Oct 27, 2021
MATH 117 - Elements of Statistics
An introductory noncalculus statistics course to serve a variety of students who need a working knowledge of statistics. Descriptive analysis and treatment of data, probability and probability distributions, statistical inferences, linear regression and correlations, chi-square, and some nonparametric statistics. Preexisting statistical computer programs may be used for some applications. PRE- or COREQUISITE(S): Appropriate score on mathematics assessment test, a grade of C or better in MATH 050 or MATH 092 , or concurrent enrollment in MATH 017 , or consent of department. Assessment Level(s): ENGL 101 /ENGL 011 or AELW 940 /ELAI 990 , READ 120 or AELR 930 /ELAR 980 . Three hours each week.
3 semester hours
Upon course completion, a student will be able to:
- Calculate and interpret confidence interval estimates of population parameters (proportions and/or means).
- Demonstrate an understanding of the importance that random sampling and randomization play in producing data that allow one to draw conclusions about the underlying populations.
- Explain that statistical procedures have specific requirements necessary for their application and verify that the fulfillment of these requirements has been satisfied for the situation with which the student is dealing.
- Express in clearly written form, and always in the context of the particular problem situation, the results of statistical investigations and analyses.
- Formulate and conduct tests of significance for population parameters (proportions and/or means) and interpret the results in the original context.
- Use a variety of graphical and numeric tools to explore and summarize categorical and quantitative data, including linear models of associations between two quantitative variables.
- Use statistical software (computer- or calculator-based) to explore and analyze data and interpret the results produced by that software in context.
- Use the results of the central limit theorems for sample proportions and sample means to predict the long-term patterns of variation of those statistics under repeated sampling based on an understanding of the normal distribution.
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