Statistics for Business and Economics (13th Edition)
13th Edition
ISBN: 9780134506593
Author: James T. McClave, P. George Benson, Terry Sincich
Publisher: PEARSON
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Textbook Question
Chapter 14.9, Problem 14.55ACI
Price of natural gas. Refer to the annual data on natural gas price (Yt), Exercise 14.42 (p. 14-37). You fit the simple linear regression model, E (Yt) = β0 + β1t, to the data for the years 1997 to 2015 (t = l, 2, ... , 19).
a. Find and plot the regression residuals against t. Does the plot suggest the presence of autocorrelation? Explain.
b. Conduct the Durbin-Watson test (at α = .05) to test formally for the presence of positively autocorrelated regression errors.
c. Comment on the validity of the inference concerning model adequacy in light of the result of part b.
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Statistics for Business and Economics (13th Edition)
Ch. 14.1 - Explain in words how to construct a simple index.Ch. 14.1 - Explain in words how to calculate the following...Ch. 14.1 - Explain in words the difference between Laspeyres...Ch. 14.1 - The table below gives the prices for three...Ch. 14.1 - Refer to Exercise 14.4. The next table gives the...Ch. 14.1 - Annual median family income. The table below lists...Ch. 14.1 - Annual U.S. craft beer production. While overall...Ch. 14.1 - Quarterly single-family housing starts. The...Ch. 14.1 - Spot price of natural gas. The table shown in the...Ch. 14.1 - Employment in farm and nonfarm categories....
Ch. 14.1 - GOP personal consumption expenditures. The gross...Ch. 14.1 - GDP personal consumption expenditures (contd)....Ch. 14.1 - Weekly earnings for workers. The table in the next...Ch. 14.1 - Production and price of metals. The level or price...Ch. 14.2 - Describe the effect of selecting an exponential...Ch. 14.2 - A monthly time series is shown in the table to the...Ch. 14.2 - Annual U.S. craft beer production. Refer to the...Ch. 14.2 - Foreign fish production. Overfishing and pollution...Ch. 14.2 - Yearly price of gold. The price of gold is used by...Ch. 14.2 - Personal consumption in transportation. There has...Ch. 14.2 - OPEC crude oil imports. The data in the table...Ch. 14.2 - SP 500 Stock Index. Standard Poors 500 Composite...Ch. 14.5 - How does the choice of the smoothing constant w...Ch. 14.5 - Refer to Exercise 14.4 (p. 14-9). The table with...Ch. 14.5 - Annual U.S. craft beer production. Refer to...Ch. 14.5 - Quarterly single-family housing starts. Refer to...Ch. 14.5 - Consumer Price Index. The CPI measures the...Ch. 14.5 - OPEC crude oil imports. Refer to the annual OPEC...Ch. 14.5 - SP 500 Stock Index. Refer to the quarterly...Ch. 14.5 - SP 500 Stock Index (contd). Refer to Exercise...Ch. 14.5 - Monthly gold prices. The fluctuation of gold...Ch. 14.6 - Annual U.S. craft beer production. Refer to the...Ch. 14.6 - Annual U.S. craft beer production (contd). Refer...Ch. 14.6 - SP 500 Stock Index. Refer to your exponential...Ch. 14.6 - SP 500 Stock Index (contd). Refer to your Holt...Ch. 14.6 - Monthly gold prices. Refer to the monthly gold...Ch. 14.6 - US school enrollments. The next table reports...Ch. 14.8 - The annual price of a finished product (in cents...Ch. 14.8 - Retail sales in Quarters 14 over a 10-year period...Ch. 14.8 - What advantage do regression forecasts have over...Ch. 14.8 - Mortgage interest rates. The level at which...Ch. 14.8 - Price of natural gas. Refer to Exercise 14.9 (p....Ch. 14.8 - A gasoline tax on carbon emissions. In an effort...Ch. 14.8 - Predicting presidential elections. Researchers at...Ch. 14.8 - Life insurance policies in force. The table below...Ch. 14.8 - Graphing calculator sales. The next table presents...Ch. 14.8 - Prob. 14.47ACICh. 14.9 - Define autocorrelation. Explain why it is...Ch. 14.9 - For each case, indicate the decision regarding the...Ch. 14.9 - What do the following Durbin-Watson statistics...Ch. 14.9 - Company donations to charity. Refer to the Journal...Ch. 14.9 - Forecasting monthly car and truck sales. Forecasts...Ch. 14.9 - Predicting presidential elections. Refer to the...Ch. 14.9 - Mortgage interest rates. Refer to the data on...Ch. 14.9 - Price of natural gas. Refer to the annual data on...Ch. 14.9 - Life insurance policies in force. Refer to the...Ch. 14.9 - Modeling the deposit share of a retail bank....Ch. 14 - Insured Social Security workers. Workers insured...Ch. 14 - Insured Social Security workers (contd). Refer to...Ch. 14 - Retail prices of food items. In 1990, the average...Ch. 14 - Demand for emergency room services. With the...Ch. 14 - Mortgage interest rates. Refer to the annual...Ch. 14 - Price of Abbott Labs stock. The yearly closing...Ch. 14 - Price o f Abbott Labs stock (contd). Refer to...Ch. 14 - Prob. 14.65ACICh. 14 - Prob. 14.66ACICh. 14 - Quarterly GOP values (contd). Refer to Exercise...Ch. 14 - Prob. 14.68ACICh. 14 - Prob. 14.69ACICh. 14 - Prob. 14.70ACICh. 14 - IBM stock prices. Refer to Example 14.1 (p. 14-5)...Ch. 14 - Prob. 14.72ACI
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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_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardLife Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. 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 as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forward
- Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.arrow_forwardDoes Table 1 represent a linear function? If so, finda linear equation that models the data.arrow_forward
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