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Course level: Graduate
This course surveys descriptive and inferential statistics with emphasis on practical applications of statistical analysis. The principles of collecting, analyzing, and interpreting data are covered in this course. It examines the role of statistical analysis, terminology, the appropriate use of techniques, and interpretation of statistical findings through the applications and functions of statistical methods.
This graduate level course requires proof of completion of a Bachelor's degree. Be prepared to provide documentation during the checkout process.
What you'll learn
Course skills and outcomes
Introduction to Statistics
- Evaluate the use of statistics in real-world applications.
- Describe descriptive and inferential statistical methods and the fundamental elements of statistics.
- Differentiate between qualitative and quantitative variables.
- Create visual representations of quantitative and qualitative data.
- Describe the distribution of quantitative data in terms of shape, center, and spread.
Probability Concepts, Central Tendency, and Variability
- Calculate central tendency and variability in the context of data sets, accounting for possible effects of extreme data points.
- Differentiate between discrete, continuous, and conditional probability.
- Describe how independence and conditional probability are applied in interpreting data.
- Calculate probabilities of compound events in a uniform probability model by applying the rules of probability.
- Evaluate the need to calculate expected values for the purpose of solving problems.
- Describe how the characteristics of standard normal distribution and the Empirical Rule can be used to interpret the significance of standard deviation.
Introduction to Inferential Statistics and Test of Hypothesis
- Evaluate the steps of hypothesis testing.
- Determine the distribution of the sample mean and the distribution of the sample.
- Distinguish an hypothesis regarding a population proportion verses a hypothesis regarding a population mean.
- Distinguish between the t-test and z-test.
- Determine statistically whether to accept or reject the null hypothesis.
- Interpret the results of a hypothesis test.
- State the steps of the decision rule for a hypothesis regarding the standard deviation.
- Perform a chi-square test using equal and unequal expected frequencies.
- Determine normality using a goodness-of-fit test.
- Interpret relative frequencies in the context of the data.
- Investigate patterns of association in bivariate data using scatter plots.
Regression and Correlation
- Use technology to calculate coefficient of correlation and coefficient of determination.
- Distinguish between correlation and causation.
- Develop a single linear regression equation.
- Predict the value of y for a given value of x using a single linear regression equation.
- Apply analysis of residuals, standard error of estimate, confidence intervals, and prediction.
Review of Topics and Objectives
- Review course topics and objectives.
- Apply statistics to real-world scenarios.