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. Students are encouraged to complete the CSI Self Placement Completion prior to registering for this course.
General Education Competency
Mathematical Way of Knowing
Notes
For appropriate math placement, all students should complete the
CSI self-placement process provided by the Mathematics Department before signing up for their first math course.
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 Way of Knowing
Credit Hours Narrative
3 Credits
Semester Contact Hours Lecture
45
Notes and Advisories (only if included in catalog)
For appropriate math placement, all students should complete the
CSI self-placement process provided by the Mathematics Department before signing up for their first math course.
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. Students are encouraged to complete the CSI Self Placement Completion prior to registering for this course.
IV. Student Learning Outcomes
Upon completion of this course, a student will be able to:
- Learn, understand, and demonstrate applying the statistical concepts to skill-based problems.
- Learn, understand, calculate, interpret, and graph descriptive statistical values.
- Calculate, analyze, and interpret results of confidence intervals and hypothesis testing in inferential statistics.
- Apply the appropriate statistical tools to describe, analyze, and infer from real world data.
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
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