ISE 5200 - Engineering Statistics

• Adjust Type I and Type II error terms within hypothesis tests.
• Assess conditional probabilities for dependent and independent events.
• Block out error in randomized complete block ANOVA analysis.
• Compare two populations, for both mean and variation, using random samples.
• Conduct hypothesis tests on population parameters utilizing point estimates from random samples.
• Describe an unknown population using statistics from a random sample.
• Describe the parameters and the application of common probability dist. (Uniform, Binom, Normal).
• Determine sample sizes for hypothesis tests of population means.
• Determine the best sample measure of central tendency and variation for a sample from a known popula.
• Determine the permutations and combinations of a sample space and probabilities..
• Distinguish between discrete and continuous random variables.
• Estimate population proportions using binomial tables and normal approximations to the binomial.
• Plot X, Y data and identify linear and transformable patterns.
• Use ANOVA techniques to identify weak regression models.
• Use ANOVA techniques to test for simple randomized one-factor experiments.
• Use least squares regression methods for single and multiple X variables.
• Use probability functions and cumulative probability functions of discrete and continuous variables.
• Use sequential model selection techniques, like stepwise regression, to build prediction models.
• Use tables to calculate probability and its inverse (Normal, t, F, and chi-squared).
• Use techniques for minimizing experimental error in comparing multiple populations with ANOVA.
• Utilize sampling distribution tables to estimate confidence intervals of population parameters (mean).
• Utilize spreadsheet software for basic statistical analysis.