STAT 700
 Applied Statistics I




Roumen Vesselinov, Ph.D.
Office: 200-C LeConte
Office phone: 777-5074
Meeting Times: MW 4:00PM- 5:15PM, LC 210A
Office Hours: Mon 1:30pm-3:30pm & Wed 2:30pm-3:30pm and by appointment.

Bulletin Description: STAT 700—Applied Statistics I (3).
Introduction to probability and the concepts of estimation and hypothesis testing for use in experimental, social, and professional sciences. One and two-sample analyses, nonparametric tests, contingency tables, sample surveys, simple linear regression, various statistical packages including SAS.
Not to be used for M.S. or Ph.D. credit in statistics or mathematics.
Purpose of Course: To provide future scientists in these fields with a base on which to build a continually expanding array of methods for experimental design and data analysis. Students will ideally come away with:
(1) an understanding of basic probability and the manner in which all formal statistical inference depends on it;
(2) the ability to carry out the basic analyses listed in the description using widely available software;
(3) a knowledge of the universal principles underlying all hypothesis testing and interval estimation, thereby
     facilitating interactions with professional statisticians.

All students are expected to:

  • Attend class regularly, asking questions when clarification is needed and participating in any in-class activities
  • Read the material listed on the board before the next class meeting
  • Attempt all of the assigned homework problems and turn them in at the start of the class in which they are due
  • Use the resource of their fellow students and their instructor to seek answers to questions that arise in class, in the readings, and on the homework
Required Text: Introduction to the Practice of Statistics (5th edition),
by D.S. Moore and G.P. McCabe, W.H. Freeman and Co., 2006.
Additional materials are on the course website.
and Calculators:

This course will use the software package SAS. You will have an account on the CSM Windows domain. Currently the computers in LC 124, LC 303A and PSC 102 have SAS. Student copies of SAS for home use are also available for purchase from the university's computer service division.


SAS is the de facto standard statistical package in a number of industries, and experience with SAS is a prerequisite for many jobs in statistics and in fields that use statistics. While we will only scratch the surface of SAS's capabilities, it should provide a useful introduction into the more standard routines, and a jumping off point for future experience with it.

Students can chose a different statistical package, e.g. SPSS, Minitab, R, etc.

A basic calculator may be used on the exams.

Exams and
Topics Covered:

There will be three exams and the final. The topics covered in the exams will generally follow the chapters of the text listed above. However, the exams may also cover material which was solely presented in class, and that is not contained in the text.

The fi
rst exam will be held in class on Monday, October 2. It will focus on the subjects covered by this time: graphical methods, measures of center and variability, basic rules of probability, probability distributions, the binomial random variable and counting rules, the normal distribution, and the normal approximation of the binomial distribution.

The second exam will be held in class on Monday, October 30. It will focus on the material covered after Exam 1, including the central limit theorem, sampling distributions, and estimation and inference for one and two populations for means, variances, and proportions.

The third exam will be held in class on Wednesday, November 29. It will focus on the material covered after Exam 2, including one-way analysis of variance and linear regression.

The final exam will be held at 5:30pm on Wednesday, December 13 in BA 210A. It will be cumulative, covering both the material from the previous three exams and the material on contingency tables.

Make up exams will be given only in extreme circumstances, and only when accompanied by appropriate documentation.

Incidence of cheating and academic dishonesty will be punished to the full extent allowed by university regulations.
Homework and Project:

Homework is due at the beginning of the class period it was assigned for.
Late homework is not accepted

Homework will be assigned at least one week in advance in class, and will also be posted on the class website. If the homework is on a handout, that handout will be available in class and during office hours.

The writing on the homework must be legible, the work used to obtain the answers must be shown and correct, and the final answers must be clearly indicated in order to receive full credit.

Extra points may be deducted for violating any of the following:

  • Multiple pages must be stapled together. No clips.
  • Copies of the SAS code must be included with any homework requiring SAS.
  • Extraneous pages of SAS output should not be turned in.
You MAY work on the homework assignments with other students, but each student must write it up individually. (i.e., no photocopies of another student's work.)

There will also be a project involving collecting and analyzing a data set using the techniques learned in the course. The details of the project are given on the course website.

The grade is determined by the scores on the homework, project  and examinations as follows:

     Homework - 10%
Project  - 10%
Exam 1 - 20%
Exam 2 - 20%
Exam 3- 20%
      Final exam - 20%

with the letter grade determined by the percentage of points obtained.





















Any questions involving the grading of a homework assignment or exam must be raised by the class period following the one in which it was returned.
The homework with the lowest grade will be dropped from the calculations.

There is no "extra credit".

Any deviations from the above grading scheme will be to the benefit of the students.
While there are end of semester evaluation forms, they come far too late to resolve any difficulties experienced in the class. All complaints should be raised by either speaking with me directly, or by anonymously leaving a message in my mailbox in 216 LeConte.
Data description and graphical representation
Probability, discrete random variables, binomial distribution
Continuous random variables, the normal distribution
Sampling distributions, the central limit theorem
Estimation, confidence intervals
Hypothesis testing
Comparing two treatments or populations
Categorical data analysis
Correlation and simple linear regression
Two-way tables analysis

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