Course details are provided as a general guide. Faculty may change certain aspects associated with the description that follows. Please check with the bookstore for specific textbooks and other required readings. Faculty may also place required readings on reserve at Gutman Library or on the Library's ERES (electronic reserve) system. Course registrants are responsible for obtaining a syllabus and project descriptions from the instructor.
COURSE DESCRIPTION:
Statistics II is a sophomore-level business core course. Review of sampling distributions; Confidence intervals and hypothesis tests for two-samples; simple linear regression, multiple linear regression with emphasis on computer output; one-and two-way analysis of variance; applications of the Chi-square statistic; non-parametric statistical techniques.
COURSE PREREQUISITES:
The prerequisite for Statistics II is: Grade of "C" (2.00) or better in B151 Statistics I.
It is your responsibility to make certain that you have successfully completed this requirement. If at any time during the semester it is learned that you have not successfully completed the prerequisites, you will be dropped from the course and receive neither credit nor a tuition refund.
COURSE OBJECTIVES & EXPECTED OUTCOMES:
Students are expected to apply the skills they learned in Statistics I in quantitative decision-making situations.
COURSE POLICIES:
Please refer to the policies page for minimum expected standards and behavior in all classes.
COURSE OUTLINE:
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Week 1 |
Introduction; Review Sampling Distributions |
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Week 2 |
Review Confidence Intervals and Hypothesis Tests |
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Week 3 |
Alpha and Beta Errors: Hypothesis Tests |
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Week 4 |
Statistical Inference: Estimation and Sample Size |
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Week 5 |
Statistical Inference: Hypothesis Testing |
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Week 6 |
Two Populations: Confidence Intervals |
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Week 7 |
Two Populations: Hypothesis Tests |
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Week 8 |
Analysis of Variance (ANOVA) |
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Week 9 |
Chi-Square Tests |
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Week 10 |
Goodness of Fit and Independence |
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Week 11 |
Simple Linear Regression: Relationships and Correlation |
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Week 12 |
Prediction |
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Week 13 |
Simple Linear Regression: Inference for Relationships |
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Week 14 |
Multiple Regression |
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Week 15 |
Non-Parametric Statistics |
