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Business Analytics 6th 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 6e - Homework and Quizzes

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

  • Chapter 1: Introduction to Business Analytics
    • 1.1: Business Analytics and Decision Making
    • 1.2: Using Analytics for Improved Decision Making and Problem Solving
    • 1.3: Big Data
    • 1.4: Artificial Intelligence
    • 1.5: Business Analytics in Practice
    • 1.6: Legal and Ethical Issues in the Use of Data and Analytics
    • 1: Conceptual Problems (15)
    • 1: Extra Problems
    • 1: Test Bank (39)

  • 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: Conceptual Problems (15)
    • 2: Application Problems
    • 2: Extra Problems (12)
    • 2: R Practice Problems (4)
    • 2: Python Practice Problems
    • 2: Excel Online Activities
    • 2: Exploring Analytics Applet Exercises (4)
    • 2: Test Bank (79)

  • 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: Conceptual Problems (13)
    • 3: Application Problems (41)
    • 3: Extra Problems (5)
    • 3: R Practice Problems (2)
    • 3: Python Practice Problems
    • 3: Excel Online Activities
    • 3: Exploring Analytics Applet Exercises (1)
    • 3: Test Bank (52)

  • 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: Conceptual Problems (7)
    • 4: Application Problems (19)
    • 4: Extra Problems
    • 4: R Practice Problems
    • 4: Python Practice Problems
    • 4: Excel Online Activities
    • 4: Exploring Analytics Applet Exercises
    • 4: Test Bank (50)

  • 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: Conceptual Problems (10)
    • 5: Application Problems (58)
    • 5: Extra Problems (2)
    • 5: R Practice Problems (6)
    • 5: Python Practice Problems
    • 5: Excel Online Activities
    • 5: Exploring Analytics Applet Exercises (4)
    • 5: Test Bank (48)

  • Chapter 6: Unsupervised Machine Learning
    • 6.1: Dimension Reduction
    • 6.2: Cluster Analysis
    • 6.3: Association Rules
    • 6.4: Text Analytics
    • 6: Conceptual Problems (22)
    • 6: Extra Problems
    • 6: R Application Problems (2)
    • 6: Orange Application Problems (2)
    • 6: Python Application Problems
    • 6: Exploring Analytics Applet Exercises (1)
    • 6: Test Bank (58)

  • 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: Test of Independence
    • 7.7: Big Data, Statistical Inference, and Practical Significance
    • 7: Conceptual Problems (20)
    • 7: Application Problems (58)
    • 7: Extra Problems
    • 7: R Practice Problems (4)
    • 7: Python Practice Problems
    • 7: Excel Online Activities
    • 7: Exploring Analytics Applet Exercises (14)
    • 7: Test Bank (68)

  • 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: Conceptual Problems (20)
    • 8: Application Problems (28)
    • 8: Extra Problems
    • 8: R Practice Problems (11)
    • 8: Python Practice Problems
    • 8: Excel Online Activities
    • 8: Exploring Analytics Applet Exercises (7)
    • 8: Test Bank (65)

  • 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: Conceptual Problems (17)
    • 9: Application Problems (35)
    • 9: Extra Problems (3)
    • 9: R Practice Problems (3)
    • 9: Python Practice Problems
    • 9: Excel Online Activities
    • 9: Exploring Analytics Applet Exercises (3)
    • 9: Test Bank (53)

  • Chapter 10: Supervised Machine Learning: 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: Conceptual Problems (14)
    • 10: Extra Problems
    • 10: R Application Problems (5)
    • 10: Orange Application Problems (5)
    • 10: Python Application Problems
    • 10: Exploring Analytics Applet Exercises
    • 10: Test Bank (32)

  • Chapter 11: Supervised Machine Learning: 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: Conceptual Problems (29)
    • 11: Extra Problems
    • 11: R Application Problems (5)
    • 11: Orange Application Problems (5)
    • 11: Python Application Problems
    • 11: Exploring Analytics Applet Exercises (3)
    • 11: Test Bank (48)

  • 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: Conceptual Problems (10)
    • 12: Application Problems (23)
    • 12: Extra Problems (2)
    • 12: R Practice Problems
    • 12: Python Practice Problems
    • 12: Excel Online Activities
    • 12: Exploring Analytics Applet Exercises
    • 12: Test Bank (50)

  • 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: Conceptual Problems (10)
    • 13: Application Problems (18)
    • 13: Extra Problems (1)
    • 13: R Practice Problems
    • 13: Python Practice Problems
    • 13: Excel Online Activities
    • 13: Exploring Analytics Applet Exercises
    • 13: Test Bank (49)

  • 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: Conceptual Problems (10)
    • 14: Application Problems (20)
    • 14: Extra Problems (7)
    • 14: R Practice Problems
    • 14: Python Practice Problems
    • 14: Excel Online Activities
    • 14: Exploring Analytics Applet Exercises (2)
    • 14: Test Bank (45)

  • 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: Conceptual Problems (10)
    • 15: Application Problems (24)
    • 15: Extra Problems (1)
    • 15: R Practice Problems
    • 15: Python Practice Problems
    • 15: Exploring Analytics Applet Exercises (2)
    • 15: Test Bank (48)

  • 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: Conceptual Problems (10)
    • 16: Application Problems (25)
    • 16: Extra Problems
    • 16: R Practice Problems
    • 16: Python Practice Problems
    • 16: Exploring Analytics Applet Exercises (1)
    • 16: Test Bank (42)

  • 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: Conceptual Problems (12)
    • 17: Application Problems (25)
    • 17: Extra Problems
    • 17: R Practice Problems
    • 17: Python Practice Problems
    • 17: Test Bank (53)

  • Chapter 18: Artificial Intelligence
    • 18.1: What is AI?
    • 18.2: A Brief History of AI
    • 18.3: AI in Practice
    • 18.4: Large Language Models
    • 18.5: Prompt Engineering
    • 18.6: Ethical Concerns Related to AI
    • 18.7: Legal Considerations Related to AI
    • 18: Conceptual Problems (20)
    • 18: Application Problems (7)
    • 18: Extra Problems
    • 18: R Practice Problems
    • 18: Python Practice Problems
    • 18: Test Bank (41)

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


Present the full range of analytics -- from descriptive and predictive to prescriptive analytics to generative AI -- with Camm/Cochran/Fry/Ohlmann's market-leading Business Analytics, 6th Edition. Step-by-step instructions teach students how to use Excel, R and Python to solve advanced analytics concepts. Choose your preferred software for teaching concepts. Solutions to problems and cases save grading time and provide students with practice. Updates cover topics beyond the traditional quantitative concepts such as data wrangling, data visualization, where appendices for Tableau and Power BI expose and teach students the use of additional software, machine learning and AI, which are increasingly important in today's analytical problem solving. WebAssign 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.
  • Excel Activities (Excel) allow students to work on algorithmic spreadsheet problems integrated directly into WebAssign providing them with an authentic problem solving experience using Microsoft Excel and includes real-time feedback with contextual support directly from Office Online, system generated Excel Solution files and video walk-throughs of similar problems.
  • Exploring Analytics Applet (AQ) problems ask students to answer questions using linked interactive statistical analysis applets.
  • New Python Practice problems and solutions have been added to the existing 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.
  • PowerPoint Presentations, Figures and Tables, Solutions Manuals, Case Solutions, Test Banks, a JMP Tip Sheet are available as textbook resources for instructors.
  • Downloadable DATAfiles (Excel and CSV), Appendix B: Tables, and Appendix C: 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
C - Conceptual Problem
A - Application Problem
MI - Master It
MI.SA - Stand Alone Master It
TB - Test Bank
AQ - Exploring Analytics Applet
R - R Problem
O - Orange Problem
P - Python Problem
Lab - Lab
XP - Extra Problem
Excel - Excel Online Activity


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.C 15 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
1.TB 39 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 038 040 046 052 057
Chapter 2: Descriptive Statistics
2.A 38 015.MI 015.MI.SA 015.alt 016.MI 016.MI.SA 016.alt 017 017.alt 018 019 020 021 022 022.alt 023 024.MI 024.MI.SA 025 026.MI 026.MI.SA 026.alt 027 027.alt 028 028.alt 029 029.alt 030 031.MI 031.MI.SA 032 033 034 035 036.MI 036.MI.SA 036.alt 037
2.AQ 4 501 502 503 504
2.C 15 001 002.MI 002.MI.SA 003 004 005 006 007 008 009.MI 009.MI.SA 010 011 012 013
2.R 4 1.001 1.002 1.003 1.004
2.TB 79 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 070 074 075 077 078 080 081 085 091 094 097 098
2.XP 12 001 001.alt 002.MI 002.MI.SA 002.alt 003.MI 003.MI.SA 003.alt 004 005 006 007
Chapter 3: Data Visualization
3.A 41 015 015.alt 017 018 019.MI 019.MI.SA 019.alt 021 022 023.MI 023.MI.SA 023.alt 025 026.MI 026.MI.SA 026.alt 027 028 029 030 031.MI 031.MI.SA 031.alt 033.MI 033.MI.SA 033.alt 034.MI 034.MI.SA 034.alt 035 036 037 038.MI 038.MI.SA 038.alt 039 040 041 042 044 045
3.AQ 1 501
3.C 13 001 002 003 004 005 006 007 008 009 010 011 012 013
3.R 2 1.001 1.002
3.TB 52 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 049 051 054 061 073 080
3.XP 5 001.MI 001.MI.SA 001.alt 002 003
Chapter 4: Data Wrangling: Data Management and Data Cleaning Strategies
4.A 19 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026
4.C 7 001 002 003 004 005 006 007
4.TB 50 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 047 049 050 051 053 055 059 068 501.XP
Chapter 5: Probability: An Introduction to Modeling Uncertainty
5.A 58 011 014 015.MI 015.MI.SA 016.MI 016.MI.SA 017.MI 017.MI.SA 018 019 020 021.MI 021.MI.SA 022.MI 022.MI.SA 023 024 025.MI 025.MI.SA 026 028.MI 028.MI.SA 029 030 031.MI 031.MI.SA 032 033 034.MI 034.MI.SA 035 036 037.MI 037.MI.SA 038 039 040.MI 040.MI.SA 041 042.MI 042.MI.SA 043 044.MI 044.MI.SA 045 046 047.MI 047.MI.SA 048.MI 048.MI.SA 049 050 051.MI 051.MI.SA 052 053.MI 053.MI.SA 055
5.AQ 4 501a 501b 502 504
5.C 10 001 002 003 004 005 006 007 008 009 010
5.R 6 1.001 1.002 2.001 2.002 2.003 2.004
5.TB 48 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 041 042 043 047 050 051 052 057 061 072 501.XP 502.XP
5.XP 2 001.MI 001.MI.SA
Chapter 6: Unsupervised Machine Learning
6.AQ 1 501
6.C 22 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 021 022 024 025
6.O 2 1.033 1.035
6.R 2 1.033 1.035
6.TB 58 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 051 053 055 057 058 059 060 061 501.XP 502.XP 503.XP
Chapter 7: Statistical Inference
7.A 58 021 021.alt 023 025.MI 025.MI.SA 027.MI 027.MI.SA 028 029 030 031 032 033 034 035.MI 035.MI.SA 035.alt 037 037.alt 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053.MI 053.MI.SA 053.alt 054 055.MI 055.MI.SA 056 057 058 059 060.MI 060.MI.SA 061 063 065 066 067 068 069 070 071 072 073 074 075
7.AQ 14 501 502 503 504a 504b 504c 504d 504e 505 506 507 508a 508b 508c
7.C 20 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020
7.R 4 1.001 2.001 3.001 4.001
7.TB 68 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 056 057 059 060 062 063 065 067 070 074 075 081 088 501.XP
Chapter 8: Linear Regression
8.A 28 021.MI 021.MI.SA 021.alt 023 024 025.MI 025.MI.SA 026 027.MI 027.MI.SA 028 029.MI 029.MI.SA 029.alt 030.MI 030.MI.SA 030.alt 031.MI 031.MI.SA 031.alt 032 033 034 035 037 038 039 041
8.AQ 7 501 502 503 504 505 506a 506b
8.C 20 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020
8.R 11 1.001 1.002 1.003 1.004 1.005 2.001 2.002 2.003 2.004 3.001 3.003
8.TB 65 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 050 052 054 055 057 059 064 066 067 069 072 075 076 078 089 501.XP 502.XP
Chapter 9: Time Series Analysis and Forecasting
9.A 35 016 016.alt 017 017.alt 018 019 020.MI 020.MI.SA 021 021.alt 022.MI 022.MI.SA 022.alt 023 024 024.alt 025 026 027 028.MI 028.MI.SA 028.alt 030 030.alt 031 032.MI 032.MI.SA 033 033.alt 034.MI 034.MI.SA 034.alt 035 035.alt 036
9.AQ 3 501 502 503
9.C 17 001 002 003 004 005 006.MI 006.MI.SA 007 008 009 010.MI 010.MI.SA 011 012 013 014 015
9.R 3 1.001 1.002 1.003
9.TB 53 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 045 047 048 050 051 054 059 061 066 073 501.XP
9.XP 3 001.MI 001.MI.SA 002
Chapter 10: Supervised Machine Learning: Regression Tasks
10.C 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 32 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 021 023 024 025 026 027 028 030 031 033 034 035 038 039
Chapter 11: Supervised Machine Learning: Classification Tasks
11.AQ 3 501a 501b 501c
11.C 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 48 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 036 040 044 046 047 049 052 053 054 055 056 062 067 501.XP 502.XP
Chapter 12: Spreadsheet Models
12.A 23 011 011.alt 012 015 017.MI 017.MI.SA 018 019 021 022 023 024 025 026 027 028 029 030.MI 030.MI.SA 031 032 034 035
12.C 10 001 002 003 004 005 006 007 008 009 010
12.TB 50 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 034 035 036 037 038 041 043 045 046 047 048 052 056 060 066 072 073 077 084
12.XP 2 001.MI 001.MI.SA
Chapter 13: Monte Carlo Simulation
13.A 18 013 014 015 016 018 019 021 022 023 024 025 026 028 030 033 035 036 041
13.C 10 001 002 003 004 005 006 007 008 009 010
13.TB 49 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 040 041 043 048 050 053 054 055 062 067 070
13.XP 1 001
Chapter 14: Linear Optimization Models
14.A 20 011.MI 011.MI.SA 012 013 014 015 016 017.MI 017.MI.SA 018 019 021 023 024 025 026 028 032 042 043
14.AQ 2 501a 501b
14.C 10 001 002 003 004 005 006 007 008 009 010
14.TB 45 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 038 042 044 048 049 051 068 501.XP 502.XP
14.XP 7 001.MI 001.MI.SA 002 003 004 005 006
Chapter 15: Integer Linear Optimization Models
15.A 24 013 015 016 016.alt 017 018 020 021 022-023 025 026 027 029 031 032 036 037 038 039 040 041 042 043 044
15.AQ 2 501a 501b
15.C 10 001 002 003 004 005 006 007 008 009 010
15.TB 48 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 045 048 051 063 067 072
15.XP 1 001
Chapter 16: Nonlinear Optimization Models
16.A 25 011 012 013 014 015 016 017 017.alt 018 019 020 021 021.alt 022 023 023.alt 025 029 030 033 034 035 036 037 038
16.AQ 1 501
16.C 10 001 002 003 004 005 006 007 008 009 010
16.TB 42 001 002 003 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 039 041 051 059 060 063
Chapter 17: Decision Analysis
17.A 25 012.MI 012.MI.SA 013 014.MI 014.MI.SA 015.MI 015.MI.SA 016 017 018 019.MI 019.MI.SA 021 022 024 025 026 027 028 029 030 031 034 036 038
17.C 12 001 002 003 004 005 006 007 008.MI 008.MI.SA 009 010 011
17.TB 53 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 042 044 046 047 049 051 054 055 058 059 064 501.XP 502.XP
Chapter 18: Artificial Intelligence
18.A 7 021 022 023 024 025 026 027
18.C 20 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020
18.TB 41 002 003 004 005 006 007 008 009 010 011 012 014 015 016 017 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045
Total 1743  

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