MATH 153 Statistical Reasoning*

This algebra-based probability and statistics course covers descriptive statistics, binomial and normal distributions, confidence intervals, and hypothesis testing using z, t, chi square, and f distributions. Correlation and regression are also introduced.

Credits

3 Credits

Prerequisite

MATH 043 with a 'C' or better or CSI placement test score.

General Education Competency

Mathematical Ways of Knowing

MATH 153Statistical Reasoning*

Please note: This is not a course syllabus. A course syllabus is unique to a particular section of a course by instructor. This curriculum guide provides general information about a course.

I. General Information

Department

Mathematics & Engineering

II. Course Specification

Course Type

General Education

General Education Competency

Mathematical Ways of Knowing

Credit Hours Narrative

3 Credits

Semester Contact Hours Lecture

45

Prerequisite Narrative

MATH 043 with a 'C' or better or CSI placement test score.

Repeatable

Letter grade

III. Catalog Course Description

This algebra-based probability and statistics course covers descriptive statistics, binomial and normal distributions, confidence intervals, and hypothesis testing using z, t, chi square, and f distributions. Correlation and regression are also introduced.

IV. Student Learning Outcomes

Upon completion of this course, a student will be able to:

This algebra-based probability and statistics course covers descriptive statistics, binomial and normal distribution, confidence intervals, and hypothesis testing using z-, t-, chi square-, and f-distributions. Correlation and regression are also introduced.

V. Topical Outline (Course Content)

1. The student will be able to learn, understand, and demonstrate applying the statistical concepts to skill-based problems from the course content listed in section V. 2. The student will be able to learn, understand, calculate, interpret, and graph descriptive statistical values. 3. The student will be able to calculate, analyze, and interpret results of confidence intervals and hypothesis testing in inferential statistics. 4. The student will be able to apply the appropriate statistical tools to describe, analyze, and infer from real world data.

VI. Delivery Methodologies

Required Assignments

Definitions of the branches of statistics Variables and types of data Data collection and sampling techniques Types of statistical studies Measures of central tendency and variation Measures of position and exploratory data analysis Discrete probability distributions and their measures of central tendency and variation, including the binomial distribution The normal and standard normal distribution, including applications in determining probabilities of occurrence The Central Limit Theorem Confidence intervals for means, proportions, variances, and standard deviations, using the z-, t-, and ?2 - distributions Hypothesis testing, including the traditional method and the p-value method, for means, proportions, variances, and standard deviations, using the z-, t-, ?2 -, and F-distributions Scatter plots, correlation coefficient (calculation and significance), regression and coefficient of determination Hypothesis testing for Goodness of Fit using the ?2 -distribution

Required Text

Final Exam

Specific Course Activity Assignment or Assessment Requirements

Definitions of the branches of statistics Variables and types of data Data collection and sampling techniques Types of statistical studies Measures of central tendency and variation Measures of position and exploratory data analysis Discrete probability distributions and their measures of central tendency and variation, including the binomial distribution The normal and standard normal distribution, including applications in determining probabilities of occurrence The Central Limit Theorem Confidence intervals for means, proportions, variances, and standard deviations, using the z-, t-, and ?2 - distributions Hypothesis testing, including the traditional method and the p-value method, for means, proportions, variances, and standard deviations, using the z-, t-, ?2 -, and F-distributions Scatter plots, correlation coefficient (calculation and significance), regression and coefficient of determination Hypothesis testing for Goodness of Fit using the ?2 -distribution