Data Science for Business and Decision Making

Cover
Data Science for Business
and Decision Making
Copyright
Dedication
Epigraph
1
	Introduction to Data Analysis and Decision Making
		Introduction: Hierarchy Between Data, Information, and Knowledge
		Overview of the Book
		Final Remarks
2
	Types of Variables and Measurement and Accuracy Scales
		Introduction
		Types of Variables
			Nonmetric or Qualitative Variables
			Metric or Quantitative Variables
		Types of Variables x Scales of Measurement
			Nonmetric Variables-Nominal Scale
			Nonmetric Variables-Ordinal Scale
			Quantitative Variable-Interval Scale
			Quantitative Variable-Ratio Scale
		Types of Variables x Number of Categories and Scales of Accuracy
			Dichotomous or Binary Variable (Dummy)
			Polychotomous Variable
			Discrete Quantitative Variable
			Continuous Quantitative Variable
		Final Remarks
		Exercises
Part II:
Descriptive Statistics
3
Univariate Descriptive Statistics
	Introduction
	Frequency Distribution Table
		Frequency Distribution Table for Qualitative Variables
		Frequency Distribution Table for Discrete Data
		Frequency Distribution Table for Continuous Data Grouped into Classes
	Graphical Representation of the Results
		Graphical Representation for Qualitative Variables
			Bar Chart
			Pie Chart
			Pareto Chart
		Graphical Representation for Quantitative Variables
			Line Graph
			Scatter Plot
			Histogram
			Stem-and-Leaf Plot
			Boxplot or Box-and-Whisker Diagram
	The Most Common Summary-Measures in Univariate Descriptive Statistics
		Measures of Position or Location
			Measures of Central Tendency
				Arithmetic Mean
					Case 1: Simple Arithmetic Mean of Ungrouped Discrete and Continuous Data
					Case 2: Weighted Arithmetic Mean of Ungrouped Discrete and Continuous Data
					Case 3: Arithmetic Mean of Grouped Discrete Data
					Case 4: Arithmetic Mean of Continuous Data Grouped into Classes
				Median
					Case 1: Median of Ungrouped Discrete and Continuous Data
					Case 2: Median of Grouped Discrete Data
					Case 3: Median of Continuous Data Grouped into Classes
				Mode
					Case 1: Mode of Ungrouped Data
					Case 2: Mode of Grouped Qualitative or Discrete Data
					Case 3: Mode of Continuous Data Grouped into Classes
			Quantiles
				Quartiles
				Deciles
				Percentiles
					Case 1: Quartiles, Deciles, and Percentiles of Ungrouped Discrete and Continuous Data
					Case 2: Quartiles, Deciles, and Percentiles of Grouped Discrete Data
					Case 3: Quartiles, Deciles, and Percentiles of Continuous Data Grouped into Classes
			Identifying the Existence of Univariate Outliers
		Measures of Dispersion or Variability
			Range
			Average Deviation
				Case 1: Average Deviation of Ungrouped Discrete and Continuous Data
				Case 2: Average Deviation of Grouped Discrete Data
				Case 3: Average Deviation of Continuous Data Grouped into Classes
			Variance
				Case 1: Variance of Ungrouped Discrete and Continuous Data
				Case 2: Variance of Grouped Discrete Data
				Case 3: Variance of Continuous Data Grouped into Classes
			Standard Deviation
			Standard Error
			Coefficient of Variation
		Measures of Shape
			Measures of Skewness
				Pearsons First Coefficient of Skewness
				Pearsons Second Coefficient of Skewness
				Bowleys Coefficient of Skewness
				Fishers Coefficient of Skewness
				Coefficient of Skewness on Stata
			Measures of Kurtosis
				Coefficient of Kurtosis
				Fishers Coefficient of Kurtosis
				Coefficient of Kurtosis on Stata
	A Practical Example in Excel
	A Practical Example on SPSS
		Frequencies Option
		Descriptives Option
		Explore Option
	A Practical Example on Stata
		Univariate Frequency Distribution Tables on Stata
		Summary of Univariate Descriptive Statistics on Stata
		Calculating Percentiles on Stata
		Charts on Stata: Histograms, Stem-and-Leaf, and Boxplots
			Histogram
			Stem-and-Leaf
			Boxplot
	Final Remarks
	Exercises
4
Bivariate Descriptive Statistics
	Introduction
	Association Between Two Qualitative Variables
		Joint Frequency Distribution Tables
		Measures of Association
			Chi-Square Statistic
			Other Measures of Association Based on Chi-Square
			Spearmans Coefficient
	Correlation Between Two Quantitative Variables
		Joint Frequency Distribution Tables
		Graphical Representation Through a Scatter Plot
		Measures of Correlation
			Covariance
			Pearsons Correlation Coefficient
	Final Remarks
	Exercises
Part III: Probabilistic Statistics
5
Introduction to Probability
	Introduction
	Terminology and Concepts
		Random Experiment
		Sample Space
		Events
		Unions, Intersections, and Complements
		Independent Events
		Mutually Exclusive Events
	Definition of Probability
	Basic Probability Rules
		Probability Variation Field
		Probability of the Sample Space
		Probability of an Empty Set
		Probability Addition Rule
		Probability of a Complementary Event
		Probability Multiplication Rule for Independent Events
	Conditional Probability
		Probability Multiplication Rule
	Bayes´ Theorem
	Combinatorial Analysis
		Arrangements
		Combinations
		Permutations
	Final Remarks
	Exercises
6
Random Variables and Probability Distributions
	Introduction
	Random Variables
		Discrete Random Variable
			Expected Value of a Discrete Random Variable
			Variance of a Discrete Random Variable
			Cumulative Distribution Function of a Discrete Random Variable
		Continuous Random Variable
			Expected Value of a Continuous Random Variable
			Variance of a Continuous Random Variable
			Cumulative Distribution Function of a Continuous Random Variable
	Probability Distributions for Discrete Random Variables
		Discrete Uniform Distribution
		Bernoulli Distribution
		Binomial Distribution
			Relationship Between the Binomial and the Bernoulli Distributions
		Geometric Distribution
		Negative Binomial Distribution
			Relationship Between the Negative Binomial and the Binomial Distributions
			Relationship Between the Negative Binomial and the Geometric Distributions
		Hypergeometric Distribution
			Approximation of the Hypergeometric Distribution by the Binomial
		Poisson Distribution
			Approximation of the Binomial by the Poisson Distribution
	Probability Distributions for Continuous Random Variables
		Uniform Distribution
		Normal Distribution
			Approximation of the Binomial by the Normal Distribution
			Approximation of the Poisson by the Normal Distribution
		Exponential Distribution
			Relationship Between the Poisson and the Exponential Distribution
		Gamma Distribution
			Special Cases of the Gamma Distribution
			Relationship Between the Poisson and the Gamma Distribution
		Chi-Square Distribution
		Students t Distribution
		Snedecors F Distribution
			Relationship Between Students t and Snedecors F Distribution
	Final Remarks
	Exercises
Part IV: Statistical Inference
7
Sampling
	Introduction
	Probability or Random Sampling
		Simple Random Sampling
			Simple Random Sampling Without Replacement
			Simple Random Sampling With Replacement
		Systematic Sampling
		Stratified Sampling
		Cluster Sampling
	Nonprobability or Nonrandom Sampling
		Convenience Sampling
		Judgmental or Purposive Sampling
		Quota Sampling
		Geometric Propagation or Snowball Sampling
	Sample Size
		Size of a Simple Random Sample
			Sample Size to Estimate the Mean of an Infinite Population
			Sample Size to Estimate the Mean of a Finite Population
			Sample Size to Estimate the Proportion of an Infinite Population
			Sample Size to Estimate the Proportion of a Finite Population
		Size of the Systematic Sample
		Size of the Stratified Sample
			Sample Size to Estimate the Mean of an Infinite Population
			Sample Size to Estimate the Mean of a Finite Population
			Sample Size to Estimate the Proportion of an Infinite Population
			Sample Size to Estimate the Proportion of a Finite Population
		Size of a Cluster Sample
			Size of a One-Stage Cluster Sample
				Sample Size to Estimate the Mean of an Infinite Population
				Sample Size to Estimate the Mean of a Finite Population
				Sample Size to Estimate the Proportion of an Infinite Population
				Sample Size to Estimate the Proportion of a Finite Population
			Size of a Two-Stage Cluster Sample
	Final Remarks
	Exercises
8
Estimation
	Introduction
	Point and Interval Estimation
		Point Estimation
		Interval Estimation
	Point Estimation Methods
		Method of Moments
		Ordinary Least Squares
		Maximum Likelihood Estimation
	Interval Estimation or Confidence Intervals
		Confidence Interval for the Population Mean (μ)
			Known Population Variance (σ2)
			Unknown Population Variance (σ2)
		Confidence Interval for Proportions
		Confidence Interval for the Population Variance
	Final Remarks
	Exercises
9
Hypotheses Tests
	Introduction
	Parametric Tests
	Univariate Tests for Normality
		Kolmogorov-Smirnov Test
		Shapiro-Wilk Test
		Shapiro-Francia Test
		Solving Tests for Normality by Using SPSS Software
		Solving Tests for Normality by Using Stata
			Kolmogorov-Smirnov Test on the Stata Software
			Shapiro-Wilk Test on the Stata Software
			Shapiro-Francia Test on the Stata Software
	Tests for the Homogeneity of Variances
		Bartletts χ2 Test
		Cochrans C Test
		Hartleys Fmax Test
		Levenes F-Test
		Solving Levenes Test by Using SPSS Software
		Solving Levenes Test by Using the Stata Software
	Hypotheses Tests Regarding a Population Mean (μ) From One Random Sample
		Z Test When the Population Standard Deviation (σ) Is Known and the Distribution Is Normal
		Students t-Test When the Population Standard Deviation (σ) Is Not Known
		Solving Students t-Test for a Single Sample by Using SPSS Software
		Solving Students t-Test for a Single Sample by Using Stata Software
	Students t-Test to Compare Two Population Means From Two Independent Random Samples
		Case 1: σ12σ22
		Case 2: σ12=σ22
		Solving Students t-Test From Two Independent Samples by Using SPSS Software
		Solving Students t-Test From Two Independent Samples by Using Stata Software
	Students t-Test to Compare Two Population Means From Two Paired Random Samples
		Solving Students t-Test From Two Paired Samples by Using SPSS Software
		Solving Students t-Test From Two Paired Samples by Using Stata Software
	ANOVA to Compare the Means of More Than Two Populations
		One-Way ANOVA
			Solving the One-Way ANOVA Test by Using SPSS Software
			Solving the One-Way ANOVA Test by Using Stata Software
		Factorial ANOVA
			Two-Way ANOVA
				Solving the Two-Way ANOVA Test by Using SPSS Software
				Solving the Two-Way ANOVA Test by Using Stata Software
			ANOVA With More Than Two Factors
	Final Remarks
	Exercises
10
Nonparametric Tests
	Introduction
	Tests for One Sample
		Binomial Test
			Solving the Binomial Test Using SPSS Software
			Solving the Binomial Test Using Stata Software
		Chi-Square Test (χ2) for One Sample
			Solving the χ2 Test for One Sample Using SPSS Software
			Solving the χ2 Test for One Sample Using Stata Software
		Sign Test for One Sample
			Solving the Sign Test for One Sample Using SPSS Software
			Solving the Sign Test for One Sample Using Stata Software
	Tests for Two Paired Samples
		McNemar Test
			Solving the McNemar Test Using SPSS Software
			Solving the McNemar Test Using Stata Software
		Sign Test for Two Paired Samples
			Solving the Sign Test for Two Paired Samples Using SPSS Software
			Solving the Sign Test for Two Paired Samples Using Stata Software
		Wilcoxon Test
			Solving the Wilcoxon Test Using SPSS Software
			Solving the Wilcoxon Test Using Stata Software
	Tests for Two Independent Samples
		Chi-Square Test (χ2) for Two Independent Samples
			Solving the χ2 Statistic Using SPSS Software
			Solving the χ2 Statistic by Using Stata Software
		Mann-Whitney U Test
			Solving the Mann-Whitney Test Using SPSS Software
			Solving the Mann-Whitney Test Using Stata Software
	Tests for k Paired Samples
		Cochrans Q Test
			Solving Cochrans Q Test by Using SPSS Software
			Solution of Cochrans Q Test on Stata Software
		Friedmans Test
			Solving Friedmans Test by Using SPSS Software
			Solving Friedmans Test by Using Stata Software
	Tests for k Independent Samples
		The χ2 Test for k Independent Samples
			Solving the χ2 Test for k Independent Samples on SPSS
			Solving the χ2 Test for k Independent Samples on Stata
		Kruskal-Wallis Test
			Solving the Kruskal-Wallis Test by Using SPSS Software
			Solving the Kruskal-Wallis Test by Using Stata
	Final Remarks
	Exercises
Part V: Multivariate Exploratory Data Analysis
11
Cluster Analysis
	Introduction
	Cluster Analysis
		Defining Distance or Similarity Measures in Cluster Analysis
			Distance (Dissimilarity) Measures Between Observations for Metric Variables
			Similarity Measures Between Observations for Binary Variables
		Agglomeration Schedules in Cluster Analysis
			Hierarchical Agglomeration Schedules
				Notation
				A Practical Example of Cluster Analysis With Hierarchical Agglomeration Schedules
					Nearest-Neighbor or Single-Linkage Method
					Furthest-Neighbor or Complete-Linkage Method
					Between-Groups or Average-Linkage Method
			Nonhierarchical K-Means Agglomeration Schedule
				Notation
				A Practical Example of a Cluster Analysis With the Nonhierarchical K-Means Agglomeration Schedule
	Cluster Analysis with Hierarchical and Nonhierarchical Agglomeration Schedules in SPSS
		Elaborating Hierarchical Agglomeration Schedules in SPSS
		Elaborating Nonhierarchical K-Means Agglomeration Schedules in SPSS
	Cluster Analysis With Hierarchical and Nonhierarchical Agglomeration Schedules in Stata
		Elaborating Hierarchical Agglomeration Schedules in Stata
		Elaborating Nonhierarchical K-Means Agglomeration Schedules in Stata
	Final Remarks
	Exercises
	Appendix
		Detecting Multivariate Outliers
12
Principal Component Factor Analysis
	Introduction
	Principal Component Factor Analysis
		Pearsons Linear Correlation and the Concept of Factor
		Overall Adequacy of the Factor Analysis: Kaiser-Meyer-Olkin Statistic and Bartletts Test of Sphericity
		Defining the Principal Component Factors: Determining the Eigenvalues and Eigenvectors of Correlation Matrix ρ and Calcula ...
		Factor Loadings and Communalities
		Factor Rotation
		A Practical Example of the Principal Component Factor Analysis
	Principal Component Factor Analysis in SPSS
	Principal Component Factor Analysis in Stata
	Final Remarks
	Exercises
	Appendix: Cronbachs Alpha
		Brief Presentation
		Determining Cronbachs Alpha Algebraically
		Determining Cronbachs Alpha in SPSS
		Determining Cronbachs Alpha in Stata
Part VI: Generalized Linear Models
13
Simple and Multiple Regression Models
	Introduction
	Linear Regression Models
		Estimation of the Linear Regression Model by Ordinary Least Squares
		Explanatory Power of the Regression Model: Coefficient of Determination R2
		General Statistical Significance of the Regression Model and Each of Its Parameters
		Construction of the Confidence Intervals of the Model Parameters and Elaboration of Predictions
		Estimation of Multiple Linear Regression Models
		Dummy Variables in Regression Models
	Presuppositions of Regression Models Estimated by OLS
		Normality of Residuals
		The Multicollinearity Problem
			Causes of Multicollinearity
			Consequences of Multicollinearity
			Application of Multicollinearity Examples in Excel
			Multicollinearity Diagnostics
			Possible Solutions for the Multicollinearity Problem
		The Problem of Heteroskedasticity
			Causes of Heteroskedasticity
			Consequences of Heteroskedasticity
			Heteroskedasticity Diagnostics: Breusch-Pagan/Cook-Weisberg Test
			Weighted Least Squares Method: A Possible Solution
			Huber-White Method for Robust Standard Errors
		The Autocorrelation of Residuals Problem
			Causes of the Autocorrelation of Residuals
			Consequences of the Autocorrelation of Residuals
			Autocorrelation of Residuals Diagnostic: The Durbin-Watson Test
			Autocorrelation of Residuals Diagnostic: The Breusch-Godfrey Test
			Possible Solutions for the Autocorrelation of Residuals Problem
		Detection of Specification Problems: Linktest and RESET Test
	Nonlinear Regression Models
		The Box-Cox Transformation: The General Regression Model
	Estimation of Regression Models in Stata
	Estimation of Regression Models in SPSS
	Final Remarks
	Exercises
	Appendix: Quantile Regression Models
		A Brief Introduction
		Example: Quantile Regression Model in Stata
14
Binary and Multinomial Logistic Regression Models
	Introduction
	The Binary Logistic Regression Model
		Estimation of the Binary Logistic Regression Model by Maximum Likelihood
		General Statistical Significance of the Binary Logistic Regression Model and Each of Its Parameters
		Construction of the Confidence Intervals of the Parameters for the Binary Logistic Regression Model
		Cutoff, Sensitivity Analysis, Overall Model Efficiency, Sensitivity, and Specificity
	The Multinomial Logistic Regression Model
		Estimation of the Multinomial Logistic Regression Model by Maximum Likelihood
		General Statistical Significance of the Multinomial Logistic Regression Model and Each of Its Parameters
		Construction of the Confidence Intervals of the Parameters for the Multinomial Logistic Regression Model
	Estimation of Binary and Multinomial Logistic Regression Models in Stata
		Binary Logistic Regression in Stata
		Multinomial Logistic Regression in Stata
	Estimation of Binary and Multinomial Logistic Regression Models in SPSS
		Binary Logistic Regression in SPSS
		Multinomial Logistic Regression in SPSS
	Final Remarks
	Exercises
	Appendix: Probit Regression Models
		A Brief Introduction
		Example: Probit Regression Model in Stata
15
Regression Models for Count Data: Poisson and Negative Binomial
	Introduction
	The Poisson Regression Model
		Estimation of the Poisson Regression Model by Maximum Likelihood
		General Statistical Significance of the Poisson Regression Model and Each of Its Parameters
		Construction of the Confidence Intervals of the Parameters for the Poisson Regression Model
		Test to Verify Overdispersion in Poisson Regression Models
	The Negative Binomial Regression Model
		Estimation of the Negative Binomial Regression Model by Maximum Likelihood
		General Statistical Significance of the Negative Binomial Regression Model and Each of Its Parameters
		Construction of the Confidence Intervals of the Parameters for the Negative Binomial Regression Model
	Estimating Regression Models for Count Data in Stata
		Poisson Regression Model in Stata
		Negative Binomial Regression Model in Stata
	Regression Model Estimation for Count Data in SPSS
		Poisson Regression Model in SPSS
		Negative Binomial Regression Model in SPSS
	Final Remarks
	Exercises
	Appendix: Zero-Inflated Regression Models
		Brief Introduction
		Example: Zero-Inflated Poisson Regression Model in Stata
		Example: Zero-Inflated Negative Binomial Regression Model in Stata
Part VII: Optimization Models and Simulation
16
Introduction to Optimization Models: General Formulations and Business Modeling
	Introduction to Optimization Models
	Introduction to Linear Programming Models
	Mathematical Formulation of a General Linear Programming Model
	Linear Programming Model in the Standard and Canonical Forms
		Linear Programming Model in the Standard Form
		Linear Programming Model in the Canonical Form
		Transformations Into the Standard or Canonical Form
	Assumptions of the Linear Programming Model
		Proportionality
		Additivity
		Divisibility and Non-negativity
		Certainty
	Modeling Business Problems Using Linear Programming
		Production Mix Problem
		Blending or Mixing Problem
		Diet Problem
		Capital Budget Problems
		Portfolio Selection Problem
			Model 1: Maximization of an Investment Portfolios Expected Return
			Model 2: Investment Portfolio Risk Minimization
		Production and Inventory Problem
		Aggregated Planning Problem
	Final Remarks
	Exercises
17
Solution of Linear Programming Problems
	Introduction
	Graphical Solution of a Linear Programming Problem
		Linear Programming Maximization Problem with a Single Optimal Solution
		Linear Programming Minimization Problem With a Single Optimal Solution
		Special Cases
			Multiple Optimal Solutions
			Unlimited Objective Function z
			There Is No Optimal Solution
			Degenerate Optimal Solution
	Analytical Solution of a Linear Programming Problem in Which m  n
	The Simplex Method
		Logic of the Simplex Method
		Analytical Solution of the Simplex method for Maximization Problems
		Tabular Form of the Simplex Method for Maximization Problems
		The Simplex Method for Minimization Problems
		Special Cases of the Simplex Method
			Multiple Optimal Solutions
			Unlimited Objective Function z
			There Is No Optimal Solution
			Degenerate Optimal Solution
	Solution by Using a Computer
		Solver in Excel
		Solution of the Examples found in Section 16.6 of Chapter 16 using Solver in Excel
			Solution of Example 16.3 of Chapter 16 (Production Mix Problem at the Venix Toys)
			Solution of Example 16.4 of Chapter 16 (Production Mix Problem at Naturelat Dairy)
			Solution of Example 16.5 of Chapter 16 (Mix Problem of Oil-South Refinery)
			Solution of Example 16.6 of Chapter 16 (Diet Problem)
			Solution of Example 16.7 of Chapter 16 (Farmers Problem)
			Solution of Example 16.8 of Chapter 16 (Portfolio Selection-Maximization of the Expected Return)
			Solution of Example 16.9 of Chapter 16 (Portfolio Selection-Minimization of the Portfolios Mean Absolute Deviation)
			Solution of Example 16.10 of Chapter 16 (Production and Inventory Problem of FenixandFurniture)
			Solution of Example 16.11 of Chapter 16 (Problem of Lifestyle Natural Juices Manufacturer)
		Solver Error Messages for Unlimited and Infeasible Solutions
			Unlimited Objective Function z
			There Is No Optimal Solution
		Result Analysis by Using the Solver Answer and Limits Reports
			Answer Report
			Limits Report
	Sensitivity Analysis
		Alteration in one of the Objective Function Coefficients (Graphical Solution)
		Alteration in One of the Constants on the Right-Hand Side of the Constraint and Concept of Shadow Price (Graphica ...
		Reduced Cost
		Sensitivity Analysis With Solver in Excel
			Special Case: Multiple Optimal Solutions
			Special Case: Degenerate Optimal Solution
	Exercises
18
Network Programming
	Introduction
	Terminology of Graphs and Networks
	Classic Transportation Problem
		Mathematical Formulation of the Classic Transportation Problem
		Balancing the Transportation Problem When the Total Supply Capacity Is Not Equal to the Total Demand Consumed
			Case 1: Total Supply Is Greater than Total Demand
			Case 2: Total Supply Capacity Is Lower than Total Demand Consumed
		Solution of the Classic Transportation Problem
			The Transportation Algorithm
			Solution of the Transportation Problem Using Excel Solver
	Transhipment Problem
		Mathematical Formulation of the Transhipment Problem
		Solution of the Transhipment Problem Using Excel Solver
	Job Assignment Problem
		Mathematical Formulation of the Job Assignment Problem
		Solution of the Job Assignment Problem Using Excel Solver
	Shortest Path Problem
		Mathematical Formulation of the Shortest Path Problem
		Solution of the Shortest Path Problem Using Excel Solver
	Maximum Flow Problem
		Mathematical Formulation of the Maximum Flow Problem
		Solution of the Maximum Flow Problem Using Excel Solver
	Exercises
19
Integer Programming
	Introduction
	Mathematical Formulation of a General Model for Integer Programming and/or Binary and Linear Relaxation
	The Knapsack Problem
		Modeling of the Knapsack Problem
		Solution of the Knapsack Problem Using Excel Solver
	The Capital Budgeting Problem as a Model of Binary Programming
		Solution of the Capital Budgeting Problem as a Model of Binary Programming Using Excel Solver
	The Traveling Salesman Problem
		Modeling of the Traveling Salesman Problem
		Solution of the Traveling Salesman Problem Using Excel Solver
	The Facility Location Problem
		Modeling of the Facility Location Problem
		Solution of the Facility Location Problem Using Excel Solver
	The Staff Scheduling Problem
		Solution of the Staff Scheduling Problem Using Excel Solver
	Exercises
20
Simulation and Risk Analysis
	Introduction to Simulation
	The Monte Carlo Method
	Monte Carlo Simulation in Excel
		Generation of Random Numbers and Probability Distributions in Excel
		Practical Examples
			Case 1: Consumption of Red Wine
			Case 2: Profit x Loss Forecast
	Final Remarks
	Exercises
Part VIII: Other Topics
21
Design and Analysis of Experiments
	Introduction
	Steps in the Design of Experiments
	The Four Principles of Experimental Design
	Types of Experimental Design
		Completely Randomized Design (CRD)
		Randomized Block Design (RBD)
		Factorial Design (FD)
	One-Way Analysis of Variance
	Factorial ANOVA
	Final Remarks
	Exercises
22
Statistical Process Control
	Introduction
	Estimating the Process Mean and Variability
	Control Charts for Variables
		Control Charts for X and R
			Control Charts for X
			Control Charts for R
		Control Charts for X and S
	Control Charts for Attributes
		P Chart (Defective Fraction)
		np Chart (Number of Defective Products)
		C Chart (Total Number of Defects per Unit)
		U Chart (Average Number of Defects per Unit)
	Process Capability
		Cp Index
		Cpk Index
		Cpm and Cpmk Indexes
	Final Remarks
	Exercises
23
Data Mining and Multilevel Modeling
	Introduction to Data Mining
	Multilevel Modeling
	Nested Data Structures
	Hierarchical Linear Models
		Two-Level Hierarchical Linear Models With Clustered Data (HLM2)
		Three-Level Hierarchical Linear Models With Repeated Measures (HLM3)
	Estimation of Hierarchical Linear Models in Stata
		Estimation of a Two-Level Hierarchical Linear Model With Clustered Data in Stata
		Estimation of a Three-Level Hierarchical Linear Model With Repeated Measures in Stata
	Estimation of Hierarchical Linear Models in SPSS
		Estimation of a Two-Level Hierarchical Linear Model With Clustered Data in SPSS
		Estimation of a Three-Level Hierarchical Linear Model With Repeated Measures in SPSS
	Final Remarks
	Exercises
	Appendix
		Hierarchical Nonlinear Models
Answers
	Answer Keys: Exercises: Chapter 2
	Answer Keys: Exercises: Chapter 3
	Answer Keys: Exercises: Chapter 4
	Answer Keys: Exercises: Chapter 5
	Answer Keys: Exercises: Chapter 6
	Answer Keys: Exercises: Chapter 7
	Answer Keys: Exercises: Chapter 8
	Answer Keys: Exercises: Chapter 9
	Answer Keys: Exercises: Chapter 10
	Answer Keys: Exercises: Chapter 11
	Answer Keys: Exercises: Chapter 12
	Answer Keys: Exercises: Chapter 13
	Answer Keys: Exercises: Chapter 14
	Answer Keys: Exercises: Chapter 15
	Answer Keys: Exercises: Chapter 16
	Answer Keys: Exercises: Chapter 17
	Answer Keys: Exercises: Chapter 18
	Answer Keys: Exercises: Chapter 19
	Answer Keys: Exercises: Chapter 20
	Answer Keys: Exercises: Chapter 21
	Answer Keys: Exercises: Chapter 22
	Answer Keys: Exercises: Chapter 23
Appendices
References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	Y
	Z