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.
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.
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.
Compute the power of tests.
Perform hypothesis tests about two means.
Analysis of Variance (ANOVA)
Perform tests of hypothesis to determine whether the variances of two populations are equal.
Perform hypothesis tests using one-way ANOVA.
Perform two-way ANOVA designs.
Interpret the summary output of ANOVA tables.
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.
Verify that residuals are normally distributed.
Construct confidence intervals for mean responses.
Construct prediction intervals for individual responses.
Perform chi square goodness of fit tests.
Perform chi square tests for independence.
Perform tests for homogeneity of population.
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