Applied Statistics
Revised: November 2006
Course Description
Descriptive statistics, exploratory data analysis, probability distributions, correlation, regression, estimation, and hypothesis testing. Three semester hours.
Objectives
1. Acquaint students with the concepts of population and sample, and some of the
basic descriptive measures used to describe them.
2. Acquaint students with graphical methods for data presentation.
3. Introduce concepts of probability, random variables, and distributions.
4. Examine properties of common distributions, especially the normal and binomial.
5. Introduce the ideas of correlation and regression.
6. Provide an introduction to estimation and hypothesis testing by applying these
procedures to population means and properties.
Text
Roxy Peck, Chris Olsen, and Jay DeVore. Introduction to Statistics and Data Analysis, Second Edition. Duxbury (Brooks Cole/Thompson Learning), 2005.
Grading Procedure
Grading procedures and factors influencing course grade are left to the discretion of individual instructors, subject to general university policy.
Attendance Policy
Attendance policy is left to the discretion of individual instructors, subject to general university policy.
Course Outline
-
Chapter 1: The Role Of Statistics. (3 class days)
Three Reasons to Study Statistics. The Nature and Role of Variability. Statistics and Data Analysis. Types of Data and Some Simple Graphical Displays. -
Chapter 2: The Data Analysis Process And Collecting Data Sensibly. (2 days)
The Data Analysis Process. Sampling. Statistical Studies: Observation and Experimentation.
Note: Sections 2.4 . 2.7 are optional. -
Chapter 3: Graphical Methods For Describing Data. (4 days)
Displaying Categorical Data: Comparative Bar Charts and Pie Charts. Displaying Numerical Data: Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Displaying Bivariate Numerical Data. Communicating and Interpreting the Results of Statistical Analyses. -
Chapter 4: Numerical Methods For Describing Data. (5 days)
Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Variability: Chebyshev's Rule, The Empirical Rule, and z-Scores. Communicating and Interpreting the Results of Statistical Analyses. -
Chapter 5: Summarizing Bivariate Data. (5 days)
Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Communicating and Interpreting the Results of Statistical Analyses.
Note: Section 5.4 is optional. -
Chapter 6: Probability. (4 days)
Chance Experiments and Events. Definition of Probability. Basic Properties of Probability. Conditional Probability. Independence.
Note: Sections 6.6 and 6.7 are optional. -
Chapter 7: Random Variables and Probability Distributions. (7 days)
Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. Mean and Standard Deviation of a Random Variable. The Binomial Distribution. Normal Distributions.
Note: The topic of Geometric Distributions in Section 7.5, and Sections 7.7 . 7.8 are optional. -
Chapter 8: Sampling Variability And Sampling Distributions. (2 days)
Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion. -
Chapter 9: Estimation Using A Single Sample. (3 days)
Point Estimation. A Large Sample Confidence Interval for a Population Proportion. A Confidence Interval for a Population Mean. Communicating and Interpreting the Results of Statistical Analyses. -
Chapter 10: Hypothesis Testing Using A Single Sample. (4 days)
Hypotheses and Test Procedures. Errors in Hypothesis Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypothesis Tests for a Population Mean.
* Note: Most instructors for this course require the use of statistical calculators.