BA 254 - Introduction to Statistics with Applied Models
Description
This course is an introduction to the statistical concepts of organizing, analyzing, and interpreting data. Topics include descriptive statistics, probabilities, probability distributions, sampling, interval estimation, tests of hypotheses, one way analysis of variance, correlation and regression. Applied business data sets and case studies related to operational concepts are leveraged.
Credit Hours: 3
Contact Hours: 3
Prerequisites/Other Requirements: None
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
Number of Times Course can be taken for credit: 1
Programs Where This Course is a Requirement:
Pre-Accounting, A.B. (3+1, Davenport University), Pre-Business Administration, A.A. (Western Michigan University), Pre-Business, A.A. (Cornerstone University), Pre-Business, A.B. (3+1, Davenport University), Pre-Business, A.A. (General Transfer), Pre-Business, A.A. (Grand Valley State University), Pre-CyberSecurity, A.A. (General Transfer), Pre-Management, A.B. (3+1, Davenport University), Pre-Marketing, A.B. (3+1, Davenport University), Pre-Music and Entertainment Business, A.A. (3 + 1, Ferris State University)
Other Courses Where This Course is a Prerequisite: None
Other Courses Where this Course is a Corequisite: None
Other Courses Where This course is included in within the Description: None
General Education Requirement:
None
General Education Learner Outcomes (GELO):
NA
Course Learning Outcomes:
1. Compute and interpret statistical measurements from data sets.
2. Calculate probabilities of simple and compound events.
3. Determine the difference between discrete and continuous variables and apply the appropriate probability distributions.
4. Apply various sampling techniques.
5. Develop point estimates and confidence intervals of population parameters.
6. Complete various types of hypothesis tests and understand the risks associated with testing.
7. Leverage appropriate technology to develop time series data and be able to apply to specific business examples.
8. Leverage appropriate technology to establish correlation and develop a linear regression model.
9. Use visual representations such as graphs, charts, or graphics to enhance the meaning of the message that is being communicated.
10. Use rules or frameworks to provide context for and understand problems or issues.
11. Evaluate information to identify limitations and biases.
12. Consider the context, costs, benefits and consequences of potential solutions to problems or issues.
Course Outline:
I. Course Introduction
II. Statistics and Data [Concept Review]
A. Relevance and Context
B. Sample Rationale
C. Data Types
D. Cross-sectional and Time Series Components
E. Variable and Scales of Measurement
F. Ethics
III. Describing Data: Numerical Measures [Location and Relative Position]
A. Mean, Median, and Mode
B. Weighted Mean
C. Percentiles
D. Box Plots
E. Interquartile Range
F. Range, MAD, Variance, Standard Deviation, and Coefficient of Variation
G. Chebyshev’s Theorem
H. Empirical Rule
IV. Describing Data: Numerical Measures [Extensions]
A. Pearson’s Coefficient of Skewness
B. Introduction: Z-Scores
C. Introduction: Covariance and Correlation
V. Probability Concepts
A. Probability Concepts
B. Rules of Probability
VI. Discrete Probability Distributions
A. Discrete Probability Distributions Mean Calculation
B. Discrete Probability Distributions Variance Calculation
C. Survey of Bi-Nominal Probability Distributions
VII. Continuous Probability Distributions [Introduction]
A. Uniform Probability Distributions
B. Normal Probability Distributions Introduction
C. Applications of the Standard Normal Distribution
VIII. Sampling Methods and Applications [Introduction]
A. Sampling Bias
B. Sampling Methods
C. Sampling Error
D. Sampling Distribution of Sample Mean
E. Central Limit Theorem
F. Sampling Distribution of Sample Proportion
IX. Interval Estimation [Introduction]
A. Confidence Interval Construction for Population Mean: Large Sample Size
B. t-Distribution Introduction
C. Confidence Interval Construction for Population Mean: Small Sample Size
D. Confidence Interval Construction for Population Proportion
E. Selecting Recommended Sample Size
X. Introduction to Hypothesis Testing
A. Framework Introduction (Critical Value and p-Value Testing)
B. Type I and II Errors
C. Testing Single Population Mean; Large Sample
D. Testing Single Population Mean; Small Sample
E. Testing Single Population Proportion
XI. Comparisons Involving Population Means
A. Confidence Interval for Population Mean Difference
B. Hypothesis Test for Population Mean Difference (Large and Small sample size)
XII. Comparisons Involving Population Proportions
A. Hypothesis Test for Population Proportion Differences
XIII. Comparisons Involving Population Variances
A. Introduction to f-Distribution
B. Hypothesis Test for Population Variances
XIV. Comparisons Involving Multiple Population Means
A. Single Factor One-Way ANOVA
XV. Expected Frequency Tests
A. Introduction of the chi-square test statistic
B. Goodness-of-fit test
XVI. Survey of Regression Analysis
A. Introduction to Correlation and Regression Analysis
B. Testing the Significance of Correlation Coefficient
Approved for Online and Hybrid Delivery?:
No
Approved for Hybrid Delivery?
Yes
Instructional Strategies:
Lecture: 0-70%
Discussion: 0-50%
Demonstration: 0-50%
In-class activities: 0-20%
Note as specified below: The lower ends of the percentages should add up to less than 100% and the upper ends should exceed 100%.
Mandatory Course Components:
Examinations: 70%
Applied Projects: 30%
Equivalent Courses:
None
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
Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 3
People Soft Course ID Number: 100036
Course CIP Code: 52.9999
Maximum Course Enrollment: 28
General Room Request: None
High School Articulation Agreements exist?: No
If yes, with which high schools?: None
Non-Credit GRCC Articulation Agreement With What Area: No
Identify the Non Credit Programs this Course is Accepted: NA
School: School of Business & Industry
Department: Business
Discipline: BA
Faculty Credential Requirements:
Master’s Degree (GRCC general requirement), Professionally qualified through work experience in field (Perkins Act or Other) (list below)
Faculty Credential Requirement Details:
[Additional Perkins professional experience must be met.] Acceptable Fields Include: Research, Operations, Quality, Statistics, & Math
Major Course Revisions: N/A
Last Revision Date Effective: 20250219T15:51:51
Course Review & Revision Year: 2029-2030
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