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A balanced and holistic approach to business analytics Business Analytics teaches the fundamental concepts of modern business analytics and provides vital tools in understanding how data analysis works in today's organisations. Author James Evans takes a fair and comprehensive, approach, examining business analytics from both descriptive and predictive perspectives. Students learn how to apply basic principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions.
And included access to commercial grade analytics software gives students real-world experience and career-focused value. As such, the 3rd Edition has gone through an extensive revision and now relies solely on Excel, enhancing students' skills in the program and basic understanding of fundamental concepts. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps.
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- Höfundur: James Evans
- Útgáfa:3
- Útgáfudagur: 2020-03-13
- Hægt að prenta út 2 bls.
- Hægt að afrita 2 bls.
- Format:Page Fidelity
- ISBN 13: 9781292339047
- Print ISBN: 9781292339061
- ISBN 10: 1292339047
Efnisyfirlit
- Half Title Page
- Title Page
- Copyright Page
- Brief Contents
- Contents
- Preface
- About the Author
- Credits
- Part 1: Foundations of Business Analytics
- Chapter 1: Introduction to Business Analytics
- Learning Objectives
- What Is Business Analytics?
- Using Business Analytics
- Impacts and Challenges
- Evolution of Business Analytics
- Analytic Foundations
- Modern Business Analytics
- Software Support and Spreadsheet Technology
- Analytics in Practice: Social Media Analytics
- Descriptive, Predictive, and Prescriptive Analytics
- Analytics in Practice: Analytics in the Home Lending and Mortgage Industry
- Data for Business Analytics
- Big Data
- Data Reliability and Validity
- Models in Business Analytics
- Descriptive Models
- Predictive Models
- Prescriptive Models
- Model Assumptions
- Uncertainty and Risk
- Problem Solving with Analytics
- Recognizing a Problem
- Defining the Problem
- Structuring the Problem
- Analyzing the Problem
- Interpreting Results and Making a Decision
- Implementing the Solution
- Analytics in Practice: Developing Effective Analytical Tools at Hewlett‐Packard
- Key Terms
- Chapter 1 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Appendix A1: Basic Excel Skills
- Excel Formulas and Addressing
- Copying Formulas
- Useful Excel Tips
- Excel Functions
- Basic Excel Functions
- Functions for Specific Applications
- Insert Function
- Date and Time Functions
- Miscellaneous Excel Functions and Tools
- Range Names
- VALUE Function
- Paste Special
- Concatenation
- Error Values
- Problems and Exercises
- Excel Formulas and Addressing
- Chapter 1: Introduction to Business Analytics
- Chapter 2: Database Analytics
- Learning Objectives
- Data Sets and Databases
- Using Range Names in Databases
- Analytics in Practice: Using Big Data to Monitor Water Usage in Cary, North Carolina
- Data Queries: Tables, Sorting, and Filtering
- Sorting Data in Excel
- Pareto Analysis
- Filtering Data
- Database Functions
- Analytics in Practice: Discovering the Value of Database Analytics at Allders International
- Logical Functions
- Lookup Functions for Database Queries
- Excel Template Design
- Data Validation Tools
- Form Controls
- PivotTables
- PivotTable Customization
- Slicers
- Key Terms
- Chapter 2 Technology Help
- Problems and Exercises
- Case: People’s Choice Bank
- Case: Drout Advertising Research Project
- Chapter 3: Data Visualization
- Learning Objectives
- The Value of Data Visualization
- Tools and Software for Data Visualization
- Analytics in Practice: Data Visualization for the New York City Police Department’s Domain Awarene
- Creating Charts in Microsoft Excel
- Column and Bar Charts
- Data Label and Data Table Chart Options
- Line Charts
- Pie Charts
- Area Charts
- Scatter Charts and Orbit Charts
- Bubble Charts
- Combination Charts
- Radar Charts
- Stock Charts
- Charts from PivotTables
- Geographic Data
- Other Excel Data Visualization Tools
- Data Bars
- Color Scales
- Icon Sets
- Sparklines
- Dashboards
- Analytics in Practice: Driving Business Transformation with IBM Business Analytics
- Key Terms
- Chapter 3 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Appendix A3: Additional Tools for Data Visualization
- Hierarchy Charts
- Waterfall Charts
- PivotCharts
- Tableau
- Problems and Exercises
- Hierarchy Charts
- Learning Objectives
- Analytics in Practice: Applications of Statistics in Health care
- Metrics and Data Classification
- Frequency Distributions and Histograms
- Frequency Distributions for Categorical Data
- Relative Frequency Distributions
- Frequency Distributions for Numerical Data
- Grouped Frequency Distributions
- Cumulative Relative Frequency Distributions
- Constructing Frequency Distributions Using PivotTables
- Percentiles and Quartiles
- Cross‐Tabulations
- Descriptive Statistical Measures
- Populations and Samples
- Statistical Notation
- Measures of Location: Mean, Median, Mode, and Midrange
- Using Measures of Location in Business Decisions
- Measures of Dispersion: Range, Interquartile Range, Variance, and Standard Deviation
- Chebyshev’s Theorem and the Empirical Rules
- Standardized Values (Z‐Scores)
- Coefficient of Variation
- Measures of Shape
- Excel Descriptive Statistics Tool
- Computing Descriptive Statistics for Frequency Distributions
- Descriptive Statistics for Categorical Data: The Proportion
- Statistics in PivotTables
- Measures of Association
- Covariance
- Correlation
- Excel Correlation Tool
- Outliers
- Using Descriptive Statistics to Analyze Survey Data
- Statistical Thinking in Business Decisions
- Variability in Samples
- Analytics in Practice: Applying Statistical Thinking to Detecting Financial Problems
- Key Terms
- Chapter 4 Technology Help
- Problems and Exercises
- Case: Drout Advertising Research Project
- Case: Performance Lawn Equipment
- Appendix A4: Additional Charts for Descriptive Statistics in Excel for Windows
- Problems and Exercises
- Learning Objectives
- Basic Concepts of Probability
- Experiments and Sample Spaces
- Combinations and Permutations
- Probability Definitions
- Probability Rules and Formulas
- Joint and Marginal Probability
- Conditional Probability
- Random Variables and Probability Distributions
- Discrete Probability Distributions
- Expected Value of a Discrete Random Variable
- Using Expected Value in Making Decisions
- Variance of a Discrete Random Variable
- Bernoulli Distribution
- Binomial Distribution
- Poisson Distribution
- Analytics in Practice: Using the Poisson Distribution for Modeling Bids on Priceline
- Continuous Probability Distributions
- Properties of Probability Density Functions
- Uniform Distribution
- Normal Distribution
- The NORM.INV Function
- Standard Normal Distribution
- Using Standard Normal Distribution Tables
- Exponential Distribution
- Triangular Distribution
- Data Modeling and Distribution Fitting
- Goodness of Fit: Testing for Normality of an Empirical Distribution
- Analytics in Practice: The value of Good Data Modeling in Advertising
- Key Terms
- Chapter 5 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Learning Objectives
- Statistical Sampling
- Sampling Methods
- Analytics in Practice: Using Sampling Techniques to Improve Distribution
- Estimating Population Parameters
- Unbiased Estimators
- Errors in Point Estimation
- Understanding Sampling Error
- Sampling Distributions
- Sampling Distribution of the Mean
- Applying the Sampling Distribution of the Mean
- Interval Estimates
- Confidence Intervals
- Confidence Interval for the Mean with Known Population Standard Deviation
- The t‐Distribution
- Confidence Interval for the Mean with Unknown Population Standard Deviation
- Confidence Interval for a Proportion
- Additional Types of Confidence Intervals
- Using Confidence Intervals for Decision Making
- Data Visualization for Confidence Interval Comparison
- Prediction Intervals
- Confidence Intervals and Sample Size
- Key Terms
- Chapter 6 Technology Help
- Problems and Exercises
- Case: Drout Advertising Research Project
- Case: Performance Lawn Equipment
- Learning Objectives
- Hypothesis Testing
- Hypothesis‐Testing Procedure
- One‐Sample Hypothesis Tests
- Understanding Potential Errors in Hypothesis Testing
- Selecting the Test Statistic
- Finding Critical Values and Drawing a Conclusion
- Two‐Tailed Test of Hypothesis for the Mean
- Summary of One‐Sample Hypothesis Tests for the Mean
- p‐Values
- One‐Sample Tests for Proportions
- Confidence Intervals and Hypothesis Tests
- An Excel Template for One‐Sample Hypothesis Tests
- Two‐Sample Hypothesis Tests
- Two‐Sample Tests for Differences in Means
- Two‐Sample Test for Means with Paired Samples
- Two‐Sample Test for Equality of Variances
- Analysis of Variance (ANOVA)
- Assumptions of ANOVA
- Chi‐Square Test for Independence
- Cautions in Using the Chi‐Square Test
- Chi‐Square Goodness of Fit Test
- Analytics in Practice: Using Hypothesis Tests and Business Analytics in a Help Desk Service Improvem
- Key Terms
- Chapter 7 Technology Help
- Problems and Exercises
- Case: Drout Advertising Research Project
- Case: Performance Lawn Equipment
- Chapter 8: Trendlines and Regression Analysis
- Learning Objectives
- Modeling Relationships and Trends in Data
- Analytics in Practice: Using Predictive Trendline Models at Procter & Gamble
- Simple Linear Regression
- Finding the Best‐Fitting Regression Line
- Using Regression Models for Prediction
- Least‐Squares Regression
- Simple Linear Regression with Excel
- Regression as Analysis of Variance
- Testing Hypotheses for Regression Coefficients
- Confidence Intervals for Regression Coefficients
- Residual Analysis and Regression Assumptions
- Checking Assumptions
- Multiple Linear Regression
- Analytics in Practice: Using Linear Regression and Interactive Risk Simulators to Predict Performanc
- Building Good Regression Models
- Correlation and Multicollinearity
- Practical Issues in Trendline and Regression Modeling
- Regression with Categorical Independent Variables
- Categorical Variables with More Than Two Levels
- Regression Models with Nonlinear Terms
- Key Terms
- Chapter 8 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 9: Forecasting Techniques
- Learning Objectives
- Analytics in Practice: Forecasting Call‐Center Demand at L.L. Bean
- Qualitative and Judgmental Forecasting
- Historical Analogy
- The Delphi Method
- Indicators and Indexes
- Statistical Forecasting Models
- Forecasting Models for Stationary Time Series
- Moving Average Models
- Error Metrics and Forecast Accuracy
- Exponential Smoothing Models
- Forecasting Models for Time Series with a Linear Trend
- Double Exponential Smoothing
- Regression‐Based Forecasting for Time Series with a Linear Trend
- Forecasting Time Series with Seasonality
- Regression‐Based Seasonal Forecasting Models
- Holt‐Winters Models for Forecasting Time Series with Seasonality and No Trend
- Holt‐Winters Models for Forecasting Time Series with Seasonality and Trend
- Selecting Appropriate Time‐Series‐Based Forecasting Models
- Regression Forecasting with Causal Variables
- The Practice of Forecasting
- Analytics in Practice: Forecasting at NBCUniversal
- Key Terms
- Chapter 9 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 10: Introduction to Data Mining
- Learning Objectives
- The Scope of Data Mining
- Cluster Analysis
- Measuring Distance Between Objects
- Normalizing Distance Measures
- Clustering Methods
- Classification
- An Intuitive Explanation of Classification
- Measuring Classification Performance
- Classification Techniques
- Association
- Cause‐and‐Effect Modeling
- Analytics In Practice: Successful Business Applications of Data Mining
- Key Terms
- Chapter 10 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 11: Spreadsheet Modeling and Analysis
- Learning Objectives
- Analytics in Practice: Using Spreadsheet Modeling and Analysis at Nestlé
- Model‐Building Strategies
- Building Models Using Logic and Business Principles
- Building Models Using Influence Diagrams
- Building Models Using Historical Data
- Model Assumptions, Complexity, and Realism
- Implementing Models on Spreadsheets
- Spreadsheet Design
- Spreadsheet Quality
- Data Validation
- Analytics in Practice: Spreadsheet Engineering at Procter & Gamble
- Descriptive Spreadsheet Models
- Staffing Decisions
- Single‐Period Purchase Decisions
- Overbooking Decisions
- Analytics in Practice: Using an Overbooking Model at a Student Health Clinic
- Retail Markdown Decisions
- Predictive Spreadsheet Models
- New Product Development Model
- Cash Budgeting
- Retirement Planning
- Project Management
- Prescriptive Spreadsheet Models
- Portfolio Allocation
- Locating Central Facilities
- Job Sequencing
- Analyzing Uncertainty and Model Assumptions
- What‐If Analysis
- Data Tables
- Scenario Manager
- Goal Seek
- Key Terms
- Chapter 11 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 12: Simulation and Risk Analysis
- Learning Objectives
- Monte Carlo Simulation
- Random Sampling from Probability Distributions
- Generating Random Variates using Excel Functions
- Discrete Probability Distributions
- Uniform Distributions
- Exponential Distributions
- Normal Distributions
- Binomial Distributions
- Triangular Distributions
- Monte Carlo Simulation in Excel
- Profit Model Simulation
- New Product Development
- Retirement Planning
- Single‐Period Purchase Decisions
- Overbooking Decisions
- Project Management
- Analytics in Practice: Implementing Large‐Scale Monte Carlo Spreadsheet Models
- Dynamic Systems Simulation
- Simulating Waiting Lines
- Analytics in Practice: Using Systems Simulation for Agricultural Product Development
- Key Terms
- Chapter 12 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 13: Linear Optimization
- Learning Objectives
- Optimization Models
- Analytics in Practice: Using Optimization Models for Sales Planning at NBC
- Developing Linear Optimization Models
- Identifying Decision Variables, the Objective, and Constraints
- Developing a Mathematical Model
- More About Constraints
- Implementing Linear Optimization Models on Spreadsheets
- Excel Functions to Avoid in Linear Optimization
- Solving Linear Optimization Models
- Solver Answer Report
- Graphical Interpretation of Linear Optimization with Two Variables
- How Solver Works
- How Solver Creates Names in Reports
- Solver Outcomes and Solution Messages
- Unique Optimal Solution
- Alternative (Multiple) Optimal Solutions
- Unbounded Solution
- Infeasibility
- Applications of Linear Optimization
- Blending Models
- Dealing with Infeasibility
- Portfolio Investment Models
- Scaling Issues in Using Solver
- Transportation Models
- Multiperiod Production Planning Models
- Multiperiod Financial Planning Models
- Analytics in Practice: Linear Optimization in Bank Financial Planning
- Key Terms
- Chapter 13 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 14: Integer and Nonlinear Optimization
- Learning Objectives
- Integer Linear Optimization Models
- Models with General Integer Variables
- Workforce‐Scheduling Models
- Alternative Optimal Solutions
- Models with Binary Variables
- Using Binary Variables to Model Logical Constraints
- Applications in Supply Chain Optimization
- Analytics in Practice: Supply Chain Optimization at Procter & Gamble
- Nonlinear Optimization Models
- A Nonlinear Pricing Decision Model
- Quadratic Optimization
- Practical Issues Using Solver for Nonlinear Optimization
- Analytics in Practice: Applying Nonlinear Optimization at Prudential Securities
- Non‐Smooth Optimization
- Evolutionary Solver
- Evolutionary Solver for Sequencing and Scheduling Models
- The Traveling Salesperson Problem
- Key Terms
- Chapter 14 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 15: Optimization Analytics
- Learning Objectives
- What‐If Analysis for Optimization Models
- Solver Sensitivity Report
- Using the Sensitivity Report
- Degeneracy
- Interpreting Solver Reports for Nonlinear Optimization Models
- Models with Bounded Variables
- Auxiliary Variables for Bound Constraints
- What‐If Analysis for Integer Optimization Models
- Visualization of Solver Reports
- Using Sensitivity Information Correctly
- Key Terms
- Chapter 15 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
- Chapter 16: Decision Analysis
- Learning Objectives
- Formulating Decision Problems
- Decision Strategies Without Outcome Probabilities
- Decision Strategies for a Minimize Objective
- Decision Strategies for a Maximize Objective
- Decisions with Conflicting Objectives
- Decision Strategies with Outcome Probabilities
- Average Payoff Strategy
- Expected Value Strategy
- Evaluating Risk
- Decision Trees
- Decision Trees and Risk
- Sensitivity Analysis in Decision Trees
- The Value of Information
- Decisions with Sample Information
- Bayes’s Rule
- Utility and Decision Making
- Constructing a Utility Function
- Exponential Utility Functions
- Analytics in Practice: Using Decision Analysis in Drug Development
- Key Terms
- Chapter 16 Technology Help
- Problems and Exercises
- Case: Performance Lawn Equipment
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- F
- G
- H
- I
- J
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- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X
- Y
- Z
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- Gerð : 208
- Höfundur : 9722
- Útgáfuár : 2020
- Leyfi : 380