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 you will learn the fundamental concepts of statistics (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 use of intuition, graphical plots, real-world examples, and numerous in-class student exercises using paper/pencil and a calculator. 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. In fact, you will learn most of the important topics covered in a semester-long university course in just four days. This seminar has been presented to the U.S. Navy several times.
Dr. Averill M. Law, the course instructor, has taught statistical concepts and techniques for more than 35 years, both in 17 years of university teaching and in presenting more than 490 short courses in 18 countries. He is the developer of ExpertFit®, which has been the world's leading distribution-fitting software since 1983. Dr. Law is the author or coauthor of three books and numerous journal articles. He has been a tenured faculty member at the University of Wisconsin-Madison and the University of Arizona. He has a Ph.D. from the University of California at Berkeley.
What You Will Learn:
1. Overview
Populations and samples
Descriptive statistics (graphical plots and numerical summaries)
Inferential statistics (confidence intervals and hypothesis tests)
Determining the relationship between two or more variables (regression analysis)
2. Random Variables
Definition and distribution function
Discrete random variables
Probability mass function
Bernoulli, binomial, geometric, and Poisson distributions and their applications
Continuous random variables
Probability density function
Normal, exponential, gamma, Weibull, and lognormal distributions and their applications
Characteristics of a random variable (mean, median, variance, standard deviation)
3. Joint Probability Distributions
Jointly distributed random variables
Marginal distributions
Independent random variables
Covariance and correlation
Statistics and their distributions
Distribution of the sample mean and the central limit theorem
4. Point Estimation
Unbiased estimator
Variance of a point estimator
Estimators for the mean and variance
5. Descriptive Statistics
Graphical plots (histogram, box plot, scatter plot)
Numerical summaries (sample mean, sample variance, skewness)
6. Confidence Intervals Based on a Single Sample
Correct interpretation
For large sample sizes
For normally distributed data
Intervals for means and proportions
7. 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
8. Inferences Based on Two Samples
Hypothesis tests and confidence intervals
Comparing two means
Comparing two proportions
9. Regression Analysis
Linear regression models with one or more independent variables
Estimating model parameters
Determining the adequacy of the model
10. Fitting Distributions to Data
Estimating a distribution's parameters
Determining the quality of fit
Graphical comparisons
Goodness-of-fit tests (chi square)
11. Commercial Statistical Packages and Their Benefits
