BASIC BUSINESS STATISTICS
14th Edition
ISBN: 9780135285923
Author: MARK BERENSON
Publisher: PEARSON
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Chapter 16, Problem 38PS
a.
To determine
Perform a residual analysis.
b.
To determine
Compute
c.
To determine
Compute the MAD.
d.
To determine
Discuss which forecasting model should selected.
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The 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…
The following is a spreadsheet for forecasting annual revenues for the Gorsuch Corp.
(check picture sent)
a. Complete the following sentence: "The linear model is (or is not) a valid model of the trend of the data because ..."
b. Identify any errors in the model's specification.
c. An incorrect entry has been made in one cell and then copied down. You should be able to spot the cell without making any calculations. Identify the cell and indicate the correct entry for it.
A sales manager collected the following data on annual sales for new customer accounts and the number of years of experience for a sample of 10 salespersons. Click on the datafile logo to reference the data.
a. Choose the correct scatter diagram for these data with years of experience as the independent variable. B
(NEED ANSWERS FOR B and C)
b. Develop an estimated regression equation that can be used to predict annual sales given the years of experience.
c. Use the estimated regression equation to predict annual sales for a salesperson with 9 years of experience (to the nearest whole number).
Chapter 16 Solutions
BASIC BUSINESS STATISTICS
Ch. 16 - If you are using exponential smoothing for...Ch. 16 - Consider a nine-year moving average used to smooth...Ch. 16 - You are using exponential smoothing on an annual...Ch. 16 - Prob. 4PSCh. 16 - Prob. 5PSCh. 16 - How have stocks performed in the past? The...Ch. 16 - Prob. 7PSCh. 16 - Prob. 8PSCh. 16 - Prob. 9PSCh. 16 - Prob. 10PS
Ch. 16 - The linear trend forecasting equation for an...Ch. 16 - There has been much publicity about bounces paid...Ch. 16 - Prob. 13PSCh. 16 - Prob. 14PSCh. 16 - Prob. 15PSCh. 16 - The data shown in the following table and stored...Ch. 16 - Prob. 17PSCh. 16 - Prob. 18PSCh. 16 - Prob. 19PSCh. 16 - Prob. 20PSCh. 16 - Prob. 21PSCh. 16 - Prob. 22PSCh. 16 - You are given an annual time series with 40...Ch. 16 - Prob. 24PSCh. 16 - Prob. 25PSCh. 16 - Prob. 26PSCh. 16 - Prob. 27PSCh. 16 - Prob. 28PSCh. 16 - Prob. 29PSCh. 16 - Using the average baseball salary from 200 through...Ch. 16 - Using the yearly amount of solar power generated...Ch. 16 - The following residuals are from a linear trend...Ch. 16 - Prob. 33PSCh. 16 - Prob. 34PSCh. 16 - Prob. 35PSCh. 16 - Prob. 36PSCh. 16 - Prob. 37PSCh. 16 - Prob. 38PSCh. 16 - Prob. 39PSCh. 16 - Prob. 40PSCh. 16 - In forecasting daily time-series data, how many...Ch. 16 - In forecasting a quarterly time series over the...Ch. 16 - Prob. 43PSCh. 16 - Prob. 44PSCh. 16 - Are gasoline prices higher during the height of...Ch. 16 - Prob. 46PSCh. 16 - Prob. 47PSCh. 16 - The file Silver-Q contains the price in London for...Ch. 16 - Prob. 49PSCh. 16 - What is a time series?Ch. 16 - What are the different components of a time-series...Ch. 16 - What is the difference between moving average and...Ch. 16 - Prob. 53PSCh. 16 - How does the least-squares linear trend...Ch. 16 - How does autoregressive modelling differ from the...Ch. 16 - What are the different approaches to choosing an...Ch. 16 - What is the major difference between using SYX and...Ch. 16 - How does forecasting for monthly or quarterly data...Ch. 16 - Prob. 60PSCh. 16 - The monthly commercial and residential prices for...Ch. 16 - The data stored in McDonalds represent the gross...Ch. 16 - Teachers’ Retirement System of the City of New...Ch. 16 - Prob. 64PS
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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_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardYour younger brother, a fourth form practical geography student wishes to test whether there could be some relationship between annual visits to a local market by all residents surrounding that market. After a well-designed field study he obtains the following figures: Number of visits per year 21 13 19 18 17 17 14 20 10 11 Residence distance from market (KM) 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 From the information above, what is the dependent variable and what is the explanatory variable? Using (i) above, obtain the regression equation of Yi on Xi. Hence, estimate the residence distance from the market (in KM) if the number of visits are 16 visits Sketch your results in (ii) above. What conclusion can you draw from your sketch?arrow_forward
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