Applied Statistics in Business and Economics
5th Edition
ISBN: 9781259329050
Author: DOANE
Publisher: MCG
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Chapter 14, Problem 6CR
When would the exponential trend model be preferred to a linear trend model?
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Chapter 14 Solutions
Applied Statistics in Business and Economics
Ch. 14.2 - Prob. 2SECh. 14.2 - Prob. 3SECh. 14.2 - Prob. 4SECh. 14.2 - Prob. 5SECh. 14.4 - (a) Make an Excel line graph of the exchange rate...Ch. 14.5 - Prob. 7SECh. 14 - Explain the difference between (a) stocks and...Ch. 14 - (a) What is periodicity? (b) Give original...Ch. 14 - (a) What are the distinguishing features of each...Ch. 14 - Name four criteria for assessing a trend forecast.
Ch. 14 - Name two advantages and two disadvantages of each...Ch. 14 - When would the exponential trend model be...Ch. 14 - Explain how to obtain the compound percent growth...Ch. 14 - (a) When might a quadratic model be useful? (b)...Ch. 14 - Name five measures of fit for a trend, and state...Ch. 14 - Prob. 10CRCh. 14 - Prob. 11CRCh. 14 - Prob. 12CRCh. 14 - (a) Why is seasonality irrelevant for annual data?...Ch. 14 - Prob. 14CRCh. 14 - (a) Explain how seasonal binaries can be used to...Ch. 14 - What is the purpose of index numbers?Ch. 14 - Prob. 10CECh. 14 - Prob. 11CECh. 14 - Prob. 12CECh. 14 - Prob. 14CECh. 14 - Prob. 15CECh. 14 - Prob. 16CECh. 14 - Prob. 17CECh. 14 - Prob. 18CECh. 14 - Prob. 19CECh. 14 - Prob. 20CECh. 14 - Prob. 21CECh. 14 - Prob. 22CECh. 14 - (a) Plot the data on skier/snowboard visits. (b)...Ch. 14 - Prob. 25CECh. 14 - (a) Use Excel, MegaStat, or MINITAB to fit an...Ch. 14 - Prob. 27CECh. 14 - Prob. 28CECh. 14 - Prob. 29CECh. 14 - Prob. 30CECh. 14 - (a) Plot the data on revolving credit (credit...
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- Does Table 1 represent a linear function? If so, finda linear equation that models the data.arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_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_forward
- The Pennsylvania State University had enrollments of 40,571 students in 2000 and 41,289 students in 2004 at its main campus in University Park, Pennsylvania. Assuming the enrollment growth is linear, predict the enrollments in 2008 and 2010.arrow_forwardEloise needs to finish a 516-page report on international trade so she can present it at the big conference this weekend. She's been tracking her page count at the end of each business day to see if she's on track. Use Excel to fit a trendline to Eloise's data to create a model of her productivity. According to your model, will Eloise be able to finish her report by the end of the day next Friday? Assume she works only on weekdays. Day Pages Monday 425 Tuesday 431 Wednesday 449 Thursday 464 Friday 471 A: Use Excel to fit a trendline to Eloise's data. What are the slope and intercept for this line? The slope is The intercept is B: Use your model (trendline) to answer the question. Will Eloise finish her report by the end of next friday? Yesarrow_forwardWhy is the data lognormalarrow_forward
- What is meant by the slope of the regression line?arrow_forwardIn 1970 the life expectancy in Norway was 70 years old, and in 1990 it was 80. Write a linearequation to model this situation and then use your model to predict how long people will livein 2020 if the trend continues. Why may you be skeptical (mathematically) of this prediction?arrow_forward1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.arrow_forward
- Suppose your dog weighed 3.1 pounds at birth and weighed 15 pounds one year later. Based on these two data points, find a linear function that describes how weight varies with age. Use this function to predict your dog's weight at 6 and 9 years of age. Comment on the validity of this model.arrow_forwardA marketing consultant created a linear regression model to predict the number of units sold by a client based on the amount of money spent on marketing by the client. Which of the following is the best graphic to use to evaluate the appropriateness of the model?arrow_forwardThe November 24, 2001, issue of The Economist published economic data for 15 industrialized nations. Included were the percent changes in gross domestic product (GDP), industrial production (IP), consumer prices (CP), and producer prices (PP) from Fall 2000 to Fall 2001, and the unemployment rate in Fall 2001 (UNEMP). An economist wants to construct a model to predict GDP from the other variables. A fit of the model GDP = , + P,IP + 0,UNEMP + f,CP + P,PP + € yields the following output: The regression equation is GDP = 1.19 + 0.17 IP + 0.18 UNEMP + 0.18 CP – 0.18 PP Predictor Coef SE Coef тР Constant 1.18957 0.42180 2.82 0.018 IP 0.17326 0.041962 4.13 0.002 UNEMP 0.17918 0.045895 3.90 0.003 CP 0.17591 0.11365 1.55 0.153 PP -0.18393 0.068808 -2.67 0.023 Predict the percent change in GDP for a country with IP = 0.5, UNEMP = 5.7, CP = 3.0, and PP = 4.1. a. b. If two countries differ in unemployment rate by 1%, by how much would you predict their percent changes in GDP to differ, other…arrow_forward
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