BUSN 210 - Statistical Analysis

5 Credits

Statistical methods and their application to practical and economic data. Teaches basic statistical analysis concepts and techniques, stressing how statistical techniques can be used to make better decisions under conditions of uncertainty. Uses data sets from business and government to make practice problems as realistic as possible and includes Excel appplications for the solving of statistical analysis problems.

Pre-requisite(s) MATH 091 w/ min. 2.0
Placement Eligibility Math 107, 111, 146, 180, 098
Fees

Quarters Typically Offered
Summer Online
Fall Day
Winter Day, Online
Spring Day

Designed to Serve Business transfer students, nursing and pharmacy students and any student needing to gain basic introductory grasp of statistical concepts.
Active Date 20190625T13:34:58

Grading Basis Decimal Grade
Class Limit 35
Contact Hours: Lecture 55
Total Contact Hours 55
Degree Distributions:
ProfTech Course Yes
Transferable Elective Yes
ProfTech Related Instruction

Computation

Course Outline
Graphic methods of statistical analysis. Measures of central tendency and dispersions, probability and sampling distributions hypothesis testing, and simple correlation and regression analysis. A. Introduction: What is statistics? B. Frequency Distributions C. Graphic Presentation of Data D. Measures of Central Tendency E. Measures of Dispersion and Skewness F. A Survey of Probability Concepts G. Discrete Probability Distributions H. The Normal Probability Distribution I. Sampling Methods J. Tests of Hypotheses: Large Samples K. Test of Hypotheses: Proportions L. Student’s t Test: Small Samples M. Analysis of Variance N. Simple Regression Analysis O. Multiple Regression and Correlation Analysis P. Chi-Square Distribution

Student Learning Outcomes
Create descriptive tabular and visual reports that use raw data for business decision-making.

Calculate and apply probability rules for business decision-making.

Use samples and sampling distributions for business decision-making.

Make inferences based on sample data using methods such as interval estimation and hypothesis testing.

Perform regression analysis on x and y data sets for business decision-making.

Perform analysis of large data sets using Microsoft Excel for business decision-making.