Statistics
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Statistics
Supported courses:
STAT 1312 - Statistical Literacy
Topics
- Describe the utility and limitations of data and statistical methods for solving real-world problems.
- Describe methods of data collection and explain potential pitfalls, biases, and ethical issues.
- Demonstrate facility with the terminology, notation, and numerical methods generally found in an introductory statistics course, such as graphical summaries, measures of central tendency and dispersion and use these to compare data sets.
- Demonstrate a basic understanding of probability and how it relates to statistics, particularly the ideas of randomness and statistical significance.
- Demonstrate facility with basic inferential statistical methods, including confidence intervals and hypothesis tests, and understand when a particular method is appropriate, and the ability to interpret results in the context of a stated problem.
- Demonstrate facility to use appropriate technology such as statistical software and/or calculators.
- Formulate and communicate solutions to statistical problems in clear, grammatically correct, precise English.
STAT 2300 - Introduction To Statistics
Topics
- Techniques For The Collection, Analysis And Interpretation Of Numerical Data
- Probability
- Confidence Intervals
- Hypothesis Testing
- Estimation
- Special Distributions
STAT 2305 - Business Statistics with Applications
Description
Basic concepts of statistics for natural science students are considered including collection of data, descriptive statistics, probability concepts, random variables, probability distributions, expected values, point and interval estimation techniques, hypothesis testing, bivariate data, linear regression, and analysis of variance. Natural science applications are stressed and computer statistical packages are used.
STAT 3311 - Statistical Analysis for Natural Science
Description
Basic concepts of statistics for natural science students are considered including collection of data, descriptive statistics, probability concepts, random variables, probability distributions, expected values, point and interval estimation techniques, hypothesis testing, bivariate data, linear regression, and analysis of variance. Natural science applications are stressed and computer statistical packages are used.