Business Analytics 5th 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 5e - Homework and Quizzes

Access is contingent on use of this textbook in the instructor's classroom.

  • Chapter 1: Introduction to Business Analytics
    • 1.1: Decision Making
    • 1.2: Business Analytics Defined
    • 1.3: A Categorization of Analytical Methods and Models
    • 1.4: Big Data, the Cloud, and Artificial Intelligence
    • 1.5: Business Analytics in Practice
    • 1.6: Legal and Ethical Issues in the Use of Data and Analytics
    • 1: Exercises (10)
    • 1: Extra Problems
    • 1: Test Bank (38)

  • Chapter 2: Descriptive Statistics
    • 2.1: Overview of Using Data: Definitions and Goals
    • 2.2: Types of Data
    • 2.3: Exploring 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: Exercises (44)
    • 2: Extra Problems (10)
    • 2: R Appendix Practice Problems (4)
    • 2: Case Problems (2)
    • 2: Exploring Analytics Applet Exercises (4)
    • 2: Test Bank (69)

  • Chapter 3: Data Visualization
    • 3.1: Overview of Data Visualization
    • 3.2: Tables
    • 3.3: Charts
    • 3.4: Specialized Data Visualization
    • 3.5: Visualizing Geospatial Data
    • 3.6: Data Dashboards
    • 3: Exercises (45)
    • 3: Extra Problems (5)
    • 3: R Appendix Practice Problems (2)
    • 3: Case Problems
    • 3: Exploring Analytics Applet Exercises (1)
    • 3: Test Bank (48)

  • Chapter 4: Data Wrangling: Data Management and Data Cleaning Strategies
    • 4.1: Discovery
    • 4.2: Structuring
    • 4.3: Cleaning
    • 4.4: Enriching
    • 4.5: Validating and Publishing
    • 4: Exercises (20)
    • 4: Extra Problems
    • 4: Case Problems
    • 4: Exploring Analytics Applet Exercises
    • 4: Test Bank (43)

  • Chapter 5: Probability: An Introduction to Modeling Uncertainty
    • 5.1: Events and Probabilities
    • 5.2: Some Basic Relationships of Probability
    • 5.3: Conditional Probability
    • 5.4: Random Variables
    • 5.5: Discrete Probability Distributions
    • 5.6: Continuous Probability Distributions
    • 5: Exercises (57)
    • 5: Extra Problems (2)
    • 5: R Appendix Practice Problems (6)
    • 5: Case Problems (2)
    • 5: Exploring Analytics Applet Exercises (4)
    • 5: Test Bank (38)

  • Chapter 6: Descriptive Data Mining
    • 6.1: Dimension Reduction
    • 6.2: Cluster Analysis
    • 6.3: Association Rules
    • 6.4: Text Mining
    • 6: Exercises (21)
    • 6: Extra Problems
    • 6: R Appendix Practice Problems (2)
    • 6: Orange Appendix Practice Problems (2)
    • 6: Case Problems
    • 6: Exploring Analytics Applet Exercises (1)
    • 6: Test Bank (49)

  • Chapter 7: Statistical Inference
    • 7.1: Selecting a Sample
    • 7.2: Point Estimation
    • 7.3: Sampling Distributions
    • 7.4: Interval Estimation
    • 7.5: Hypothesis Tests
    • 7.6: Big Data, Statistical Inference, and Practical Significance
    • 7: Exercises (47)
    • 7: Extra Problems
    • 7: R Appendix Practice Problems (4)
    • 7: Case Problems (1)
    • 7: Exploring Analytics Applet Exercises (14)
    • 7: Test Bank (50)

  • Chapter 8: Linear Regression
    • 8.1: Simple Linear Regression Model
    • 8.2: Least Squares Method
    • 8.3: Assessing the Fit of the Simple Linear Regression Model
    • 8.4: The Multiple Linear Regression Model
    • 8.5: Inference and Linear Regression
    • 8.6: Categorical Independent Variables
    • 8.7: Modeling Nonlinear Relationships
    • 8.8: Model Fitting
    • 8.9: Big Data and Regression
    • 8.10: Prediction with Regression
    • 8: Exercises (29)
    • 8: Extra Problems
    • 8: R Appendix Practice Problems (10)
    • 8: Case Problems
    • 8: Exploring Analytics Applet Exercises (7)
    • 8: Test Bank (50)

  • Chapter 9: Time Series Analysis and Forecasting
    • 9.1: Time Series Patterns
    • 9.2: Forecast Accuracy
    • 9.3: Moving Averages and Exponential Smoothing
    • 9.4: Using Linear Regression Analysis for Forecasting
    • 9.5: Determining the Best Forecasting Model to Use
    • 9: Exercises (44)
    • 9: Extra Problems
    • 9: R Appendix Practice Problems (3)
    • 9: Case Problems
    • 9: Exploring Analytics Applet Exercises (3)
    • 9: Test Bank (44)

  • Chapter 10: Predictive Data Mining: Regression Tasks
    • 10.1: Regression Performance Measures
    • 10.2: Data Sampling, Preparation, and Partitioning
    • 10.3: k-Nearest Neighbors Regression
    • 10.4: Regression Trees
    • 10.5: Neural Network Regression
    • 10.6: Feature Selection
    • 10: Exercises (14)
    • 10: Extra Problems
    • 10: R Appendix Practice Problems (5)
    • 10: Orange Appendix Practice Problems (5)
    • 10: Case Problems
    • 10: Exploring Analytics Applet Exercises
    • 10: Test Bank (20)

  • Chapter 11: Predictive Data Mining: Classification Tasks
    • 11.1: Data Sampling, Preparation, and Partitioning
    • 11.2: Performance Measures for Binary Classification
    • 11.3: Classification with Logistic Regression
    • 11.4: k-Nearest Neighbors Classification
    • 11.5: Classification Trees
    • 11.6: Neural Network Classification
    • 11.7: Feature Selection
    • 11: Exercises (29)
    • 11: Extra Problems
    • 11: R Appendix Practice Problems (5)
    • 11: Orange Appendix Practice Problems (5)
    • 11: Case Problems
    • 11: Exploring Analytics Applet Exercises (3)
    • 11: Test Bank (36)

  • Chapter 12: Spreadsheet Models
    • 12.1: Building Good Spreadsheet Models
    • 12.2: What-If Analysis
    • 12.3: Some Useful Excel Functions for Modeling
    • 12.4: Auditing Spreadsheet Models
    • 12.5: Predictive and Prescriptive Spreadsheet Models
    • 12: Exercises (23)
    • 12: Extra Problems (2)
    • 12: Case Problems
    • 12: Exploring Analytics Applet Exercises
    • 12: Test Bank (34)

  • Chapter 13: Monte Carlo Simulation
    • 13.1: Risk Analysis for Sanotronics LLC
    • 13.2: Inventory Policy Analysis for Promus Corp
    • 13.3: Simulation Modeling for Land Shark Inc.
    • 13.4: Simulation with Dependent Random Variables
    • 13.5: Simulation Considerations
    • 13: Exercises (18)
    • 13: Extra Problems (1)
    • 13: Case Problems
    • 13: Exploring Analytics Applet Exercises
    • 13: Test Bank (40)

  • Chapter 14: Linear Optimization Models
    • 14.1: A Simple Maximization Problem
    • 14.2: Solving the Par, Inc. Problem
    • 14.3: A Simple Minimization Problem
    • 14.4: Special Cases of Linear Program Outcomes
    • 14.5: Sensitivity Analysis
    • 14.6: General Linear Programming Notation and More Examples
    • 14.7: Generating an Alternative Optimal Solution for a Linear Program
    • 14: Exercises (18)
    • 14: Extra Problems (7)
    • 14: Case Problems (1)
    • 14: Exploring Analytics Applet Exercises (2)
    • 14: Test Bank (39)

  • Chapter 15: Integer Linear Optimization Models
    • 15.1: Types of Integer Linear Optimization Models
    • 15.2: Eastborne Realty, an Example of Integer Optimization
    • 15.3: Solving Integer Optimization Problems with Excel Solver
    • 15.4: Applications Involving Binary Variables
    • 15.5: Modeling Flexibility Provided by Binary Variables
    • 15.6: Generating Alternatives in Binary Optimization
    • 15: Exercises (20)
    • 15: Extra Problems (1)
    • 15: Case Problems
    • 15: Exploring Analytics Applet Exercises (2)
    • 15: Test Bank (43)

  • Chapter 16: Nonlinear Optimization Models
    • 16.1: A Production Application: Par, Inc. Revisited
    • 16.2: Local and Global Optima
    • 16.3: A Location Problem
    • 16.4: Markowitz Portfolio Model
    • 16.5: Adoption of a New Product: The Bass Forecasting Model
    • 16.6: Heuristic Optimization Using Excel's Evolutionary Method
    • 16: Exercises (25)
    • 16: Extra Problems
    • 16: Case Problems
    • 16: Exploring Analytics Applet Exercises (1)
    • 16: Test Bank (38)

  • Chapter 17: Decision Analysis
    • 17.1: Problem Formulation
    • 17.2: Decision Analysis Without Probabilities
    • 17.3: Decision Analysis with Probabilities
    • 17.4: Decision Analysis with Sample Information
    • 17.5: Computing Branch Probabilities with Bayes' Theorem
    • 17.6: Utility Theory
    • 17: Exercises (27)
    • 17: Extra Problems
    • 17: Case Problems (1)
    • 17: Test Bank (42)

  • Chapter A: Appendix
    • A: Appendix A: Basics of Excel (11)


Present the full range of analytics—from descriptive and predictive to prescriptive analytics—with Camm/Cochran/Fry/Ohlmann's market-leading BUSINESS ANALYTICS, 5E. Clear, step-by-step instructions teach students how to use Excel, R, or the Python-based Orange data mining software to solve more advanced analytics concepts. As the instructor, you choose your preferred software for teaching concepts. Extensive solutions to problems and cases save grading time while providing students with critical practice. Updates throughout this edition cover topics beyond the traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's analytical problem solving. In addition, WebAssign, the customizable online learning platform, offers an interactive eBook, auto-graded exercises, algorithmic practice problems and Exploring Analytics visualizations to strengthen students' understanding.

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.
  • All questions contain detailed solutions to the problem, available to students at your discretion.
  • Exploring Analytics Applet (AQ) problems ask students to answer questions using linked interactive statistical analysis applets.
  • New practice problems and solutions in the R and Orange appendices strengthen students' problem-solving skills for descriptive and predictive analytics.
  • Students can Talk to a Tutor for additional assistance through a link at the assignment level.
  • Test Bank (TB) questions can either be used for additional practice or as a way to conduct formative or summative assessments of student performance.
  • Case Problems (CP) allow students to work on more extensive problems related to the chapter material and work with larger data sets.
  • PowerPoint Presentations, Figures and Tables, Solutions Manuals, Case Solutions, Test Banks, Tip Sheets (XLSTAT, Minitab17, Mintab Express, and SPSS), Video Channels (XLSTAT and Minitab), and Support Centers (XLSTAT, Minitab, and SPSS) are available as textbook resources for instructors.
  • Downloadable DATAfiles (Excel and CSV), Downloadable Online Chapters, Appendix B: Tables, and Appendix D: Self-Test Solutions and Even-Numbered Answers are available as textbook resources for students and instructors.

    • Use the Textbook Edition Upgrade Tool to automatically update all of your assignments from the previous edition to corresponding questions in this textbook.

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
R - R Practice Problem
O - Orange Practice Problem


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 to Business Analytics
1.E 10 001 002 003 004 005 006 007 008 009 010
1.TB 38 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 040 046 051
Chapter 2: Descriptive Statistics
2.AQ 4 501 502 503 504
2.CP 2 001 001.alt
2.E 44 001 003.MI 003.MI.SA 003.alt 004.MI 004.MI.SA 005.MI 005.MI.SA 005.alt 006 006.alt 007 008 009 010 011 012 012.alt 013 014 015.MI 015.MI.SA 016.MI 016.MI.SA 017 018.MI 018.MI.SA 018.alt 019 019.alt 020 020.alt 021 021.alt 022 023.MI 023.MI.SA 024 025 026 027.MI 027.MI.SA 027.alt 028
2.R 4 1.001 1.002 1.003 1.004
2.TB 69 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 068 070 073
2.XP 10 001 001.alt 002.MI 002.MI.SA 002.alt 003.MI 003.MI.SA 003.alt 004 005
Chapter 3: Data Visualization
3.AQ 1 501
3.E 45 001 001.alt 003 003.alt 004 005.MI 005.MI.SA 005.alt 007 008 009.MI 009.MI.SA 009.alt 011 012.MI 012.MI.SA 012.alt 013 014 015 016 017.MI 017.MI.SA 017.alt 019.MI 019.MI.SA 019.alt 020.MI 020.MI.SA 020.alt 021 022 023 024 025.MI 025.MI.SA 025.alt 026 027 028 029 030 032 033 034
3.R 2 1.001 1.002
3.TB 48 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 049 056 063
3.XP 5 001.MI 001.MI.SA 001.alt 002 003
Chapter 4: Data Wrangling: Data Management and Data Cleaning Strategies
4.E 20 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020
4.TB 43 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 019 020 021 022 023 024 025 026 027 028 030 031 032 033 034 035 036 038 039 040 041 042 044 050 054 064
Chapter 5: Probability: An Introduction to Modeling Uncertainty
5.AQ 4 501a 501b 502 504
5.CP 2 001 001.alt
5.E 57 001 004 005.MI 005.MI.SA 006.MI 006.MI.SA 007.MI 007.MI.SA 008 009 010 011.MI 011.MI.SA 012.MI 012.MI.SA 013 014 015.MI 015.MI.SA 016 018.MI 018.MI.SA 019 020 021.MI 021.MI.SA 022 023.MI 023.MI.SA 024 025 026.MI 026.MI.SA 027 028 029.MI 029.MI.SA 030 031.MI 031.MI.SA 032 033.MI 033.MI.SA 034 035 036.MI 036.MI.SA 037.MI 037.MI.SA 038 039 040.MI 040.MI.SA 041 042.MI 042.MI.SA 044
5.R 6 1.001 1.002 2.001 2.002 2.003 2.004
5.TB 38 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 039 043 050
5.XP 2 001.MI 001.MI.SA
Chapter 6: Descriptive Data Mining
6.AQ 1 501
6.E 21 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 021 022 025
6.O 2 1.033 1.035
6.R 2 1.033 1.035
6.TB 49 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 049 052 057
Chapter 7: Statistical Inference
7.AQ 14 501 502 503 504a 504b 504c 504d 504e 505 506 507 508a 508b 508c
7.CP 1 001
7.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
7.R 4 1.001 2.001 3.001 4.001
7.TB 50 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 055 059 064
Chapter 8: Linear Regression
8.AQ 7 501 502 503 504 505 506a 506b
8.E 29 001.MI 001.MI.SA 001.alt 003 004 005.MI 005.MI.SA 006 007.MI 007.MI.SA 008 009.MI 009.MI.SA 009.alt 010.MI 010.MI.SA 010.alt 011.MI 011.MI.SA 011.alt 012 013 014 015 017 018 018.alt 019 021
8.R 10 1.001 1.002 1.003 1.004 1.005 2.001 2.002 2.003 2.004 3.001
8.TB 50 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 053 055 062
Chapter 9: Time Series Analysis and Forecasting
9.AQ 3 501 502 503
9.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
9.R 3 1.001 1.002 1.003
9.TB 44 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 045 053 060
Chapter 10: Predictive Data Mining: Regression Tasks
10.E 14 001 002 003 004 005 006 007 008 009 010 011 012 013 014
10.O 5 1.015 2.017 3.021 4.023 5.029
10.R 5 1.015 2.017 3.021 4.023 5.029
10.TB 20 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 022 023
Chapter 11: Predictive Data Mining: Classification Tasks
11.AQ 3 501a 501b 501c
11.E 29 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029
11.O 5 1.031 2.033 3.044 4.047 5.055
11.R 5 1.031 2.033 3.044 4.047 5.055
11.TB 36 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 036 039 045
Chapter 12: Spreadsheet Models
12.E 23 001 001.alt 002 005 007.MI 007.MI.SA 008 009 011 012 013 014 015 016 017 018 019 020.MI 020.MI.SA 021 022 024 025
12.TB 38 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 029 030 031 032 034 035 036 046 048 053 063
12.XP 2 001.MI 001.MI.SA
Chapter 13: Monte Carlo Simulation
13.E 18 003 004 005 006 008 009 011 012 013 014 015 016 018 020 023 025 026 031
13.TB 42 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 035 036 037 038 039 040 042 047 050
13.XP 1 001
Chapter 14: Linear Optimization Models
14.AQ 2 501a 501b
14.CP 1 002
14.E 18 001.MI 001.MI.SA 002 003 004 005 006 007.MI 007.MI.SA 008 009 011 013 014 015 016 018 022
14.TB 39 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 042 054 057
14.XP 7 001.MI 001.MI.SA 002 003 004 005 006
Chapter 15: Integer Linear Optimization Models
15.AQ 2 501a 501b
15.E 20 003 005 006 006.alt 007 008 010 011 012-013 015 016 017 019 021 022 026 027 028 029 030
15.TB 43 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 048 052 057
15.XP 1 001
Chapter 16: Nonlinear Optimization Models
16.AQ 1 501
16.E 25 001 002 003 004 005 006 007 007.alt 008 009 010 011 011.alt 012 013 013.alt 015 019 020 023 024 025 026 027 028
16.TB 38 001 002 003 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 046 047 050
Chapter 17: Decision Analysis
17.CP 1 001
17.E 27 001.MI 001.MI.SA 002 003.MI 003.MI.SA 004.MI 004.MI.SA 005 006 007 008.MI 008.MI.SA 010 011 013 014 015 016 017 018.MI 018.MI.SA 019 020 021 024 026 028
17.TB 42 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 043 049
Total 1359