Gentle Introduction to Statistics
- Course Content
- Requirements & Materials
COURSE ID: DL 0054M
Contact for course-related questionsDavid Goldsman, Ph.D.
Gentle Introduction to Statistics
This course provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions, where we motivate their uses and go over their fundamental properties.
These distributions include such important folks as the Bernoulli, binomial, geometric, Poisson, uniform, exponential, and normal distributions, just to name a few. Particular attention is paid to the normal distribution, because it leads to the Central Limit Theorem (the most-important mathematical result in the universe, actually), which enables us to make probability calculations for arbitrary averages and sums of random variables.
We then discuss elementary descriptive statistics and estimation methods, including unbiased estimation, maximum likelihood estimation, and the method of moments – you got to love your MoM! Finally, we describe the t, χ2, and F sampling distributions, which will prove to be useful in upcoming statistical applications.
GETTING STARTED WITH STATISTICS
- Prior experience in Excel and/or the R freeware statistical package, or something similar
- Introductory knowledge in theory and basic calculus, as well as the material from the first two courses in this series (Gentle Introduction to Probability and Random Variables).
- Internet connection (DSL, LAN, or cable connection desirable)
- Adobe Acrobat PDF reader (to download for free, visit get.adobe.com/reader/)
- R statistical software (to download for free, visit cran.r-project.org) (or similar statistics packages such as Minitab, JMP, SAS, etc.)
- Excel (or equivalent)
- Bonus software: Any “real,” high-level language such as MATLAB, Python, etc.
- All content and course materials available online
Who Should Attend
This course is designed for anyone who needs the tools to understand data, science, philosophy, engineering, economics, and finance.
What You Will Learn
- A library of discrete and continuous probability distributions
- How to recognize elementary methods of descriptive statistics
- Normal distribution and the Central Limit Theorem, and how they are applied in practice
- How to identify statistical sampling distributions
- Methods that can be used to estimate the unknown parameters of a distribution
How You Will Benefit
- Learn about normal distribution and the Central Limit Theorem, and how they are applied in practice.
- Study methods that can be used to estimate the unknown parameters of a distribution.
Taught by Experts in the Field
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