predict that a gross domestic product of 17.3 trillion dollars would be reached? The actual gross domestic product in 2014 was 17.3 trillion dollars. What does that say about your prediction?
Special Rounding Instructions. For this exercise set, round all regression parameters to three decimal places, but round all other answers to two decimal places unless otherwise indicated. Growth in Length of HaddockA study by Raitt showed that the maximum length that a haddock could be expected to grow is about 53centimeters.Let D=D(t) denote the difference between 53centimeters and the length at age t years. The table below gives experimentally collected values for D. Age t Difference D 2 28.2 5 16.1 7 9.5 13 3.3 19 1.0 a.Find an exponential model of D as a function of t. b.Let L=L(t) denote the length in centimeters of a haddock at age t years. Find the model for L as a function of t. c.Plot the graph of the experimentally gathered data for the length L at ages 2,5,7,13, and 19years along with the graph of the model you made for L. Does this graph show that the 5year old haddock is a bit shorter or a bit longer than would be expected? d.A fisherman has caught a haddock that measures 41centimeters. What is the approximate age of the haddock?
Remainder Round all answers to two decimal places unless otherwise indicated. Domestic Auto Sales in the United States For 2005 through 2008, the following table shows the total U.S. sales, in millions, of domestic automobiles excluding light trucks. Date Domestic cars sold 2005 5.53 2006 5.48 2007 5.25 2008 4.54 a.Get the equation of the regression line rounding parameters to two decimal places, and explain in practical terms the meaning of the slope. In particular, comment on the meaning of the sign of the slope. b.Plot the data points and the regression line. c.In 2009, 3.62 million domestic cars were sold in the United States. How does the forecast obtained from the regression line compare with this figure?
A Dubious Model of Oil Prices The following table shows the prices of oil in U.S. dollars per barrel, t years since 1990, One analysis involving additional data used a cubic equation to model this data. t Years since 1990 0 2 5 7 10 12 15 17 20 21 P Price, dollars per barrel 18.91 16.22 16.63 18.20 27.04 23.47 49.63 69.04 77.46 106.92 a. Use cubic regression to model these data. Round the regression parameters to four decimal places. b. Plot the data along with the cubic model. c. In the analysis mentioned above, the graph is expanded through 2020. Expand the viewing window to show the model from 1990 to 2020. d. What estimate does the model give for oil prices in 2015? e. The actual price of oil in December of 2015 was about 35 per barrel. What basic principle in the use of models would be violated in relying on the estimate in part d?
Zipfs Law The following table shows U.S cities by rank in terms of population and population in thousands. City Rank r Population N New York 1 8491 Chicago 3 2722 Philadelphia 5 1560 Dallas 9 1280 Austin 11 913 San Francisco 13 852 Columbus 15 836 A rule known as Zipfs law tells us that it is reasonable to approximate these data with a power function. a Use power regression to express the population as a function of the rank. b Plot the data along with the power function from part a. c Phoenix is the sixth largest city in the United States. Use your answer from part a to estimate population of Phoenix. Round your answer in thousands to the nearest whole number. Note: The actual population is 1537 thousand.
Remainder Round all answers to two decimal places unless otherwise indicated. Cell Phones The following table gives the amount spent on cellular service worldwide, in trillions of U.S. dollars. Round the regression parameters to three decimal places. Date Cellular service revenue 2011 1.01 2012 1.05 2013 1.09 2014 1.11 a.Plot the data points. b.Find the equation of the regression line and add its graph to the plotted data. c.In 2015, 1.14 trillion was spent on cellular service. If you had been a financial strategist in 2014 with only the data in the table above available, what would been your prediction for the amount spent on cellular service in 2015?
Special Rounding Instructions For this exercise set, round all regression parameters to three decimal places, but round all other answers to two decimal places unless otherwise indicated. Design Patents The following table shows the number P of design patents awarded by the U.S. Patents and Trademark Office from 1950 through 2010. t = years since 1950 P = patents 0 4718 10 2543 20 3214 30 3949 40 8024 50 17,413 60 22,799 a.Use exponential regression to model P as a function of t. b.Plot the data along with the regression equation. c.In what years were there more patents awarded than might be expected from the model?
Special Rounding Instructions. For this exercise set, round all regression parameters to three decimal places, but round all other answers to two decimal places unless otherwise indicated. Caloric Content Versus Shell Length In 1965, Robert T.Paine gathered data on the length L, in millimeters, of the shell and the caloric content C, in calories, for a certain mollusk. The table below is adapted from those data. L=length C=Calories 7.5 92 13 210 20 625 24 1035 31 1480 a.Find an exponential model of calories as a function of length. b.Plot the graph of the data and the exponential model. Which of the data points show a good deal less caloric content than the model would predict for the given length? c.If length is increased by 1millimeter, how is caloric content affected?

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