Instruction Mode: Online
Professor: N/A
Resource(s): | Type | Description |
---|
Required | Textbook | Introduction to Probability and Statistics, William Mendenhall; Robert J. Beaver; Barbara M. Beaver, Cengage Learning, 15th Edition, ISBN DIGITAL: 9780357044308, 2020, print ISBNs are 9781337554428, 1337554421 |
Required | Software | RStudio Open-Source |
Applicable student group(s): Students in the online class in the Continuing and Professional Studies.
Course Details: 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