EBK BASIC BUSINESS STATISTICS
14th Edition
ISBN: 9780134685168
Author: STEPHAN
Publisher: YUZU
expand_more
expand_more
format_list_bulleted
Concept explainers
Question
Chapter 16, Problem 35PS
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 is appropriate.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
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.
The sales manager of a large automotive parts distributor wants to estimate the total annual sales for each of the company’s regions. Three factors appear to be related to regional sales: the number of retail outlets in the region, the total personal income of the region, and the number of cars registered in the region. The following table shows the data for 10 regions that were gathered for last year sales.
The excel data file for this problem is:final exam question 6B spring 2021.xlsx
Region
Annual sales ($ million)
Number of retail outlets
Number of automobiles registered (million)
Personal income ($ billion)
1
37.702
1,739
9.27
85.4
2
24.196
1,221
5.86
60.7
3
32.055
1,846
8.81
68.1
4
3.611
120
4.81
20.1
5
17.625
1,096
10.31
33.8
6
45.919
2,290
11.62
95.1
7
29.600
1,687
8.96
69.3
8
8.114
241
6.28
16.5
9
20.116
649
7.77
34.9
10
12.994
1,427
10.92…
Drop-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.
Chapter 16 Solutions
EBK 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
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- 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_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: Number of Amount of non- (x, – x)* (Y, -Y) (x; - x)(Y, – Y) households per drop-off point disposed food waste per household (kg/week) Yi Xi 387.5 4.0 7656.25 0.25 43.75 337.5 3.2 1406.25 0.09 -11.25 187.5 2.5 12656.25 1.00 112.50 412.5 5.0 12656.25 2.25 168.75 175.0 2.8 15625 0.49 87.50 E; = 1500 Σ 17.5 E; = 50000 Σ -4.08 Ej = 401.25 2) Camille also calculated the slope of the fitted regression line: estimated slope = 0.008. Test whether or not the…arrow_forward
- Problem #1b: Use a two-month weighted moving average to create a forecast for May, using the weights: 0.6 and 0.4 (largest weight is for the most recent data). Calculate the forecast errors associated the weighted moving average forecasts. Month Demand January 18 February 23 March 20 April 16 May ???arrow_forwardFor each of the following situations, state the independent variable and the dependent variable. a. A study is done to determine if elderly drivers are involved in more motor vehicle fatalities than other drivers. The number of fatalities per 100,000 drivers is compared to the age of drivers. b. A study is done to determine if the weekly grocery bill changes based on the number of family members. c. Insurance companies base life insurance premiums partially on the age of the applicant. d. Utility bills vary according to power consumption. e. A study is done to determine if a higher education reduces the crime rate in a population.arrow_forwardSTER. 1. Wine Consumption. The table below gives the U.S. adult wine consumption, in gallons per person per year, for selected years from 1980 to 2005. a) Create a scatterplot for the data. Graph the scatterplot Year Wine below. Consumption 2.6 b) Determine what type of model is appropriate for the 1980 data. 1985 2.3 c) Use the appropriate regression on your calculator to find a Graph the regression equation in the same coordinate plane below. d) According to your model, in what year was wine consumption at a minimum? A e) Use your model to predict the wine consumption in 2008. 1990 2.0 1995 2.1 2000 2.5 2005 2.8arrow_forward
- The I-85 Carpet Outlet wants to develop a means to forecastits carpet sales. The store manager believes that the store’s sales are directly related to the number of new housing starts in town. The manager has gathered data from county records of monthly house construction permits and from store records on monthly sales. These data are as follows: a. Develop a linear regression model for this data and forecast carpet sales if 25 construction permits for new homes are filed. b. Determine the strength of the causal relationship be- tween monthly sales and new home construction using correlation. Monthly Carpet Sales Monthly Construction(1000s yd) Permits5 1712 306 125 14arrow_forwardJijustration 8.1. Represent the following data by linc charts: TABLE 8.2. PROGRESS OF ELECTRICITY SUPPLY Installed capacity (MW) Diesel Year Steam Hydro 1971. 2,436 2,471 300 1,917 2,419 2,936 3,167 3,389 4,124 1972 329 1973 2,538 3,008 3,605 4,417 327 1974 401 1975 403 1976 486 4,887 5,975 1977 448 4 757 1978 421 5,487arrow_forwardA 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).arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
- Linear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage LearningCollege AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningFunctions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning
- Algebra and Trigonometry (MindTap Course List)AlgebraISBN:9781305071742Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningBig Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
Linear Algebra: A Modern Introduction
Algebra
ISBN:9781285463247
Author:David Poole
Publisher:Cengage Learning
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...
Algebra
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Cengage Learning
Algebra and Trigonometry (MindTap Course List)
Algebra
ISBN:9781305071742
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Big Ideas Math A Bridge To Success Algebra 1: Stu...
Algebra
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:Houghton Mifflin Harcourt
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY