Download COURSE SYLLABUS

MASTER COURSE OUTLINE
Big Bend Community College
Date: January 2007
DEPT: MTH
NO: 161
COURSE TITLE: Statistics
CIP Code: 27.0501
Intent Code: 11
SIS Code:
CREDITS: 5
Total Contact Hrs: 55
Lecture Hours Per Qtr: 55
Lab Hours Per Qtr:
Other Hours Per Qtr:
Distribution Designation: Math/Science
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PREPARED BY: Stephen Lane
COURSE DESCRIPTION:
This course is an introduction to descriptive statistics, probability and its applications,
statistical inference and hypothesis testing, predictive statistics and linear regression.
PREREQUISITE(S):
Appropriate scores in the BBCC Mathematics Assessment
or successful completion of MPC 099 or MPC 091, 092, and 093.
TEXT: Appropriate college level text as chosen by instructor.
COURSE GOALS: After completion of the course the student should have:
a. developed some degree of understanding of the origins and utility of statistical
analysis;
b. a higher probability of success in advanced statistics courses;
c. have an understanding of how statistics affects their lives;
d. to be able to ask intelligent questions when involved in situations utilizing
statistical methods in the real world.
COURSE OBJECTIVES: Upon successful completion of the course, the student will
be able to:
1. compute the mean, median and mode and standard deviation of a population
distribution;
2. apply basic descriptive graphing techniques to sample and population data;
3. apply the basic concepts of probability to appropriate situations;
4. be able to compute the appropriate probabilities using various probability
distributions such as the binomial, poisson and normal distributions;
5. find confidence intervals for the mean of a population;
6. perform hypothesis testing using various statistical methods;
7. derive the regression line for a collection of data;
8. make appropriate predictions using the regression line;
9. do appropriate statistical inferences on the regression line.
COURSE CONTENT OUTLINE:
I.
Introduction to probability
General probability concepts.
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II.
III.
IV.
Probability distributions.
Baye's Theorem.
Expected value of a distribution.
Descriptive statistics
Analysis of data using graphs, charts, box plots, whisker diagrams, etc.
Relation of distributions to probability concepts.
Measures of central tendency.
Measures of variation.
Tsychebev's rule and the Normal distribution.
Advanced probability and statistical testing.
Normal and Poisson distributions.
Central Limit Theorem.
Standard Error of the mean.
Confidence intervals.
Hypothesis Tests
Predictive Statistics and Chi-Square and F-distributions.
Linear Regression and Correlation.
Hypothesis tests with standard deviations.
EVALUATION METHODS/GRADING PROCEDURES:
In order to give the instructor the greatest flexibility in assigning a grade for the course,
grades will be based on various instruments at the instructors' discretion. However, to
maintain instructional integrity there must be at least three class exams and a statistical
project designed to show the student the application side of statistics. At least 60% of
the grade will be based on quantifiable work (exams, homework, quizzes, etc.). The
remaining portion of the grade may be based on quantifiable work, attendance, projects,
journal work, etc., at the instructor's discretion.
The following is a compilation of acceptable grading instruments: In class exams and a
final, attendance, homework or quizzes, research paper, modeling projects on the
calculator or computer. Other projects or assignments as deemed appropriate at the
instructor's discretion.
PLANNED TEACHING METHODS/LEARNING STRATEGIES:
x Lecture
x Small Group Discussion
x Special Project
Laboratory
Audiovisual
Other (List)
Supervised Clinical
Individual Instruction
Division Chair Signature
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