Course Objectives


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Mail S. Kellogg



Confidence Intervals Hypothesiss Testing

    Given a word statement, be able to state the null hypothesis, the alternative hypothesis, and the appropriate test statistic.


    Be able to conduct a two tail hypothesis test for
      True mean given a sample mean; standard deviation known.

      True mean given a sample mean; standard deviation unknown.

      Difference between two means; standard deviations known.

      Difference between two means; standard deviations unknown.

      Difference given paired data; standard deviation unknown.

      True variance given a sample variance.

      Ration of variance from two populations given two sample variances.


    Know the underlying assumptions when using the sampling distrutions for hypothesis testing.

Simple Regression

    Given a data set and summary statistics be able to calculate
      Sum of squares of x about x.

      Sum of squares of y about y.

      sum of squares of y about x.

      sum of residuals squared.
      esimtated parameters a and b.

      T-statistic for a.

      T-statistic for b.
      Given a regression output, be able to

      Conduct a test of significance for a or b.

      Calculate a 95% confidence interval of y-hat.
    Given a data set and residuals, be able to conduct a residual analysis.
Transformations

    Given a data set and associated scatter plot, be able to identify an appropriate transformation.

    Given a data set, scatter plot, and two or more outputs, be able to select the best transformative model.

    Given a data set, transformation, and an output. Be able to predict the response y for a given value of x.

Polynomial Regression

    Given a data set and associated scatter plot, be able to identify an appropriate polynomial model.

    Given a data set, polynomial model, and an output, be able to predict the response y for a given value of x.

Multiple Regression

    Given a data set and associated output, be able to determine which if any variables should be excluded from the model.

    Given a data set, associated output, and residual plots, be able to do a residual analysis.

    Given a data set and associated output, be able to predict the response y for a given X vector.
Stepwise Regression

    Given a data set and associated outputs, be able to determine which which subset of variables is appropriate for inclusion.