[OER] Business Data Mining

Course Modules

Getting Started

Getting Started Getting Started

Introduction To R Programming

Introduction To R Programming Introduction To R Programming

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Business and Coding Business and Coding 1

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The Working Environment The Working Environment 2

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Creating Your First R Script Creating Your First R Script 3

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Using R: The Basics Using R: The Basics 4

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Assigning and Using Variables in R Assigning and Using Variables in R 5

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Manipulating Data Structures in R Manipulating Data Structures in R 6

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Importing and Exporting Data in R Importing and Exporting Data in R 7

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Exploratory Data Analysis

Exploratory Data Analysis Exploratory Data Analysis

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Introduction to Exploratory Data Analysis Introduction to Exploratory Data Analysis 1

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Exploratory Data Analysis and Plotting Basics Exploratory Data Analysis and Plotting Basics 2

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Advanced Exploratory Data Analysis Advanced Exploratory Data Analysis 3

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Linear Regression Models

Linear Regression Models Linear Regression Models

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Linear Regression in R Linear Regression in R 1

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The Assumptions of Linear Regression The Assumptions of Linear Regression 2

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Choosing A Regression Model in R Choosing A Regression Model in R 3

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Indicator, Ordinal, & Categorical Data Indicator, Ordinal, & Categorical Data 4

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Clustering

Clustering Clustering

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Clustering in R: Linear Discriminant Analysis Clustering in R: Linear Discriminant Analysis 1

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Clustering in R: K Means Clustering in R: K Means 2

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Time Series Analysis

Time Series Analysis Time Series Analysis

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Introduction to Time Series Analysis in R Introduction to Time Series Analysis in R 1

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Trend & Seasonality Trend & Seasonality 2

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Time Series Analysis in R: Seasonal Regression Time Series Analysis in R: Seasonal Regression 3

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Financial Forecasting Stocks Financial Forecasting Stocks 4

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Mathematical Materials

Mathematical Materials Mathematical Materials

Context Module Sub Header

Chapter 1: Sampling and Data 3

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External Url

Introduction Introduction 4

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1.1 Definitions of Statistics, Probability, and Key Terms 1.1 Definitions of Statistics, Probability, and Key Terms 5

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1.2 Data, Sampling, and Variation in Data and Sampling 1.2 Data, Sampling, and Variation in Data and Sampling 6

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1.3 Levels of Measurement 1.3 Levels of Measurement 7

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1.4 Experimental Design and Ethics 1.4 Experimental Design and Ethics 8

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Context Module Sub Header

Chapter 2: Descriptive Statistics 9

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External Url

Introduction Introduction 10

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External Url

2.1 Display Data 2.1 Display Data 11

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2.2 Measures of the Location of the Data 2.2 Measures of the Location of the Data 12

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2.3 Measures of the Center of the Data 2.3 Measures of the Center of the Data 13

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2.4 Sigma Notation and Calculating the Arithmetic Mean 2.4 Sigma Notation and Calculating the Arithmetic Mean 14

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2.5 Geometric Mean 2.5 Geometric Mean 15

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2.6 Skewness and the Mean, Median, and Mode 2.6 Skewness and the Mean, Median, and Mode 16

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External Url

2.7 Measures of the Spread of the Data 2.7 Measures of the Spread of the Data 17

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Context Module Sub Header

Chapter 3: Probability Topics 18

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External Url

Introduction Introduction 19

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External Url

3.1 Terminology 3.1 Terminology 20

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3.2 Independent and Mutually Exclusive Events 3.2 Independent and Mutually Exclusive Events 21

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3.3 Two Basic Rules of Probability 3.3 Two Basic Rules of Probability 22

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3.4 Contingency Tables and Probability Trees 3.4 Contingency Tables and Probability Trees 23

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3.5 Venn Diagrams 3.5 Venn Diagrams 24

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Context Module Sub Header

Chapter 4: Discrete Random Variables 25

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External Url

Introduction Introduction 26

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4.1 Hypergeometric Distribution 4.1 Hypergeometric Distribution 27

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4.2 Binomial Distribution 4.2 Binomial Distribution 28

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4.3 Geometric Distribution 4.3 Geometric Distribution 29

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4.4 Poisson Distribution 4.4 Poisson Distribution 30

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Chapter 5: Continuous Random Variables 31

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Introduction Introduction 32

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5.1 Properties of Continuous Probability Density Functions 5.1 Properties of Continuous Probability Density Functions 33

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5.2 The Uniform Distribution 5.2 The Uniform Distribution 34

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5.3 The Exponential Distribution 5.3 The Exponential Distribution 35

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Chapter 6: The Normal Distribution 36

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Introduction Introduction 37

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6.1 The Standard Normal Distribution 6.1 The Standard Normal Distribution 38

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6.2 Using the Normal Distribution 6.2 Using the Normal Distribution 39

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6.3 Estimating the Binomial with the Normal Distribution 6.3 Estimating the Binomial with the Normal Distribution 40

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Chapter 7: The Central Limit Theorem 41

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Introduction Introduction 42

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7.1 The Central Limit Theorem for Sample Means 7.1 The Central Limit Theorem for Sample Means 43

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7.2 Using the Central Limit Theorem 7.2 Using the Central Limit Theorem 44

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7.3 The Central Limit Theorem for Proportions 7.3 The Central Limit Theorem for Proportions 45

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7.4 Finite Population Correction Factor 7.4 Finite Population Correction Factor 46

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Chapter 8: Confidence Intervals 47

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Introduction Introduction 48

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8.1 A Confidence Interval for a Population Standard Deviation, Known or Large Sample Size 8.1 A Confidence Interval for a Population Standard Deviation, Known or Large Sample Size 49

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8.2 A Confidence Interval for a Population Standard Deviation Unknown, Small Sample Case 8.2 A Confidence Interval for a Population Standard Deviation Unknown, Small Sample Case 50

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8.3 A Confidence Interval for A Population Proportion 8.3 A Confidence Interval for A Population Proportion 51

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8.4 Calculating the Sample Size n: Continuous and Binary Random Variables 8.4 Calculating the Sample Size n: Continuous and Binary Random Variables 52

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Chapter 9: Hypothesis Testing with One Sample 53

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Introduction Introduction 54

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9.1 Null and Alternative Hypotheses 9.1 Null and Alternative Hypotheses 55

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9.2 Outcomes and the Type I and Type II Errors 9.2 Outcomes and the Type I and Type II Errors 56

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9.3 Distribution Needed for Hypothesis Testing 9.3 Distribution Needed for Hypothesis Testing 57

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9.4 Full Hypothesis Test Examples 9.4 Full Hypothesis Test Examples 58

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Chapter 10: Hypothesis Testing with Two Samples 59

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Introduction Introduction 60

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10.1 Comparing Two Independent Population Means 10.1 Comparing Two Independent Population Means 61

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10.2 Cohen's Standards for Small, Medium, and Large Effect Sizes 10.2 Cohen's Standards for Small, Medium, and Large Effect Sizes 62

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10.3 Test for Differences in Means: Assuming Equal Population Variances 10.3 Test for Differences in Means: Assuming Equal Population Variances 63

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10.4 Comparing Two Independent Population Proportions 10.4 Comparing Two Independent Population Proportions 64

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10.5 Two Population Means with Known Standard Deviations 10.5 Two Population Means with Known Standard Deviations 65

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10.6 Matched or Paired Samples 10.6 Matched or Paired Samples 66

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Chapter 11: The Chi-Squared Distribution 67

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Introduction Introduction 68

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11.1 Facts About the Chi-Square Distribution 11.1 Facts About the Chi-Square Distribution 69

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11.2 Test of a Single Variance 11.2 Test of a Single Variance 70

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11.3 Goodness-of-Fit Test 11.3 Goodness-of-Fit Test 71

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11.4 Test of Independence 11.4 Test of Independence 72

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11.5 Test for Homogeneity 11.5 Test for Homogeneity 73

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11.6 Comparison of the Chi-Square Tests 11.6 Comparison of the Chi-Square Tests 74

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Chapter 12: F Distribution and One-Way ANOVA 75

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Introduction Introduction 76

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12.1 Test of Two Variances 12.1 Test of Two Variances 77

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12.2 One-Way ANOVA 12.2 One-Way ANOVA 78

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12.3 The F Distribution and the F-Ratio 12.3 The F Distribution and the F-Ratio 79

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12.4 Facts About the F Distribution 12.4 Facts About the F Distribution 80

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Chapter 13: Linear Regression and Correlation 81

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Introduction Introduction 82

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13.1 The Correlation Coefficient r 13.1 The Correlation Coefficient r 83

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13.2 Testing the Significance of the Correlation Coefficient 13.2 Testing the Significance of the Correlation Coefficient 84

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13.3 Linear Equations 13.3 Linear Equations 85

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13.4 The Regression Equation 13.4 The Regression Equation 86

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13.5 Interpretation of Regression Coefficients: Elasticity and Logarithmic Transformation 13.5 Interpretation of Regression Coefficients: Elasticity and Logarithmic Transformation 87

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13.6 Predicting with a Regression Equation 13.6 Predicting with a Regression Equation 88

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13.7 How to Use Microsoft Excel® for Regression Analysis 13.7 How to Use Microsoft Excel® for Regression Analysis 89

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Statistical Tables 90

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Statistical Tables Statistical Tables 91

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Mathematical Phrases, Symbols, and Formulas Index 92

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Mathematical Phrases, Symbols, and Formulas Mathematical Phrases, Symbols, and Formulas 93

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This course content is offered under a CC Attribution Non-Commercial Share Alike license. Content in this course can be considered under this license unless otherwise noted.

[OER] Business Data Mining

Course Modules

Getting Started

Introduction To R Programming

Exploratory Data Analysis

Linear Regression Models

Clustering

Time Series Analysis

Mathematical Materials

Chapter 1: Sampling and Data 3

Chapter 2: Descriptive Statistics 9

Chapter 3: Probability Topics 18

Chapter 4: Discrete Random Variables 25

Chapter 5: Continuous Random Variables 31

Chapter 6: The Normal Distribution 36

Chapter 7: The Central Limit Theorem 41

Chapter 8: Confidence Intervals 47

Chapter 9: Hypothesis Testing with One Sample 53

Chapter 10: Hypothesis Testing with Two Samples 59

Chapter 11: The Chi-Squared Distribution 67

Chapter 12: F Distribution and One-Way ANOVA 75

Chapter 13: Linear Regression and Correlation 81

Statistical Tables 90

Mathematical Phrases, Symbols, and Formulas Index 92