Module 1: Descriptive Statistics Methods

• Identify types of variables and scales of measurement
• Describe and display categorical data
• Display and summarize quantitative data
• Calculate measures of location and dispersion

Module 2: Data Analysis Using Software

• Set-up the R environment
• Explain variables in R
• Use R codes for vectors, matrices, factors, lists and data frames
• Use R for manipulating datasets
• Use R for descriptive statistics
• Use R for creating charts

Evaluation: Assignment 1: 20%

Practice: Lab 1

Module 3: Probability Theory and Real-World Applications

• Explain the role of probability in statistics
• Explain events and the sample space
• Calculate probabilities using simple events
• Calculate probabilities for unions and complements, independence, conditional probability
• Apply multiplication rule
• Explain probability distributions
• Identify discrete random variables and their probability distributions

Module 4: Probability and Normal Distribution

• Describe probability distributions for continuous random variables
• Identify the properties of the normal curve
• Describe the normal probability distribution
• Calculate the tabulated areas of the normal probability distribution
• Define the standard normal random variable
• Evaluate probabilities for a general normal random variable
• Use R codes for normal distributions

Module 5: Sampling Distribution Techniques

• Explain the statistics of sampling distributions 
• Describe the central limit theorem 
• Describe the sampling distribution of the sample mean
• Describe interval estimation
• Calculate large-sample confidence interval for a population mean
• Interpret the confidence interval 
• Calculate one-sided confidence bound 
• Calculate sample size 
• Use R codes for calculating confidence intervals 

Module 6: Hypothesis Testing

• Formulate a hypothesis and apply testing of hypotheses on population parameters for large sample size
• Select appropriate statistical test of hypothesis (z-test)
• Evaluate a large-sample test about a population mean for one tail and two tail
• Explain critical value approach and p-value approach for hypothesis testing
• Assess two types of errors
• Evaluate the difference between two means  
• Apply testing of hypotheses on population parameters for small sample
• Select appropriate statistical test of hypothesis (t-test)
• Evaluate a sample test about a population mean for sample size less than 30
• Calculate p-value using T distribution
• Evaluate a small sample test of hypothesis for the difference between two population means
• Use R for hypothesis testing

Evaluation: Assignment 2: 20%

Practice: Lab 2

Module 7 Correlation and Regression

• Apply descriptive statistical methods to data
• Use statistical analysis software to explore and analyze data
• Use probability theory to evaluate the probability of real-world events
• Evaluate the probability of real-world events involving the normal distribution
• Apply sampling distribution tools and estimation techniques
• Apply a hypothesis test to data analysis problems
• Interpret correlation coefficient and regression line equations 

Evaluation: Assignment 3: 20%

Practice: Lab 3

Module 8 Statistical Experiments: A/B Testing

• Examine the importance of A/B testing in e-commerce and the principles of its implementation
• Understand the challenges of multivariate testing

Evaluation: Assignment 4: 20%

Module 9 Multiple Linear Regression Models

• Understand the subtle differences between using multiple regression models in statistics versus using them in machine learning 
• Articulate the assumptions of multiple linear regression
• Interpret the parameters of a multiple regression model
• Evaluate the performance of regression models

Evaluation: Assignment 5: 20%

Practice: Lab 4