Statistics –

mth540

(3 credits)

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 is 6 weeks. This course is available to take individually or as part of a degree or certificate program. To enroll, speak with an Enrollment Advisor.

Introduction to Statistics

  • Describe the importance of statistics.
  • Explain the value of statistics as it relates to you.
  • Calculate the mean, median, and mode for grouped and ungrouped data.
  • Develop a frequency distribution based on raw data.
  • Identify the various symbols and nomenclature used in statistics.

Distribution and Variation

  • Determine whether or not data is normally distributed.
  • Utilize graphical representation to display data.
  • Differentiate among the various levels of measurement.
  • Calculate range, variance, and standard deviation for grouped and ungrouped data.
  • Interpret the significance of standard deviation using the Empirical Rule.

Introduction to Inferential Statistics and Test of Hypothesis

  • State the five steps in the decision rule.
  • Formulate null and alternative hypotheses.
  • Calculate a test statistic in a one-tailed and two-tailed z-test.
  • Determine statistically whether to accept or reject the null hypothesis.
  • Interpret the results of a hypothesis test.

Chi-square Applications

  • Perform a chi-square test using equal and unequal expected frequencies.
  • Determine normality using a Goodness-of-Fit Test.

Regression and Correlation

  • Calculate coefficient of correlation and coefficient of determination.
  • Develop a single linear regression equation.
  • Predict the value of y for a given value of x using a single linear regression equation.
  • Calculate standard error of estimate, confidence intervals, and prediction intervals.
  • Interpret standard error of estimate, confidence intervals, and prediction intervals.

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