mth464 | undergraduate

Inferential Statistics

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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 To enroll, speak with an Enrollment Representative.

Course details:

Credits: 3
Continuing education units: XX
Professional development units: XX
Duration: 5 weeks

topic title goes here

    Hypothesis Testing

    • Perform hypothesis tests about two means.
    • 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.

    Inferential Methods in Regression

    • Verify that residuals are normally distributed.

    • 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.

    Analysis of Variance (ANOVA)

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

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

    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.
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