Statistics
(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 ztests, ttests, correlation,
and chisquare, 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
realworld 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/Topics
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 stemandleaf
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 ztable
• Using zscores 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 pvalues
• The difference between Type 1 and
Type 2 errors
Module
7: Hypothesis Testing: One Sample for the
Mean
• Conducting onesample hypothesis
tests for the mean when the variance is
known/not known
• Reading ttables
• Recognizing when each kind of test
is appropriate
Module 8: Hypothesis Testing for Means:
Two Independent Samples
• Recognizing when an independentsamples
ttest is appropriate
• The assumptions underlying the independentsamples
ttest
• Pooling the variance of two independent
samples
• Conducting an independentsamples
ttest with pooled variance estimate
Module
9: Hypothesis Testing. Two Dependent Samples
• Recognizing the difference between
independent and dependent samples
• Calculating a dependent samples
ttest
Module10:
Correlation
• Differentiating between z and ttests
and correlation analyses
• Constructing scatterplots
• Calculating Pearson's productmoment
correlation
• Differentiating between a negative
and a positive correlation
• Recognizing the limitations of correlation
analyses
Module
11: ChiSquare
• Recognizing when a chisquare test
is appropriate
• Calculating and interpreting a chisquare
test of independence
• Calculating and interpreting a chisquare
goodnessoffit 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
