BASIC BUSINESS STATISTICS-STUD.SOLN.MAN
14th Edition
ISBN: 9780134685045
Author: BERENSON
Publisher: PEARSON
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Chapter 16, Problem 36PS
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 following is a spreadsheet for forecasting annual revenues for the Gorsuch Corp.
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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.
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 residuals for data set A and data set B were calculated and plotted on
separate residual plots. If the residuals for data set A do not form a pattern
and the residuals for data set B do not form a pattern, what can be
concluded?
OA. A linear model is a good fit for data set A but not data set B.
O B. A linear model is a good fit for data set B but not data set A.
AC. A linear model is a good fit for both data sets.
D. A linear model is not a good fit for either data set.
Chapter 16 Solutions
BASIC BUSINESS STATISTICS-STUD.SOLN.MAN
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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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_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_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forward
- Urban Travel Times Population of cities and driving times are related, as shown in the accompanying table, which shows the 1960 population N, in thousands, for several cities, together with the average time T, in minutes, sent by residents driving to work. City Population N Driving time T Los Angeles 6489 16.8 Pittsburgh 1804 12.6 Washington 1808 14.3 Hutchinson 38 6.1 Nashville 347 10.8 Tallahassee 48 7.3 An analysis of these data, along with data from 17 other cities in the United States and Canada, led to a power model of average driving time as a function of population. a Construct a power model of driving time in minutes as a function of population measured in thousands b Is average driving time in Pittsburgh more or less than would be expected from its population? c If you wish to move to a smaller city to reduce your average driving time to work by 25, how much smaller should the city be?arrow_forward4arrow_forwardProblem 1: A diamond's price is determined by the Five Cs: cut, clarity, color, depth, and carat weight. Use the data in the attached excel file "Diamond data 1) To develop a linear regression model to show the effect of Carat on Price. What is the expected price of a 1.85 carat diamond? 2) To develop a linear regression model to show the effect of depth on Price. What is the expected price of a diamond having a depth of 62.5? Use the diamond's price as the dependent variable). (Do this manually as well as using a software. Hint: Work smart not hard for the manual part and show screen shot for what you have done and how) Comment on the adequacy of the regression modelarrow_forward
- 6arrow_forwardWould someone familiar with SPSS be able to help with sub-question d please?arrow_forwardDrop-off points (points de collecte in French), where residents can dispose of their organic waste for composting, were established in each of the five targeted neighborhoods of PROBLEM 1. Camille wants to see whether or not there exists a relationship between the average weekly amount of non-disposed food waste per household and the availability of drop-off points in the neighborhood, expressed as the number of households per drop-off point (considered fixed). Her data and some of her preliminary calculations are presented below: Camille also calculated the slope of the fitted regression line: estimated slope = 0.008. Test whether or not the availability of drop-off points in the neighborhood has a linear effect on the amount of non-disposed food waste per household. Use α = 0.10.arrow_forward
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