undergraduate business core courses
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B152 - Statistics II

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:

Week 1

Introduction; Review Sampling Distributions

Week 2

Review Confidence Intervals and Hypothesis Tests

Week 3

Alpha and Beta Errors: Hypothesis Tests

Week 4

Statistical Inference: Estimation and Sample Size

Week 5

Statistical Inference: Hypothesis Testing

Week 6

Two Populations: Confidence Intervals

Week 7

Two Populations: Hypothesis Tests

Week 8

Analysis of Variance (ANOVA)

Week 9

Chi-Square Tests

Week 10

Goodness of Fit and Independence

Week 11

Simple Linear Regression: Relationships and Correlation

Week 12

Prediction

Week 13

Simple Linear Regression: Inference for Relationships

Week 14

Multiple Regression

Week 15

Non-Parametric Statistics