Large sets of numbers can be daunting, and characterizing them in a few words or numbers can be even more daunting. This course considers how to take data sets--whether large or small--and describe them using a few numbers (descriptive statistics). This, however, is only a small portion of the course.
The majority of the course is dedicated to reaching statistically justified conclusions on the basis of these descriptive statistics. For instance, does the average value of one data set deviate from another in what might be called a "statistically significant" manner? To this end, the course covers cross tabulation of data (including the chi-square test), correlation, linear regression, Student's t-tests, analysis of variance (ANOVA), repeated measures analysis, and factor analysis.
Closing out the course is a brief overview of SPSS, which is a powerful statistics software package that can automatically perform many of the tasks described in the lesson. Although the course does not always provide rigorous mathematical justifications for every aspect of the statistical tools and theory discussed therein, it does provide common-sense explanations of many of these aspects, and it includes numerous real-world examples to illustrate the use of these tools.
Thus, the course teaches students to take sets of data, describe them using a few numbers (including the mean, variance, and skewness), and then reach statistically justifiable conclusions about those data sets. Students should come away from the course with confidence in their ability to tackle basic applied statistics problems and with the fundamental knowledge needed to learn more in-depth statistical theory.
Course Goals:
Learn other types of means, including geometric and power means associated in descriptive statistics
Learn how to represent measures of dispersion and asymmetry
Calculate the variance, standard deviation, and skewness of data sets
Create frequency tables to represent data sets
Represent multivariate data using tables and scatterplots
Create cross tabulations for bivariate data sets
Understand how variance can be used to define a statistic that measures the linear relationship between variables
Understand the fundamental concepts associated with stepwise linear regression
Calculate data values for standardized variables
Calculate standardized regression coefficients using matrix math
Recognize and construct path diagrams
Learn and apply hypothesis testing procedure to the one-sample Student's t-test
Apply the paired two-sample Student's t-test to determine if two samples have statistically different means
Identify the test statistic for one-way ANOVA
Use one-way ANOVA to compare the means of multiple sample groups
Recognize repeated measures designs
Understand the overall purpose and procedures of exploratory and confirmatory factor analysis
Use SPSS to calculate statistical results in some simple cases
Lessons will cover these topics:
Lesson 1 - Descriptive Statistics I
Lesson 2 - Descriptive Statistics II
Lesson 3 - Frequencies
Lesson 4 - Multivariate Data
Lesson 5 - Cross Tabulation I
Lesson 6 - Cross Tabulation II
Lesson 7 - Correlation
Lesson 8 - Linear Regression I
Lesson 9 - Linear Regression II
Lesson 10 - Student's t-Tests I
Lesson 11 - Student's t-Tests II
Lesson 12 - One-Way ANOVA
Lesson 13 - Repeated Measures
Lesson 14 - Factor Analysis
Lesson 15 - SPSS
