MCCCD Program Description

Business Statistics

Course: GBS221

First Term: 2011 Fall

Lecture 3 Credit(s) 3 Period(s) 3 Load
Subject Type: Academic
Load Formula: S

Description: Business applications of descriptive and inferential statistics, measurement of relationships, and statistical process management. Includes the use of spreadsheet software for business statistical analysis

MCCCD Official Course Competencies
1. Collect, organize, present, analyze, and interpret numerical data using frequency distributions and graphical presentations. (I) 2. Calculate and interpret the measures of central tendency for either raw or grouped data. (I) 3. Calculate and interpret the measures of dispersion and skewness for a data set. (I) 4. Use discrete and continuous probability distributions in probability applications. (II) 5. Explain probability sampling and sampling distributions, and describe their uses. (III) 6. Use statistical inference techniques and confidence levels for decision making when testing hypotheses. (IV) 7. Use regression and correlation analysis, and interpret the results of the analysis. (V) 8. Use statistical process management and control charting to solve statistical quality control problems. (VI)
MCCCD Official Course Competencies must be coordinated with the content outline so that each major point in the outline serves one or more competencies. MCCCD faculty retains authority in determining the pedagogical approach, methodology, content sequencing, and assessment metrics for student work. Please see individual course syllabi for additional information, including specific course requirements.

MCCCD Official Course Outline
I. Collection, Organization, Presentation, Analysis, and Interpretation of Numerical Data A. Frequency Distribution B. Graphical Representation 1. Histogram 2. Frequency Polygon C. Measures of Central Tendency (Ungrouped and Grouped) 1. Mean 2. Median 3. Mode D. Measures of Dispersion (Ungrouped and Grouped) 1. Range 2. Variance 3. Average Deviation 4. Standard Deviation 5. Empirical Rule 6. Skewness 7. Relative Dispersion II. Probability, Counting Methods, and Probability Distributions A. Rules of Multiplication and Addition B. Bayes Theorem C. Combinations and Permutations D. Random Variables E. Discrete Random Variables F. The Binomial Distribution G. Poisson Distribution H. The Normal Probability Distribution III. Sampling Methods and Sampling Distribution A. Probability Sampling B. Sampling Error C. Sampling Distribution of the Means D. Central Limit Theorem E. Confidence Intervals F. Selecting a Sample Size 1. Mean 2. Proportion IV. Hypothesis Testing A. Large Samples 1. Means a. Testing One Population Mean b. Testing the Difference Between Two Population Means 2. Proportions a. Testing One Population Proportion b. Testing the Difference Between Two Population Proportion B. Small Samples 1. Testing One Population Mean 2. Comparing Two Population Means V. Regression and Correlation A. Coefficients of Correlation and Determination B. Testing the Significance of the Coefficient of Correlation C. Regression Equation D. Standard Error of the Estimate E. Confidence-Interval Estimates VI. Statistical Quality Control A. The Control Chart B. Acceptance Sampling

MCCCD Governing Board Approval Date: 4/24/2007

All information published is subject to change without notice. Every effort has been made to ensure the accuracy of information presented, but based on the dynamic nature of the curricular process, course and program information is subject to change in order to reflect the most current information available.