Virtual Fall 2020: August 24 to December 11, 2020

Goals

Student Learning Outcomes

MAT 13

Students will analyze a data set using appropriate technology to calculate and interpret summary statistics.
Students will evaluate and assess a data set in context to identify the appropriate inferential procedure for constructing an estimate or making a decision about a population parameter.
Students will analyze a sample space to determine probabilities and identify unusual events.
Students will apply the concept of sampling distribution in the context of the Central Limit Theorem to calculate probabilities.

MAT 213

Respond to a case study scenario of a struggling student to provide a recommendation for academic success that incorporates metacognition, study skills, time management and/or learning theory.
Construct, use, and interpret mathematical models, specifically linear functions to represent, interpret and communicate linear relationships in quantitative data.
Evaluate algebraic expressions with multiple variables and involving several operations.

Upon completing this course successfully, you should be able to:

interpret data displayed in tables and graphically, including frequency distributions and histograms.
apply concepts of sample space and probability with single selections and multiple selections.
calculate measures of central tendency and variation for a given data set.
identify the standard methods of obtaining data and identify advantages and disadvantages of each.
compare and contrast standard sampling methods of statistics.
calculate the mean and variance of a discrete distribution.
calculate probabilities using normal and t-distributions.
distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem, including the sampling distribution for the mean.
construct and interpret confidence intervals.
determine and interpret levels of statistical significance including p-values.
interpret the output of a technology-based statistical analysis.
identify the basic concept of hypothesis testing including Type I and II errors.
formulate hypothesis tests involving samples from one and two populations.
select the appropriate technique for testing a hypothesis and interpret the result.
use linear regression and correlation for estimation and inference and interpret the associated statistics.
formulate hypothesis tests for multiple populations, make inferences and interpret the associated statistics, including One-Way ANOVA.
use appropriate statistical techniques to analyze and interpret applications based on data from at least four of the following disciplines: business, economics, social science, psychology, political science, administration of justice, life science, physical science, health science, information technology, and education.

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