MA 115 - Math Essentials for Statistics
Description
Students will use mathematical and statistical tools to model and explore real life data. Topics include an introduction to data collection and analysis, statistical studies, descriptive statistics and graphs, linear functions and inequalities, exponential functions, modeling data with functions, linear systems of equations, set theory, and an introduction to probability. This course requires the use of statistical technology.
Credit Hours: 4
Contact Hours: 4
Prerequisites/Other Requirements: C or Higher in MA 98 or MA 99 or ALEKS PPL 22 or Higher
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corequisite(s): MA 15 (ALEKS PPL score of 22 or Higher see class section details for pairing with MA 115)
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:
None
General Education Requirement:
None
General Education Learner Outcomes (GELO):
None
Course Learning Outcomes:
1. Incorporate symbolic, numeric and graphic techniques in solving application problems
2. Create and/or organize data and information into meaningful patterns in order to interpret and draw inferences from it.
a. Summarize and interpret data using tables, graphs and statistical measures
b. Categorize and describe data
3. Demonstrate the application of relationships and operations within set theor
4. Identify, explain and apply linear and non-linear mathematical functions
a. Utilize and interpret mathematical models
b. Utilize linear and non-linear models to describe relationships in data
5. Identify and write statistical questions.
6. Use algebra to manipulate formulas, solve equations and inequalities.
7. Discern relevant and irrelevant information to understand probability and statistical questions.
8. Apply algebraic principles and statistical theory to solve problems.
Course Outline:
1. Statistical Questions and Studies
a. Is this a statistical question?
b. Variables, Treatments and Measures
i. Identify Variables in a study
ii. Identify Treatments in a study
Iiii. dentify Measures in a study
iv. Greek Symbols
v. Data Collection and variables
vi. Data classification as qualitative/quantitative
c. Identify representative, random, and biased samples
d. Types of Studies
i. Observation
ii. Experiment
iii. Simulation
2. Statistical Measures, Charts, Equations and Inequalities for univariate data
a. Descriptive Charts and Tables
i. Frequency Tables
ii. Bar Chart
iii. Circle Chart
iv. Histogram
b. Measures of Central Tendency
i. Mean
ii. Median
iii. Mode
c. Measures of Position
i. Percentiles
ii. Quartiles
iii. Box Plots
d. Measures of Dispersion
i. Absolute Deviation
ii. Range
iii. Variance/Standard Deviation
iv. IQR
e. Equations and inequalities
i. Verbal Translations of equations and inequalities
1. Simple
2. Compound
3. Absolute value
ii. Solve, Graph and Identify Solution sets
3. Functions and Graphs for bivariate data
a. Scatterplots
i. Ordered pairs and tables
ii. Plotting
iii. Common Curves and Curve matching
b. Functions
i. Definition
ii. Notation
iii. Evaluate
iv. Domains in context
c. Linear Functions and Equations
i. Scatterplots, Graphs, and Tables
ii. Slopes
iii. Linear Models - f(x) = a + bx
1. Find and interpret “a” and “b”
2. line of best fit
3. interpolation vs extrapolation
iv. Linear Systems
1. Modeling 2x2 systems
2. Solving 2x2 systems
d. Exponential Function - f(x) = a * b^x
i. Scatterplots, Graphs, and Tables
ii. Find and interpret “a” and “b”
iii. Decay/Growth Models
iv. Compound Interest
v. Annuities and Loans
vi. Using logarithms to solve exponential equations
4. Set Theory, Counting and Probability
a. Introduction to Set Theory
i. Definition of a set
ii. Notation
b. Subsets, Venn Diagrams, and Operations
i. Mutually Exclusive
ii. Complements
iii. Union and Intersection
c. Counting
i. Fundamental Counting Principle
ii. Permutations
iii. Combinations
d. Problem Solving using Set Theory
e. Intro to Probability
i. Basic Concepts
ii Make Predictions
Approved for Online and Hybrid Delivery?:
Yes
Instructional Strategies:
Lecture: 0-80%
Facilitated Discussion: 0-80%
Mediated instruction: 0-60%
Collaborative Work: 0-30%
Mandatory Course Components:
None
Equivalent Courses:
None
Name of Industry Recognize Credentials: None
Course-Specific Placement Test: None
Course Aligned with ARW/IRW Pairing: N/A
Mandatory Department Assessment Measures:
None
Course Type:
Elective- Offering designed to expand learning opportunities for degree seeking students. May or may not be required for students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 4
People Soft Course ID Number: 105128
Course CIP Code: 27.01
Maximum Course Enrollment: 32
High School Articulation Agreements exist?: No
Non-Credit GRCC Articulation Agreement With What Area: No
School: School of STEM
Department: Mathematics
Discipline: MA
First Term Valid: Fall 2022 (8/1/2022)
1st Catalog Year: 2022-2023
Name of Course Author:
Oscar Neal
Faculty Credential Requirements:
18 graduate credit hours in discipline being taught (HLC Requirement), Master’s Degree (GRCC general requirement)
Faculty Credential Requirement Details:
18 graduate credit hours in discipline being taught (HLC requirement) - The instructor must possess a background in statistics and mathematical modeling.
Major Course Revisions: Corequisite
Last Revision Date Effective: 20230307T11:35:42
Course Review & Revision Year: 2027-2028
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