MATH& 146: Introduction to Statistics

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Class Program

Mathematics

Credits

Clinical Hours None

Weekly Contact Hours

Course ID

091776

Meets Degree Requirements For

Natural Science,

Quantitative Skills

Description

Fundamental concepts and applications of descriptive and inferential statistics. Includes measures of central tendency and variability, statistical graphs, probability, the normal distribution, hypothesis testing, confidence intervals, ANOVA testing and regression analysis. Graphing calculator or statistical software techniques are used throughout the course.

Grading Basis

Graded

Prerequisites

MATH 99 with a "C" (2.0) or better, or a grade of 3 or higher on the Smarter Balanced exam, "C" (2.0) or better in high school Algebra, Precalculus or Calculus within past three years or appropriate placement score.

Course Learning Outcomes

Calculate and interpret measures of central tendency and variability.

Create and interpret histograms and other types of statistical graphs.

Discuss how statistical techniques such as random sampling help to overcome bias.

Calculate probability with the fundamental counting principle, compound event rules, and the binomial probability formula.

Relate normally-distributed raw scores to z-scores, probabilities, and p.

Perform hypothesis tests on proportions and their differences, and on means and their differences, with large and small samples.

Create and interpret estimates, using confidence intervals.

Perform chi-square hypothesis tests.

Perform ANOVA hypothesis tests.

Create linear regression models, perform hypothesis testing on the coefficient of correlation, and use the model to make predictions.

Utilize a graphing calculator and/or statistical software to facilitate statistical computations.

Core Topics

Measures of central tendency and variability
Histograms and other types of statistical graphs
Basic sampling methods and pitfalls
Techniques for calculating probability, including the binomial probability formula
The normal distribution and its relationship to z-scores, probabilities, and percentiles
Hypothesis tests on proportions and their differences, and on means and their differences, with large and small samples
Confidence intervals and estimation
Chi-square hypothesis tests
ANOVA hypothesis testing
Linear regression, correlation and reliability of predictions