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MAT 162 Applied Statistics
Credit Hours:  3
Effective Term: Fall 2017
SUN#:  None
AGEC: Mathematics  
Credit Breakdown: 3 Lectures
Times for Credit: 1
Grading Option: A, B, C, D, F

Description: Graphical and quantitative description of data; binomial, normal and t distributions; one and two sample hypothesis tests and confidence intervals; simple linear regression and correlation. Prequisite: MAT121 or higher. Prequisite or corequisite: RDG100

Prerequisites: MAT121 or higher and RDG100

Corequisites: RDG100

Recommendations: None

Measurable Student Learning Outcomes
1. (Comprehension Level) Explain simple statistical methods commonly used in reporting polling data and scientific research studies using correct statistical notation and appropriate language.
2. (Synthesis Level) Construct informative graphical and numerical summaries of data appropriate for the type of data and the context in which the data was collected.
3. (Evaluation Level) Interpret the meaning of graphical and numerical summaries of data in written terms appropriate to the context in which the data was collected.
4. (Analysis Level) Recognize and properly carry out parameter estimation and hypothesis testing procedures with and without the use of technology.
5. (Comprehension Level) Discuss the formalism of parameter estimation and hypothesis testing and how it relates to, supports and advances the scientific method.
6. (Analysis Level) Apply parameter estimation and hypothesis testing to solve problems utilizing appropriate statistical methods.
7. (Analysis Level) Recognize the limitations of statistical methods and discuss the appropriateness of use within a context.
8. (Application Level) Empirically and theoretically obtain the probability of an event.
9. (Application Level) Apply the normal distribution to calculate probability of event.
10. (Application Level) Apply the properties of the t-distribution, t-statistic, and degrees of freedom to construct confidence intervals.
11. (Analysis Level) Examine the relationship between two quantitative variables using the correlation coefficient and by computing the regression equation.
Internal/External Standards Accreditation