Course Number: BU310
Course Title: Applied Statistics

STUDENT LEARNING OUTCOMES

General

The course builds on the fundamental statistics concepts developed in the introductory statistics course. Generally, the student is expected to:

1) develop an understanding of statistical methods of sampling and estimating population statistics.
2) develop and demonstrate competence in using excel to calculate point estimates and confidence intervals.
3) be able to use statistical methods to test hypothesis, recognize trends and make forecasts to support decisions in the
    business/economics environment

Specific

Students will be able to:

  1. explain the difference between a population and a sample.
  2. discuss different methods of sampling and choose the best for an application.
  3. calculate point estimators of a population from sample data.
  4. determine if a point estimator is unbiased, efficient and consistent.
  5. construct interval estimates of a population mean for a large sample and a small sample.
  6. determine an appropriate sample size.
  7. develop null and alternative hypothesis for testing research hypothesis, testing validity of claims and testing decision making
      situations.
  8. describe Type I and Type II errors
  9. use test statistics for one and two-tailed test for large and small samples.
10. perform one and two-tailed test for large and small samples using p-values.
11. make estimates of the difference between means for two populations.
12. perform hypothesis test about the difference between means of two populations
13. identify independent samples, dependent samples, and matched samples.
14. make inferences about the variance of a population.
15. describe goodness of fit test and test of independence using appropriate statistical distributions.
16. read an ANOVA table and use analysis of variance test statistics to test Between-treatment and Within-treatment variances.
17. discuss experimental design and describe randomized designs and block designs.
18. use linear regression to recognize trends and make forecasts.
19. determine when to add or delete variables in model building.
20. apply trend, cyclical, seasonal, and irregular components.
21. apply smoothing methods in forecasting problems.
22. recognize and make adjustments for trends and seasonal differences.