MTH 215 - Statistics
Description
MTH 215 is designed for students needing an introductory (not calculus-based) Statistics course. Topics include descriptive statistics, probability distributions, estimation, sampling distributions, hypothesis testing, regression and correlation, chi-square tests, and analysis of variance. In addition, students will solve applied problems by completing required computer assignments using statistical computing software and graphing calculators. Applications apply to all fields including education, social sciences, business, engineering, medicine, and the sciences.
Was previously MA 215.
Credit Hours: 4
Contact Hours: 4
Prerequisites/Other Requirements: Algebra Based - Mathametics OR C or higher in MTH 115 or MTH 136
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corequisite(s): None
Academic Program Prerequisite: None
Consent to Enroll in Course: No Department Consent Required
Dual Enrollment Allowed?: Yes
Course Fees: $19.00
Number of Times Course can be taken for credit: 1
Programs Where This Course is a Requirement:
Pre-Anthropology, A.A. (General Transfer), Pre-Business, A.A. (General Transfer), Pre-Business Administration, A.A. (Western Michigan University), Pre-CyberSecurity, A.A. (General Transfer), Pre-Pharmacy, A.A. (General Transfer),
General Education Requirement:
Mathematics
General Education Learner Outcomes (GELO):
3. Critical Thinking: Gather and synthesize relevant information, evaluate alternative perspectives, or understand disciplinary strategies of creating knowledge., 7. Problem-Solving: Apply theory, calculation, or experimentation to demonstrate effective problem-solving.
Course Learning Outcomes:
1. Compute and interpret statistical measurements from data sets using the appropriate notation.
2. Calculate probabilities of simple and compound events. (GELO 7)
3. Distinguish between discrete and continuous random variables and their probability distributions. (GELO 3)
4. Create and interpret statistical graphs.
5. Understand and apply various sampling techniques.
6. Determine and interpret point estimates and interval estimates of population parameters.
7. Complete and interpret hypothesis tests for population parameters and understand the types of errors associated with testing. (GELO 7)
8. Distinguish between different types of distributions including binomial, normal, student’s t, chi-square and F. (GELO 3)
9. Complete and interpret single factor analysis of variance hypothesis tests.
10. Use appropriate technology to enhance the study of statistics.
11. Use linear regression to model data and interpret results.
12. Discern relevant and irrelevant information to solve problems.
13. Analyze problems to identify appropriate methods with which to solve problems. (GELO 3)
14. Evaluate information presented in verbal, tabular, symbolic, or graphical form.
15. Formulate mathematical models to describe phenomena.
16. Create a written summary of the main ideas extracted from information gathered.
17. Use visual representations such as graphs, charts, or graphics to enhance the meaning of the message that is being communicated.
18. Create and/or organize data and information into meaningful patterns in order to interpret and draw inferences from it.
19. Identify the best solution to a problem or issue.
Course Outline:
I. Introduction to Statistics
A. Sampling methods and errors
B. Introduction to basic statistical terms
C. Data collection, sampling techniques
D. Statistics and technology
II. Descriptive Statistics
A. Summarizing data
B. Graphic presentation and interpretation of data
C. Shapes of distribution
D. Measures of central tendency
E. Measures of dispersion
F. Measures of position
III. Probability
A. Concepts of probability
B. Rules of probability
C. Sample spaces
D. Probability of simple and compound events
E. Counting techniques
IV. Discrete Probability Distribution
A. Random variables
B. Probability distributions of a discrete random variable
C. Mean and standard deviation of a discrete probability distribution
D. The Binomial probability distribution
V. Continuous Probability Distribution
A. applications of normal and uniform distribution
B. Sampling Distribution
C. The Central Limit Theorem
VI. Estimates and Sample Sizes
A. Estimation of mean with large and small samples
B. Computation of confidence intervals
C. Interpretation of confidence intervals
D. Choosing the Sample Size
VII. Inferences Involving One Population
A. The nature of hypothesis testing
B. Inferences about a population mean
C. Inferences about variance and standard deviation
D. Inferences about proportion
VIII. Inferences Involving Two Population
A. Dependent and independent samples
B. Inferences concerning the meandifference between two dependent samples
C. Inferences concerning the difference between the means of two independent samples
D. Inferences concerning the difference between proportion using two independent samples
IX. Linear Correlation and Regression Analysis
A. Linear correlation analysis
B. Inferences about the linear correlation coefficient and coefficient of determination
C. Linear regression analysis
D. Confidence intervals for regression
X. Chi-Square Applications
A. Chi-Square distribution
B. Inferences concerning distribution
C. Inferences concerning independence
XI. Analysis of Variance
A. Introduction to the analysis of variance technique (ANOVA)
B. The logic behind ANOVA
C. Applications of single-factor ANOVA
Approved for Online and Hybrid Delivery?:
Yes
Instructional Strategies:
Lecture: 0-90%
Facilitated discussion: 0-50%
On-line instruction: 0-100%
Group work: 0-30%
Mandatory Course Components:
None
Equivalent Courses:
None
Accepted GRCC Advanced Placement (AP) Exam Credit: Statistics
AP Min. Score: 3
Name of Industry Recognize Credentials: None
Course prepares students to seek the following external certification:
No
Course-Specific Placement Test: None
Course Aligned with ARW/IRW Pairing: N/A
Mandatory Department Assessment Measures:
None
GRCC Course Type:
General Education: Meets the specific criteria for a GRCC Distribution Requirement. The course should be designated by the requirement it fulfills.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 4
People Soft Course ID Number: 101086
Course CIP Code: 27.01
Maximum Course Enrollment: 30
High School Articulation Agreements exist?: No
If yes, with which high schools?: NA
Non-Credit GRCC Articulation Agreement With What Area: No
Identify the Non Credit Programs this Course is Accepted: NA
School: School of STEM
Department: Mathematics
Discipline: MA
Faculty Credential Requirements:
Master’s Degree (GRCC general requirement), Other (list below)
Faculty Credential Requirement Details:
Standard requirements for the Mathematics Department apply.
Major Course Revisions: Prefix, Course Number Change
Last Revision Date Effective: 20260304T10:58:45
Course Review & Revision Year: 2029-2030
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