Spring Semester 2004

Course Management Aspects

Instructor: Professor Edsel A. Pena

Office: LC Room 200B

Office Hours: 10-12MWF at LC Room 200B

Instructor’s Website: http://www.stat.sc.edu/~pena/

Course Website: http://www.stat.sc.edu/~pena/SCCC312A.htm

Instructor’s E-Mail Address: pena@stat.sc.edu

Graduate Student Grader: Mr. Roland Deutsch

Lecture Textbook: Just the Essentials of Elementary Statistics (3^rd edition), by Robert Johnson and Patricia Kuby. Belmont, CA: Duxbury.

Laboratory Textbook: Statistics Learning By Doing, by John Spurrier, Don Edwards, and Lori Thombs. Long Beach, NY: Whittier.

Computer Packages to Use: Minitab Statistical Package. This is available in the computer labs. You will be assigned a computer account for the semester. We might also have occasion to use other computer packages such as DoStat, and if we decide to be adventurous enough, maybe R.

Attendance Policy: Students are expected to attend class. If important circumstances prevent this, it is the student’s responsibility to find out what was covered in class, what was assigned for reading or homework, and what special announcements (if any) were made. Potentially, most of these information will be in the course website, but this is not guaranteed. The University Bulletin states that

“Absence from more than 10 percent of the scheduled class sessions, whether excused or unexcused, is excessive and the instructor may chose to exact a grade penalty ...”.

Attendance Monitoring System: The following system has the triple purpose of monitoring your attendance, teaching you the process of random sampling and sampling variability, and making me remember and know who you are.

At the beginning of every class meeting (labs and lectures), five names from the class roll will be generated randomly by the computer (Minitab), and only the attendance of the chosen students will be recorded.

Homeworks: Required and due as assigned. No exceptions.

General Advice:

Ask questions, and remember that the only stupid questions are those that are not asked! I sincerely welcome questions and interactions with students, and don’t be shy!
Read the sections of the text to be covered prior to the lecture class or laboratory session.
Attend class every meeting, if possible. The topics are very sequential, and so if you miss a class, the next time you are in class you might find yourself lost!
Attempt to do all assigned homework and writing assignments, and as much as possible, do it by yourself. However, asking others for hints or discussing things are allowed.
Ask me questions in my office, or send me e-mail if you have questions and can’t come to my office.

Grade Components: Lecture Portion = 80%; Laboratory Portion = 20%

Lecture Components: Homework = 25%; Exam 1 = 25%; Exam 2 = 25%; Finals = 25%

Laboratory Components: SAWA = 60%; EWA = 40%

Grading Scale: 90-100 = A; 87-89 = B+; 80-86 = B; 77-79 = C+; 70-76 = C; 67-69 = D+; 60-66 = D; 0-59 = F. The final grade will be based on the value of

Final Score = (.8)(Lecture) + (.2)(Laboratory).

Course Topics

Introduction

Introduction to the Course: Essence, Importance, and Indispensability of Statistical Methods and Probabilistic Thinking.
Basic Elements of Statistics: Variables and their types. Population; Population Parameters. Why do we want to know these parameters? A “bottleneck!” Time and Resource constraints. Samples: surveys and experiments. Sample Statistics. Deductive versus Inductive Inference.

Descriptive Statistics

Graphical presentation of data. Histogram; Bar plots; Pie diagrams; Distributions; Shapes of Distributions.
Numerical measures of location: extremes; mean; median; mode; quartiles; percentiles. Interpretations.
Numerical measures of dispersion or spread: range; variances; standard deviations; mean absolute deviation; inter-quartile range. Boxplots; Comparative boxplots.
Interplay between the mean and standard deviation, and what it could tell us about the percentages of values in intervals: Chebyshev’s Inequality and the Empirical rule.
Bivariate data; scatterplots; and introduction to correlation and regression; measures of association; simple linear regression line.

Probability and Distributions

Introduction to probability, its nature and origins, and its role in science and statistics.
Probability Basics: Random experiments; Sample Spaces; Sample Points; Events; Probabilities of Events; Assigning probabilities. Odds.
Probability Operations: Addition Rule; Multiplication Rule; Mutually Exclusive Events; Independent Events.
Updating Probability: Conditional Probability; Theorem of Total Probability; Bayes Theorem.
Random Variables: why do we need them? Types of random variables: discrete and continuous.
Probability Distributions: as population models; how they arise; finding probabilities from probability distributions. Some distributions for discrete random variables. Bivariate distribution functions.
Mean, Variance, and Standard Deviation of a Random Variable or its Distribution.
The Binomial Distribution. Its Mean and Standard Deviation. Applications of the Binomial Distribution.
Continuous Random Variables: Intrinsic differences between discrete and continuous variables.
Normal Random Variables and their Distributions. Finding probabilities under the normal distribution. Normal approximation to the binomial distribution.

Sampling, Sampling Variability, and Sampling Distributions

Process of simple random sampling: with replacement; without replacement.
Sample statistics. Notion of a sampling distribution of a sample statistic.
What does the sampling distribution tell us? Its uses in prediction and inference.
Sampling distribution of the sample mean.
The Central Limit Theorem and its Applications.
The sampling distribution of the sample variance (chi-square distributions).
The sampling distribution of the T-statistic.

Inferential Statistics

Types of statistical inference: estimation and confidence intervals; hypothesis tests; predictions.
Inferences about the population means: single-sample; two-samples; several samples.
Inferences about the population proportion: single-sample; two-samples.
Inferences about the population variance: single-sample; two-samples.
Contingency Tables and chi-square tests: associations and relationships for categorical random variables. Tests for independence.
Study of relationships: correlation and regression analysis, and inferring about measures of associations for continuous random variables. Doing predictions using the regression function.