Syllabus Stuff

Below you'll find the required parts of a syllabus, so that I can print this and send it to admin to make them happy. You're welcome to peruse it... but it's dry and not terribly informative in terms of what the course will be like or how I am as an instructor. (It probably won't make much sense either unless you've previously taking statistics.)

• Lecture Notes (available on Canvas)

• StatCrunch access

• Math 96 with a grade of “C” or better or Assessment Skill Level M50.

• Math 92 with a grade of "C" or better or Assessment Skill Level M45.

This course covers descriptive and inferential statistics. The descriptive portion analyzes data through graphs, measures of central tendency and dispersion. The inferential statistics portion covers statistical rules to compute basic probability, including binomial, normal, Chi-squares, and T-distributions. This course also covers estimation of population parameters, hypothesis testing, linear regression, correlation and ANOVA. Emphasis is placed on applications of technology, using software packages, for statistical analysis and interpretation of statistical values based on data from disciplines including business, social sciences, psychology, life science, health science and education. This course is intended for transfer students interested in statistical analysis.

Students successfully completing this course will be able to:

• Organize qualitative and quantitative data into meaningful charts and graphs.

• Analyze data by comparing and contrasting graphs.

• Evaluate measures of location, central tendency and variation.

• Evaluate probabilities using a variety of computational methods.

• Evaluate probabilities using a variety of distributions.

• Apply the Central Limit Theorem to sampling distributions.

• Use estimation techniques to determine confidence intervals and sample size.

• Perform and analyze hypothesis tests of means and proportions using both one-and two-sample data

• Evaluate correlation to determine the appropriateness of regression models.

• Compute suitable regression models.

1. Given a variety of situations, students will identify the appropriate hypothesis test.

2. Utilize the correct procedure to conduct a hypothesis test and communicate in words the results of a hypothesis test