Statistics is undoubtedly one of the most valuable of all disciplines, since virtually all organizations have data from which inferences must be drawn. In this course on statistics, you will learn the fundamental concepts (descriptive statistics, confidence intervals, hypothesis tests, regression, etc.) and be able to apply them immediately to the problems that you encounter on the job. This will be accomplished by the extensive use of intuition, graphical plots, and real-world examples. Whether you are new to statistics or are looking for a refresher course, you will find this seminar a great way to get up to speed quickly in a cost-effective manner.
Dr. Averill M. Law, the course instructor, has taught statistical concepts and techniques for more than 31 years, both in 17 years of university teaching and in presenting more than 450 short courses in 18 countries. He is the developer of the ExpertFit distribution-fitting software, which has been the world's leading such tool since 1983. He has a Ph.D. from the University of California at Berkeley.
What You Will Learn:
1. Overview
Populations and samples
Descriptive statistics (numerical summaries and graphical plots)
Inferential statistics (confidence intervals and hypothesis tests)
Determining the relationship between two or more variables (regression analysis)
2.
Basics of ProbabilitySample space, events, and axioms
Conditional probability
Independence
Random variables and their distribution functions
3.
Discrete Random Variables and Probability DistributionsProbability mass function
Mean and variance
Bernoulli, binomial, and Poisson distributions and their applications
4. Continuous Random Variables and Probability Distributions
Probability density function
Mean and variance
Normal, exponential, gamma, Weibull, and lognormal distributions and their applications
5. Joint Probability Distributions
Marginal distributions
Independent random variables
Covariance and correlation
Statistics and their distributions
6. Point Estimation
Unbiased estimator
Variance of a point estimator
Estimators for the mean and variance
7. Confidence Intervals Based on a Single Sample
Correct interpretation
For large sample sizes
For normally distributed data
Intervals for means and proportions
8. Hypothesis Tests Based on a Single Sample
Hypotheses and test procedures
Type I error, type II error, and power
P-values
Tests for means and proportions
9. Inferences Based on Two Samples
Hypothesis tests and confidence intervals
Comparing two means
Comparing two proportions
10. Regression Analysis
Linear regression models with one or more independent variables
Estimating model parameters
Correlation
11. Statistical Software Packages
