Learning Outcomes

Assessment

1. Estimate population parameters using point estimates and confidence intervals, for large and small samples. Determine the appropriate sample size for a specific margin of error and confidence level.

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2. Given a research question involving a single sample, formulate a null and alternative hypothesis, choose a test statistic, describe the rejection criteria, make a decision using a p-value or critical value of the test statistic, and draw an appropriate conclusion. Describe Type I and Type II errors.

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3. Apply confidence interval estimation and hypothesis testing for two-sample problems (e.g. difference of two means) for both large sample and small problems, and, further, be familiar with the concept of dependent samples (paired differences).

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4. Understand and apply simple linear regression models, find and interpret the coefficient of determination, estimate model parameters, make inferences about model parameters. Use the regression model for estimation and prediction. Assess the model using residuals. Compute the sample correlation coefficient and understand its properties; make inferences about the population correlation coefficient.

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5. Understand and apply multiple linear regression models; determine model parameters using a computer software package; use the computer output for estimation and inference on model parameters; find and interpret the coefficient of determination.

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6. Understand and interpret two-way contingency tables and conduct chi-square tests of independence and goodness of fit.

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7. Understand and interpret statistical quality control charts, including process location and variation.

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8. Understand experimental designs such as single-factor completely randomized and randomized block experimental designs; understand and be able to carry out the steps of analysis of variance for these designs (set up hypotheses, compute the ANOVA table and make the appropriate inferences).

Classroom discussions including questions, Student board exercises, Exams