Statistical Techniques in Business and Economics, 16th Edition
16th Edition
ISBN: 9780078020520
Author: Douglas A. Lind, William G Marchal, Samuel A. Wathen
Publisher: McGraw-Hill Education
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Chapter 3, Problem 32E
To determine
Find the geometric mean annual increase for the period.
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The U.S. Federal Aviation Administration reported that passenger revenues on international flights increased from $500 million in 1986 to $5,100 million in 2009. What is the geometric mean annual percent increase in international passenger revenues?
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The side-by-side bar graph available below shows the approximate average grade point average for the
years 1991-1992, 1996-1997, 2001-2002, and 2006-2007 for colleges and universities. Complete parts (a) through (c)
below.
Click the icon to view the side-by-side bar graph.
(a) Does the graph suggest that grade inflation is a problem in colleges?
A. Yes, because the GPAS increased over time for all schools.
B. No, because nothing can be concluded about individual GPAS from the averages.
C. No, because the GPAS are different for each time period.
D. Yes, because the GPAS are greater for private schools for every time period.
(b) In public schools, the average GPA was 2.86 in 1991-1992 and 3.02 in 2006-2007. In private schools, the average
GPA was 3.09 in 1991-1992 and 3.30 in 2006-2007. Determine the percentage increase in GPAs for public schools
from 1991 to 2006. Determine the percentage increase in GPAs for private schools from 1991 to 2006. Which type of
institution appears to have…
Chapter 3 Solutions
Statistical Techniques in Business and Economics, 16th Edition
Ch. 3 - The annual incomes of a sample of...Ch. 3 - The six students in Computer Science 411 are a...Ch. 3 - Compute the mean of the following population...Ch. 3 - Compute the mean of the following population...Ch. 3 - a. Compute the mean of the following sample...Ch. 3 - a. Compute the mean of the following sample...Ch. 3 - Compute the mean of the following sample values:...Ch. 3 - Suppose you go to the grocery store and spend...Ch. 3 - For Exercises 710, (a) compute the arithmetic mean...Ch. 3 - For Exercises 710, (a) compute the arithmetic mean...
Ch. 3 - For Exercises 710, (a) compute the arithmetic mean...Ch. 3 - Prob. 10ECh. 3 - Prob. 11ECh. 3 - Prob. 12ECh. 3 - Prob. 2.1SRCh. 3 - The number of work stoppages in the automobile...Ch. 3 - Prob. 13ECh. 3 - Prob. 14ECh. 3 - Prob. 15ECh. 3 - Prob. 16ECh. 3 - Prob. 17ECh. 3 - Sally Reynolds sells real estate along the coastal...Ch. 3 - Prob. 19ECh. 3 - Prob. 20ECh. 3 - Prob. 3SRCh. 3 - Prob. 21ECh. 3 - Prob. 22ECh. 3 - Prob. 4SRCh. 3 - Prob. 23ECh. 3 - Prob. 24ECh. 3 - Prob. 25ECh. 3 - Prob. 26ECh. 3 - Prob. 5.1SRCh. 3 - Prob. 5.2SRCh. 3 - Compute the geometric mean of the following...Ch. 3 - Prob. 28ECh. 3 - Listed below is the percent increase in sales for...Ch. 3 - Prob. 30ECh. 3 - Prob. 31ECh. 3 - Prob. 32ECh. 3 - Prob. 33ECh. 3 - Prob. 34ECh. 3 - Prob. 6SRCh. 3 - Prob. 35ECh. 3 - Prob. 36ECh. 3 - Prob. 37ECh. 3 - Prob. 38ECh. 3 - Prob. 39ECh. 3 - Prob. 40ECh. 3 - Prob. 7SRCh. 3 - Prob. 41ECh. 3 - Prob. 42ECh. 3 - Prob. 43ECh. 3 - Prob. 44ECh. 3 - Plywood Inc. reported these returns on stockholder...Ch. 3 - Prob. 46ECh. 3 - Prob. 8SRCh. 3 - Prob. 47ECh. 3 - Prob. 48ECh. 3 - Prob. 49ECh. 3 - Prob. 50ECh. 3 - Prob. 51ECh. 3 - Prob. 52ECh. 3 - Prob. 9SRCh. 3 - Prob. 53ECh. 3 - The mean income of a group of sample observations...Ch. 3 - Prob. 55ECh. 3 - Prob. 56ECh. 3 - Prob. 10SRCh. 3 - Prob. 57ECh. 3 - Prob. 58ECh. 3 - Estimate the mean and the standard deviation of...Ch. 3 - Prob. 60ECh. 3 - The IRS was interested in the number of individual...Ch. 3 - Prob. 62ECh. 3 - Prob. 63CECh. 3 - Prob. 64CECh. 3 - Prob. 65CECh. 3 - Prob. 66CECh. 3 - Prob. 67CECh. 3 - Prob. 68CECh. 3 - Prob. 69CECh. 3 - Prob. 70CECh. 3 - Prob. 71CECh. 3 - Prob. 72CECh. 3 - Prob. 73CECh. 3 - A recent article suggested that, if you earn...Ch. 3 - Prob. 75CECh. 3 - Prob. 76CECh. 3 - Prob. 77CECh. 3 - Prob. 78CECh. 3 - The Apollo space program lasted from 1967 until...Ch. 3 - Prob. 80CECh. 3 - Prob. 81CECh. 3 - The Kentucky Derby is held the first Saturday in...Ch. 3 - Prob. 83CECh. 3 - Prob. 84CECh. 3 - Bidwell Electronics Inc. recently surveyed a...Ch. 3 - Refer to the Real Estate data, which reports...Ch. 3 - Refer to the Baseball 2012 data, which report...Ch. 3 - Refer to the Buena School District bus data....
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- In 2011 home prices and mortgage rates dropped so low that in a number of cities the monthly cost of owning a home was less expensive than renting. The following data show the average asking rent for 10 markets and the monthly mortgage on the median priced home (including taxes and insurance) for 10 cities where the average monthly mortgage payment was less than the average asking rent (The Wall Street Journal, November 26–27, 2011).Click on the datafile logo to reference the data. Enter negative values as negative numbers. a. Develop the estimated regression equation that can be used to predict the monthly mortgage given the average asking rent (to 2 decimals).The regression equation is:Mortgage ($) = + Rent ($)arrow_forwardIn preparing your monthly report, you want to determine whether or not your firm's sales for the month have exceeded average sales for the previous 12 months. Using the data below, estimate the firm's average sales for the last 12 months. Provide your answer to the first decimal point, that is, 0.9 instead of 1. Month Sales January 177,968 February 132,872 March 168,059 April 143,759 May 159,702 June 169,603 July 147,164 August 145,102 September 149,743 October 160,933 November 264,097 December 300,000arrow_forwardThe following data report total, monthly U.S. gasoline sales in millions of dollars from January 2017 to December 2018. (To find the data, go to the site https://www.census.gov/retail/index.html#mrts, find “Monthly Retail Trade Report,” then select “Time Series/Trend Charts.”) e. What are the monthly indexes? (Round your answers to 3 decimal places.) f. Forecast sales for January, February, March, and April of 2019. (Round your answers to 2 decimal places.)arrow_forward
- Using the following time series data of U.S. new car sales ($ millions), respond to the following items. (The data is sourced from https://www.census.gov/retail/index.html#mrts. Find “Monthly Retail Trade Report,” then select “Time Series/Trend Charts” to find the data for U.S. new car sales.). Year $ Sales (Millions) 2009 486,565 2010 549,473 2011 608,788 2012 672,550 2013 735,374 2014 785,142 2015 842,122 2016 877,743 2017 911,157 2018 943,538 b. Test for autocorrelation using the 0.05 significance level. (Negative amounts should be indicated by a minus sign. Round your answers to 2 decimal places.) Year $ Sales (Millions) Predicted Residuals Lagged Residuals Squared Difference Squared Residuals 2009 486,565 2010 549,473 2011 608,788 2012 672,550 2013 735,374 2014 785,142 2015 842,122 2016 877,743 2017 911,157 2018 943,538…arrow_forwardSuppose that a company's annual sales were $1,200,000 in 1999. The annual growth rate of sales from 1999 to 2000 was 16%, from 2000 to 2001 was -5%, and from 2001 to 2002 was 22%.The geometric mean growth rate of sales over this three-year period is calculated as 10.37%. Use the geometric mean growth rate and determine the forecasted sales for 2004.A. 1,780,678B. 1,200,014C. 1,965,337D. 1,613,371E. 1,422,197arrow_forwardUsing the following time series data of U.S. new car sales ($ millions), respond to the following items. (The data is sourced from https://www.census.gov/retail/index.html#mrts. Find “Monthly Retail Trade Report," then select "Time Series/Trend Charts" to find the data for U.S. new car sales.). $ Sales (Millions) $ 486,565 Year 2009 549,473 608,790 672,550 735,374 785,142 842,120 877,743 911,157 943,538 2010 2011 2012 2013 2014 2015 2016 2017 2018 E Click here for the Excel Data File b. Test for autocorrelation using the 0.05 significance level. (Negative amounts should be indicated by a minus sign. Round your answers to 2 decimal places.) Lagged Residuals Squared Difference Squared Residuals Year Sales Predicted Residuals 2009 $ 486,565 508,123.70 (21,558.70) 0.00 0.00 464,758,328.46 (10,455.50) (2,943.26) 2010 549,473 559,928.50 2011 608,790 611,733.30 2012 672,550 663,538.00 9,011.96 2013 735,374 715,342.80 20,031.19 2014 785.142 767,147.60 17,994.41 2015 842.120 818,952.40…arrow_forward
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