Business Analytics 4th edition

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Jeffrey D. Camm, James J. Cochran, Michael J. Fry, and Jeffrey W. Ohlmann
Publisher: Cengage Learning

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  • Camm Business Analytics 4e - Homework and Quizzes

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  • Chapter 1: Introduction
    • 1.1: Decision Making
    • 1.2: Business Analytics Defined
    • 1.3: A Categorization of Analytical Methods and Models
    • 1.4: Big Data
    • 1.5: Business Analytics in Practice
    • 1.6: Legal and Ethical Issues in the Use of Data and Analytics
    • 1: Exercises
    • 1: Case Problems
    • 1: Test Bank

  • Chapter 2: Descriptive Statistics
    • 2.1: Overview of Using Data: Definitions and Goals
    • 2.2: Types of Data
    • 2.3: Modifying Data in Excel
    • 2.4: Creating Distributions from Data
    • 2.5: Measures of Location
    • 2.6: Measures of Variability
    • 2.7: Analyzing Distributions
    • 2.8: Measures of Association Between Two Variables
    • 2.9: Data Cleansing
    • 2: Exercises (43)
    • 2: Case Problems (2)
    • 2: Exploring Analytics Applet Exercises (4)
    • 2: JMP Statistics Applet Exercises (31)
    • 2: Test Bank

  • Chapter 3: Data Visualization
    • 3.1: Overview of Data Visualization
    • 3.2: Tables
    • 3.3: Charts
    • 3.4: Advanced Data Visualization
    • 3.5: Data Dashboards
    • 3: Exercises (37)
    • 3: Case Problems
    • 3: Exploring Analytics Applet Exercises (1)
    • 3: Test Bank

  • Chapter 4: Probability: An Introduction to Modeling Uncertainty
    • 4.1: Events and Probabilities
    • 4.2: Some Basic Relationships of Probability
    • 4.3: Conditional Probability
    • 4.4: Random Variables
    • 4.5: Discrete Probability Distributions
    • 4.6: Continuous Probability Distributions
    • 4: Exercises (51)
    • 4: Case Problems (2)
    • 4: Exploring Analytics Applet Exercises (7)
    • 4: Test Bank

  • Chapter 5: Descriptive Data Mining
    • 5.1: Cluster Analysis
    • 5.2: Association Rules
    • 5.3: Text Mining
    • 5: Exercises (21)
    • 5: Case Problems
    • 5: Exploring Analytics Applet Exercises (1)
    • 5: Test Bank

  • Chapter 6: Statistical Inference
    • 6.1: Selecting a Sample
    • 6.2: Point Estimation
    • 6.3: Sampling Distributions
    • 6.4: Interval Estimation
    • 6.5: Hypothesis Tests
    • 6.6: Big Data, Statistical Inference, and Practical Significance
    • 6: Exercises (47)
    • 6: Case Problems (3)
    • 6: Exploring Analytics Applet Exercises (11)
    • 6: JMP Statistics Applet Exercises (18)
    • 6: Test Bank

  • Chapter 7: Linear Regression
    • 7.1: Simple Linear Regression Model
    • 7.2: Least Squares Method
    • 7.3: Assessing the Fit of the Simple Linear Regression Model
    • 7.4: The Multiple Regression Model
    • 7.5: Inference and Regression
    • 7.6: Categorical Independent Variables
    • 7.7: Modeling Nonlinear Relationships
    • 7.8: Model Fitting
    • 7.9: Big Data and Regression
    • 7.10: Prediction with Regression
    • 7: Exercises (34)
    • 7: Case Problems
    • 7: Exploring Analytics Applet Exercises (6)
    • 7: JMP Statistics Applet Exercises (8)
    • 7: Test Bank (1)

  • Chapter 8: Time Series Analysis and Forecasting
    • 8.1: Time Series Patterns
    • 8.2: Forecast Accuracy
    • 8.3: Moving Averages and Exponential Smoothing
    • 8.4: Using Regression Analysis for Forecasting
    • 8.5: Determining the Best Forecasting Model to Use
    • 8: Exercises (44)
    • 8: Case Problems
    • 8: Exploring Analytics Applet Exercises (3)
    • 8: Test Bank

  • Chapter 9: Predictive Data Mining
    • 9.1: Data Sampling, Preparation, and Partitioning
    • 9.2: Performance Measures
    • 9.3: Logistic Regression
    • 9.4: k-Nearest Neighbors
    • 9.5: Classification and Regression Trees
    • 9: Exercises (27)
    • 9: Case Problems
    • 9: Exploring Analytics Applet Exercises (3)
    • 9: Test Bank

  • Chapter 10: Spreadsheet Models
    • 10.1: Building Good Spreadsheet Models
    • 10.2: What-If Analysis
    • 10.3: Some Useful Excel Functions for Modeling
    • 10.4: Auditing Spreadsheet Models
    • 10.5: Predictive and Prescriptive Spreadsheet Models
    • 10: Exercises (20)
    • 10: Case Problems
    • 10: Test Bank

  • Chapter 11: Monte Carlo Simulation
    • 11.1: Risk Analysis for Sanotronics LLC
    • 11.2: Inventory Policy Analysis for Promus Corp
    • 11.3: Simulation Modeling for Land Shark Inc.
    • 11.4: Simulation with Dependent Random Variables
    • 11.5: Simulation Considerations
    • 11: Exercises (18)
    • 11: Case Problems
    • 11: Test Bank

  • Chapter 12: Linear Optimization Models
    • 12.1: A Simple Maximization Problem
    • 12.2: Solving the Par, Inc. Problem
    • 12.3: A Simple Minimization Problem
    • 12.4: Special Cases of Linear Program Outcomes
    • 12.5: Sensitivity Analysis
    • 12.6: General Linear Programming Notation and More Examples
    • 12.7: Generating an Alternative Optimal Solution for a Linear Program
    • 12: Exercises (20)
    • 12: Case Problems
    • 12: Exploring Analytics Applet Exercises (2)
    • 12: Test Bank

  • Chapter 13: Integer Linear Optimization Models
    • 13.1: Types of Integer Linear Optimization Models
    • 13.2: Eastborne Realty, an Example of Integer Optimization
    • 13.3: Solving Integer Optimization Problems with Excel Solver
    • 13.4: Applications Involving Binary Variables
    • 13.5: Modeling Flexibility Provided by Binary Variables
    • 13.6: Generating Alternatives in Binary Optimization
    • 13: Exercises (15)
    • 13: Case Problems
    • 13: Exploring Analytics Applet Exercises (2)
    • 13: Test Bank

  • Chapter 14: Nonlinear Optimization Models
    • 14.1: A Production Application: Par, Inc. Revisited
    • 14.2: Local and Global Optima
    • 14.3: A Location Problem
    • 14.4: Markowitz Portfolio Model
    • 14.5: Adoption of a New Product: The Bass Forecasting Model
    • 14: Exercises (19)
    • 14: Case Problems
    • 14: Exploring Analytics Applet Exercises (1)
    • 14: Test Bank

  • Chapter 15: Decision Analysis
    • 15.1: Problem Formulation
    • 15.2: Decision Analysis Without Probabilities
    • 15.3: Decision Analysis with Probabilities
    • 15.4: Decision Analysis with Sample Information
    • 15.5: Computing Branch Probabilities with Bayes' Theorem
    • 15.6: Utility Theory
    • 15: Exercises (26)
    • 15: Case Problems (1)
    • 15: Test Bank (2)

  • Chapter A: Appendix
    • A: Appendix A: Basics of Excel (11)
    • A: Appendix B: Database Basics with Microsoft Access


Camm/Cochran/Fry/Ohlmann's best-selling Business Analytics, 4th Edition covers the full range of analytics, from descriptive and predictive to prescriptive analytics. Step-by-step instructions demonstrate how to use Excel, Tableau, R, and JMP Pro for more advanced analytics concepts. You have the freedom to select your preferred method for teaching concepts using any of today's software choices. Extensive solutions to problems and cases save significant grading time while allowing you to ensure students are mastering the material. In addition, an all new WebAssign online course management system will allow you to customize material while strengthening each student's understanding of the concepts.

Features:

  • Read It links under each question quickly jump to the corresponding section of the eBook.
  • Watch It links provide step-by-step instruction with short, engaging videos that are ideal for visual learners.
  • Master It Tutorials (MI) show how to solve a similar problem in multiple steps by providing direction along with derivation so students understand the concepts and reasoning behind the problem solving.
  • Students can Talk to a Tutor for additional assistance through a link at the assignment level.
  • All questions contain detailed solutions to the problem, available to students at your discretion.
  • Case Problems (CP) allow students to work on more extensive problems related to the chapter material and work with larger data sets.
  • Exploring Analytics Applet (AQ) problems ask students to answer questions using linked interactive statistical analysis applets.
  • Exploring Statistics JMP Applet (JMP) questions let students understand concepts by utilizing real data. Students must discover the answer to guided questions by interacting with a simulation of real data in our JMP interactive applet within WebAssign.
  • Downloadable DATAfiles (Excel and CSV), Appendix A: Basics of Excel, Appendix B: Database Basics with Microsoft Access, and Appendix C: Solutions to Even-Numbered Problems will be available as textbook resources for students and instructors.

Questions Available within WebAssign

Most questions from this textbook are available in WebAssign. The online questions are identical to the textbook questions except for minor wording changes necessary for Web use. Whenever possible, variables, numbers, or words have been randomized so that each student receives a unique version of the question. This list is updated nightly.

Question Group Key
E - End of Chapter Exercise
MI - Master It
MI.SA - Stand Alone Master It
CP - Case Problem
TB - Test Bank
AQ - Exploring Analytics Applet
JMP - Simulation Question By JMP


Question Availability Color Key
BLACK questions are available now
GRAY questions are under development


Group Quantity Questions
Chapter A: Appendix
A.E 11 001 002 003 004 005 006 007 008 009 010 011
Chapter 1: Introduction
1.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035
Chapter 2: Descriptive Statistics
2.AQ 4 501 502 503 504
2.CP 2 001 001.alt
2.E 43 001 003.MI 003.MI.SA 003.alt 004.MI 004.MI.SA 005.MI 005.MI.SA 005.alt 006 006.alt 007 008 008.alt 009.MI 009.MI.SA 009.alt 010 011.MI 011.MI.SA 011.alt 012 012.alt 013 014.MI 014.MI.SA 015.MI 015.MI.SA 016 017.MI 017.MI.SA 017.alt 019 019.alt 020 020.alt 021.MI 021.MI.SA 023 024 025.MI 025.MI.SA 025.alt
2.JMP 31 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031
2.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068
Chapter 3: Data Visualization
3.AQ 1 501
3.E 37 001 001.alt 003 003.alt 004 005.MI 005.MI.SA 005.alt 007 008 009.MI 009.MI.SA 009.alt 011.MI 011.MI.SA 011.alt 012.MI 012.MI.SA 012.alt 013 014 015.MI 015.MI.SA 015.alt 017.MI 017.MI.SA 017.alt 018.MI 018.MI.SA 018.alt 019 020.MI 020.MI.SA 020.alt 021 022 023
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Chapter 4: Probability: An Introduction to Modeling Uncertainty
4.AQ 7 501a 501b 502 503a 503b 503c 504
4.CP 2 001 001.alt
4.E 51 001.MI 001.MI.SA 003.MI 003.MI.SA 004.MI 004.MI.SA 005.MI 005.MI.SA 006 007 008 009.MI 009.MI.SA 010.MI 010.MI.SA 011 012.MI 012.MI.SA 013 015.MI 015.MI.SA 016 017.MI 017.MI.SA 018 019.MI 019.MI.SA 020 021.MI 021.MI.SA 022 023.MI 023.MI.SA 024 025.MI 025.MI.SA 026 027.MI 027.MI.SA 028 029 030.MI 030.MI.SA 031.MI 031.MI.SA 032 033.MI 033.MI.SA 034 035.MI 035.MI.SA
4.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035
Chapter 5: Descriptive Data Mining
5.AQ 1 501
5.E 21 001 002 005 006 009 012.JMP 012.R 013.JMP 013.R 014.JMP 014.R 015.JMP 015.R 016.JMP 016.R 017.JMP 017.R 018.JMP 018.R 019.JMP 019.R
5.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047
Chapter 6: Statistical Inference
6.AQ 11 501 502 503 504a 504b 504c 504d 504e 505 506 507
6.CP 3 001 002 002.alt
6.E 47 001 001.alt 003 005.MI 005.MI.SA 007.MI 007.MI.SA 008 009 010 011 012 013 014 015.MI 015.MI.SA 015.alt 017 017.alt 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033.MI 033.MI.SA 033.alt 034 035.MI 035.MI.SA 036 037 038 039 040.MI 040.MI.SA 041 043
6.JMP 18 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018
6.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048
Chapter 7: Linear Regression
7.AQ 6 501 502 503 504 505 506a 506b
7.E 34 001.MI 001.MI.SA 001.alt 003 004 004.alt 005.MI 005.MI.SA 005.alt 006 007.MI 007.MI.SA 007.alt 008 008.alt 009.MI 009.MI.SA 009.alt 010.MI 010.MI.SA 010.alt 011.MI 011.MI.SA 011.alt 012 013 013.alt 014 015 017 018 018.alt 019 021
7.JMP 8 001 002 003 004 005 006 007 008
7.TB 1 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047
Chapter 8: Time Series Analysis and Forecasting
8.AQ 3 501 502 503
8.E 44 001.MI 001.MI.SA 002 003 004 005.MI 005.MI.SA 006 007 007.alt 008 008.alt 009 010 011.MI 011.MI.SA 012 012.alt 013.MI 013.MI.SA 013.alt 014 015 015.alt 016 017.MI 017.MI.SA 018 019.MI 019.MI.SA 019.alt 021 021.alt 022 023.MI 023.MI.SA 024 024.alt 025.MI 025.MI.SA 025.alt 026 026.alt 027
8.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041
Chapter 9: Predictive Data Mining
9.AQ 3 501a 501b 501c
9.E 27 001 002 003 004 005 006 007 009.JMP 009.R 010.JMP 010.R 011.JMP 011.R 012.JMP 012.R 014.JMP 014.R 016.JMP 016.R 017.JMP 017.R 020.JMP 020.R 022.JMP 022.R 023.JMP 023.R
9.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038
Chapter 10: Spreadsheet Models
10.E 20 001 001.alt 002 003.MI 003.MI.SA 005.MI 005.MI.SA 006 007 009 011 012 013 014 015.MI 015.MI.SA 016 017 019 020
10.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040
Chapter 11: Monte Carlo Simulation
11.E 18 003 004 005 006 008 009 011 012 013 014 015 016 018 020 023 025 026 031
11.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040
Chapter 12: Linear Optimization Models
12.AQ 2 501a 501b
12.E 20 001.MI 001.MI.SA 002 003 004 005 006 007.MI 007.MI.SA 008 009.MI 009.MI.SA 011 013 014 015 016 017 018 019
12.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036
Chapter 13: Integer Linear Optimization Models
13.AQ 2 501a 501b
13.E 15 003 005 006 006.alt 007 008 010 011 012-013 015 016 017 019 021 022
13.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040
Chapter 14: Nonlinear Optimization Models
14.AQ 1 501
14.E 19 001 002 003 004 005 006 007 007.alt 008 009 010 011 011.alt 012 013 013.alt 015 019 020
14.TB 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040
Chapter 15: Decision Analysis
15.CP 1 001
15.E 26 001.MI 001.MI.SA 002 003.MI 003.MI.SA 004.MI 004.MI.SA 005 006 007 008.MI 008.MI.SA 009 010 011 012 013 014 015 016.MI 016.MI.SA 017 018 021 023 025
15.TB 2 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039
Total 542 (636)