Master Course Outlinedept: Math&

MASTER COURSE OUTLINEDEPT: MATH&

Big Bend Community CollegeNO: 146

Date: April 2009(Formerly: MTH 161)

COURSE TITLE: Introduction to Statistics

CIP Code: 27.0501CREDITS: 5

Intent Code: 11Total Contact Hrs: 55

SIS Code: 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, Barbara Whitney, Sonia Farag, Salah Abed, Tyler Wallace.

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 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.

Probability distributions.

Baye's Theorem.

Expected value of a distribution.

II.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.

Tschebechev's rule and the Normal distribution.

III.Advanced probability and statistical testing.

Normal and Poisson distributions.

Central Limit Theorem.

Standard Error of the mean.

Confidence intervals.

Hypothesis Tests

IV.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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