MATH 256 - Statistics for Business and Social Science

Credits: 5
Study of descriptive and inferential statistics; boxplots, histograms and scatterplots; introduction to design of experiments; measures of central tendency; frequency distributions; probability distributions; sampling and sampling distributions; hypothesis testing; confidence intervals; and linear regression.

Prerequisite: MATH 106,  MATH& 141  or MATH 147  with a grade of 2.0 or higher; or appropriate math placement. Recommended: Eligible for READ 104 .

Satisfies Requirement: Natural Science and Quantitative Skills

Course Outcomes:
Students who successfully complete this class will be able to:

1. Define and use common statistical terminology.
2. Identify the major categories of experimental designs and sampling methods.
3. Identify common sources of bias in surveys and experiments.
4. Construct and interpret frequency distributions, histograms, pie charts, and box plots.
5. Calculate and interpret the measures of center and spread.
6. Carry out a linear regression analysis of paired data.
7. State and apply the basic axioms and theorems of probability.
8. State and apply the central limit theorem.
9. Calculate confidence intervals and conduct hypothesis tests for one and two samples using the standard normal, Student-t, and Chi square distributions.

1. Recognize which quantitative or symbolic reasoning methods are appropriate for solving a given problem.

2. Correctly implement the quantitative or symbolic reasoning methods that are appropriate for solving a given problem.

3. Translate data into various formats such as symbolic language, equations, graphs, and formulas.

• Quantitative and Symbolic Reasoning - Quantitative Reasoning encompasses abilities necessary for a student to become literate in today’s technological world. Quantitative reasoning begins with basic skills and extends to problem solving.