This course will provide an introduction to the principles of data collection, description and analysis. You will learn the basic tools of statistical inference and modeling, as well as some fundamentals of designing a statistical study, how to sample and collect data, and which statistical techniques are appropriate. You will also learn how to interpret statistical output, and how not to be fooled by statistical studies.
At the end of this course, you should be able to:
- Present data visually in tabular and graphic form
- Summarize a set of observations by reporting a measure of center and dispersion
- Explain how and why sample data can be used to estimate descriptive measures of populations when census data is unavailable, and how we measure the accuracy and precision of the estimate
- Apply the basic rules of probability
- Find and interpret the probability for a random variable which has a normal distribution
- Explain how to take a proper scientific sample that can be used to make inferences about the larger population
- Explain what sampling error is and why it exists
- Classify data by type (quantitative or quantitative, discrete or continuous) and use the proper summary statistics and tests for the data type
- Interpret the p-value, test statistic and other Minitab output from a test of hypotheses, confidence interval, and linear regression
- Explain what it means if test results or poll differences are statistically significant
- Apply the concepts of sampling, estimation, and hypothesis testing to real world examples from polls and surveys, clinical trials and observational studies.
Faculty: Kathleen A. Smith
Course format: Online
Check the original course description for the most updated information.