Inferential Statistics – mth464 (3 credits)

This course presents techniques for analyzing data and uses of inferential statistics. Students examine the applications of hypothesis testing using normal probability distribution, t-distribution, analysis of variance (ANOVA), linear regression, and nonparametric data.

This undergraduate-level course is 5 weeks. This course is available to take individually or as part of a degree or certificate program. To enroll, speak with an Enrollment Representative.

Inferential Methods in Regression

  • Determine if a linear relationship exists between a dependent and independent variables.
  • Formulate a hypothesis test to determine if an independent variable is useful for making decisions.
  • Construct confidence intervals for mean responses.
  • Construct prediction intervals for individual responses.
  • Verify that residuals are normally distributed.

Applications of Normal Distribution, Sampling Distributions, and Confidence Intervals

  • Describe the distribution of sample means.
  • Describe the distribution of sample proportions.

  • Compute probabilities of sample proportions.
  • Construct confidence intervals for population means.

  • Construct confidence intervals for population proportions.

Nonparametric Tests

  • Perform chi square goodness of fit tests.
  • Perform tests for homogeneity of population.
  • Perform chi square tests for independence.

Analysis of Variance (ANOVA)

  • Perform hypothesis tests using one-way ANOVA.
  • Perform tests of hypothesis to determine whether the variances of two populations are equal.

  • Perform two-way ANOVA designs.
  • Interpret the summary output of ANOVA tables.

Hypothesis Testing

  • Compute the power of tests.
  • Utilize hypothesis testing to formulate a decision about a claim.

  • Perform hypothesis tests for population means.
  • Perform hypothesis tests for population proportions.
  • Compute the probability of Type II errors.

  • Perform hypothesis tests about two means.

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