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(CSC 240 3.0 Cr)

Arts & Sciences

Course Description

Statistics is a vital part of the scientific process, but it is often misused and misunderstood. This course presents the concepts and skills that students will need to successfully use and interpret statistical analyses. Course topics include when and how to use statistics appropriately, summarizing and presenting data, the assumptions that underlie statistical analyses, several statistical tests including z-tests, t-tests, correlation, and chi-square, and how to recognize when each kind of statistical test is appropriate.

Learning Objectives
After completing this course, students will be able to:

• Summarize and present different kinds of data
• Recognize when different statistical analyses are appropriate for addressing real-world questions
• Conduct several basic statistical analyses, both by hand and by using a software application

Breadth of Assignments
This course relies on a variety of assignments to adequately explore the topic of Statistics.
Students are expected to keep up with textbook and online reading assignments. Individual assignments involve case studies, review questions, and writing assignments used to test and challenge the concepts of the course. Each module contains a collaborative assignment whereby students can help one another with the more challenging concepts of the course.

Required Resources
Blaisdell, Ernest A. (1998) Statistics in Practice (2nd ed.). Brooks/Cole
Publishing. ISBN 0030271142 (bundled with MINITAB software: ISBN 0030193745)

Introduction to Statistics
Arts & Sciences

Module 1: Introduction to Data Types and Statistical Measures
• The importance of statistics as a tool
• Differentiating between populations and samples
• Identifying examples of the four scales of measurement
• Identifying examples of different types of variables
• Differentiating between a parameter and a statistic

Module 2: Organizing and Describing Data: Graphical Methods
• Identifying the appropriate graphical or tabular method for presenting data
• Frequency distributions
• Creating meaningful graphs including frequency polygons, bar graphs, and stem-and-leaf plots

Module 3: Describing Data: Measures of Central Tendency and Variation
• Computing the mode, median, and mean of a dataset
• Recognizing when each of these three is appropriate
• Computing the variance and standard deviation of a dataset
• Variance and standard deviation

Module 4: The Normal Distribution
• Understanding the properties of the normal distribution
• Reading a z-table
• Using z-scores in conjunction with the normal distribution

Module 5: Sampling Procedures and Sampling Distributions
• Randomly selecting a sample from a population
• Creating a sampling distribution of the mean
• Calculating the mean and measure of variance for the sampling distribution of the mean
• Applying the central limit theorem to statistical analyses

Module 6: Hypothesis Testing: The Logic of Hypothesis Testing and Its Role in Science
• Writing a hypothesis using statistical notation
• The logic of inferential statistics
• The region of rejection, alpha values, and p-values
• The difference between Type 1 and Type 2 errors

Module 7: Hypothesis Testing: One Sample for the Mean
• Conducting one-sample hypothesis tests for the mean when the variance is known/not known
• Reading t-tables
• Recognizing when each kind of test is appropriate

Module 8: Hypothesis Testing for Means: Two Independent Samples

• Recognizing when an independent-samples t-test is appropriate
• The assumptions underlying the independent-samples t-test
• Pooling the variance of two independent samples
• Conducting an independent-samples t-test with pooled variance estimate

Module 9: Hypothesis Testing. Two Dependent Samples
• Recognizing the difference between independent and dependent samples
• Calculating a dependent samples t-test

Module10: Correlation
• Differentiating between z and t-tests and correlation analyses
• Constructing scatter-plots
• Calculating Pearson's product-moment correlation
• Differentiating between a negative and a positive correlation
• Recognizing the limitations of correlation analyses

Module 11: Chi-Square
• Recognizing when a chi-square test is appropriate
• Calculating and interpreting a chi-square test of independence
• Calculating and interpreting a chi-square goodness-of-fit statistic

Module 12: Comparing Different Statistical Methods: Determining When Each Is Appropriate
• Recognizing when each of the six statistical analyses covered in this course is appropriate
• Recognizing when you need analyses other than the six covered in this course

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