Course Modules

Getting Started

Getting Started
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Getting Started 61787    

Introduction To R Programming

Introduction To R Programming
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Introduction To R Programming 60780    
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    Business and Coding Business and Coding
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    The Working Environment The Working Environment
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    Creating Your First R Script Creating Your First R Script
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    Using R: The Basics Using R: The Basics
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    Assigning and Using Variables in R Assigning and Using Variables in R
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    Manipulating Data Structures in R Manipulating Data Structures in R
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    Importing and Exporting Data in R Importing and Exporting Data in R
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Exploratory Data Analysis

Exploratory Data Analysis
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Exploratory Data Analysis 60906    
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    Introduction to Exploratory Data Analysis Introduction to Exploratory Data Analysis
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    Exploratory Data Analysis and Plotting Basics Exploratory Data Analysis and Plotting Basics
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    Advanced Exploratory Data Analysis Advanced Exploratory Data Analysis
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Linear Regression Models

Linear Regression Models
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    Linear Regression in R Linear Regression in R
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    The Assumptions of Linear Regression The Assumptions of Linear Regression
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    Choosing A Regression Model in R Choosing A Regression Model in R
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    Indicator, Ordinal, & Categorical Data Indicator, Ordinal, & Categorical Data
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Clustering

Clustering
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Clustering 60852    
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    Clustering in R: Linear Discriminant Analysis Clustering in R: Linear Discriminant Analysis
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    Clustering in R: K Means Clustering in R: K Means
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Time Series Analysis

Time Series Analysis
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Time Series Analysis 60905    
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    Introduction to Time Series Analysis in R Introduction to Time Series Analysis in R
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    Trend & Seasonality Trend & Seasonality
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    Time Series Analysis in R: Seasonal Regression Time Series Analysis in R: Seasonal Regression
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    Financial Forecasting Stocks Financial Forecasting Stocks
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Mathematical Materials

Mathematical Materials
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Mathematical Materials 61716    
  • Context Module Sub Header
    Chapter 1: Sampling and Data
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    Introduction Introduction
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    1.1 Definitions of Statistics, Probability, and Key Terms 1.1 Definitions of Statistics, Probability, and Key Terms
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    1.2 Data, Sampling, and Variation in Data and Sampling 1.2 Data, Sampling, and Variation in Data and Sampling
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    1.3 Levels of Measurement 1.3 Levels of Measurement
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    1.4 Experimental Design and Ethics 1.4 Experimental Design and Ethics
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  • Context Module Sub Header
    Chapter 2: Descriptive Statistics
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    Introduction Introduction
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    2.1 Display Data 2.1 Display Data
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    2.2 Measures of the Location of the Data 2.2 Measures of the Location of the Data
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    2.3 Measures of the Center of the Data 2.3 Measures of the Center of the Data
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    2.4 Sigma Notation and Calculating the Arithmetic Mean 2.4 Sigma Notation and Calculating the Arithmetic Mean
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    2.5 Geometric Mean 2.5 Geometric Mean
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    2.6 Skewness and the Mean, Median, and Mode 2.6 Skewness and the Mean, Median, and Mode
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    2.7 Measures of the Spread of the Data 2.7 Measures of the Spread of the Data
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  • Context Module Sub Header
    Chapter 3: Probability Topics
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    Introduction Introduction
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    3.1 Terminology 3.1 Terminology
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    3.2 Independent and Mutually Exclusive Events 3.2 Independent and Mutually Exclusive Events
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    3.3 Two Basic Rules of Probability 3.3 Two Basic Rules of Probability
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    3.4 Contingency Tables and Probability Trees 3.4 Contingency Tables and Probability Trees
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    3.5 Venn Diagrams 3.5 Venn Diagrams
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  • Context Module Sub Header
    Chapter 4: Discrete Random Variables
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    Introduction Introduction
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    4.1 Hypergeometric Distribution 4.1 Hypergeometric Distribution
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    4.2 Binomial Distribution 4.2 Binomial Distribution
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    4.3 Geometric Distribution 4.3 Geometric Distribution
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    4.4 Poisson Distribution 4.4 Poisson Distribution
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  • Context Module Sub Header
    Chapter 5: Continuous Random Variables
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    Introduction Introduction
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    5.1 Properties of Continuous Probability Density Functions 5.1 Properties of Continuous Probability Density Functions
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    5.2 The Uniform Distribution 5.2 The Uniform Distribution
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    5.3 The Exponential Distribution 5.3 The Exponential Distribution
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  • Context Module Sub Header
    Chapter 6: The Normal Distribution
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    Introduction Introduction
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    6.1 The Standard Normal Distribution 6.1 The Standard Normal Distribution
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    6.2 Using the Normal Distribution 6.2 Using the Normal Distribution
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    6.3 Estimating the Binomial with the Normal Distribution 6.3 Estimating the Binomial with the Normal Distribution
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    Chapter 7: The Central Limit Theorem
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    Introduction Introduction
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    7.1 The Central Limit Theorem for Sample Means 7.1 The Central Limit Theorem for Sample Means
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    7.2 Using the Central Limit Theorem 7.2 Using the Central Limit Theorem
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    7.3 The Central Limit Theorem for Proportions 7.3 The Central Limit Theorem for Proportions
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    7.4 Finite Population Correction Factor 7.4 Finite Population Correction Factor
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    Chapter 8: Confidence Intervals
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    Introduction Introduction
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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
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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
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    8.3 A Confidence Interval for A Population Proportion 8.3 A Confidence Interval for A Population Proportion
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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
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    Chapter 9: Hypothesis Testing with One Sample
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    Introduction Introduction
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    9.1 Null and Alternative Hypotheses 9.1 Null and Alternative Hypotheses
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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
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    9.3 Distribution Needed for Hypothesis Testing 9.3 Distribution Needed for Hypothesis Testing
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    9.4 Full Hypothesis Test Examples 9.4 Full Hypothesis Test Examples
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    Chapter 10: Hypothesis Testing with Two Samples
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    Introduction Introduction
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    10.1 Comparing Two Independent Population Means 10.1 Comparing Two Independent Population Means
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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
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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
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    10.4 Comparing Two Independent Population Proportions 10.4 Comparing Two Independent Population Proportions
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    10.5 Two Population Means with Known Standard Deviations 10.5 Two Population Means with Known Standard Deviations
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    10.6 Matched or Paired Samples 10.6 Matched or Paired Samples
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  • Context Module Sub Header
    Chapter 11: The Chi-Squared Distribution
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    Introduction Introduction
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    11.1 Facts About the Chi-Square Distribution 11.1 Facts About the Chi-Square Distribution
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    11.2 Test of a Single Variance 11.2 Test of a Single Variance
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    11.3 Goodness-of-Fit Test 11.3 Goodness-of-Fit Test
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    11.4 Test of Independence 11.4 Test of Independence
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    11.5 Test for Homogeneity 11.5 Test for Homogeneity
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    11.6 Comparison of the Chi-Square Tests 11.6 Comparison of the Chi-Square Tests
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    Chapter 12: F Distribution and One-Way ANOVA
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    Introduction Introduction
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    12.1 Test of Two Variances 12.1 Test of Two Variances
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    12.2 One-Way ANOVA 12.2 One-Way ANOVA
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    12.3 The F Distribution and the F-Ratio 12.3 The F Distribution and the F-Ratio
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    12.4 Facts About the F Distribution 12.4 Facts About the F Distribution
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    Chapter 13: Linear Regression and Correlation
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    Introduction Introduction
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    13.1 The Correlation Coefficient r 13.1 The Correlation Coefficient r
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    13.2 Testing the Significance of the Correlation Coefficient 13.2 Testing the Significance of the Correlation Coefficient
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    13.3 Linear Equations 13.3 Linear Equations
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    13.4 The Regression Equation 13.4 The Regression Equation
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    13.5 Interpretation of Regression Coefficients: Elasticity and Logarithmic Transformation 13.5 Interpretation of Regression Coefficients: Elasticity and Logarithmic Transformation
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    13.6 Predicting with a Regression Equation 13.6 Predicting with a Regression Equation
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    13.7 How to Use Microsoft Excel® for Regression Analysis 13.7 How to Use Microsoft Excel® for Regression Analysis
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    Statistical Tables
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    Statistical Tables Statistical Tables
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    Mathematical Phrases, Symbols, and Formulas Index
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    Mathematical Phrases, Symbols, and Formulas Mathematical Phrases, Symbols, and Formulas
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