Department of Mathematics Syllabus
STAT 02.260 - Statistics I


STAT 02.260 - Statistics I - 3 s.h.

Prerequisites: Equivalent of College Algebra

Students learn to use various graphical displays and measures of location and variability to describe data. The course considers elementary probability and sampling distributions, and uses the normal and t-distributions in estimation and hypotheses testing. It includes descriptive techniques for simple linear regression and correlation. Use of a graphing calculator is required; computer software may be used. Students are expected to have completed an equivalent of College Algebra.


The objectives of this course are to familiarize students with basic statistical terminology and tools for describing data sets. Students will also obtain a knowledge of basic concepts in data description, hypothesis testing, statistical inference and obtain a firm basis for further statistical study. Students will be exposed to the importance of the basic assumptions underlying all statistical calculations


1. Introduction

1.1 Brief discussion on the usefulness and relevance of statistics
1.2 Graphical Techniques - bar graphs, pie charts, stemplots, histograms, boxplots, scatterplots.
1.3 Descriptive Statistics - mean, median, standard deviation, interquartile range and five-number summary

2. Basic Probability

2.1 Probability - experiments, sample spaces, events, probability of event, set notation, independent and dependent events, intersections and unions, probability calculations
2.2 Variable description - discrete & continuous random variables
2.3 Expected value for discrete random variables
2.4 Probability distributions for continuous random variables - normal and t distributions

3. Bivariate descriptive statistics

3.1 Interpreting scatterplots and the Pearson coefficient of correlation
3.2 Simple Linear Regression
3.3 Coefficient of determination

4. Sampling and Sampling Distributions

4.1 Importance of appropriate sampling
4.2 Concept of (simple) random sample, sampling distribution of the mean, the Central Limit Theorem

5. Estimation

5.1 Point and interval estimation
5.2 Confidence intervals for a population mean (and t-distributions)
5.3 Confidence internals for a population proportion
5.4 Sample size estimation

6. Hypothesis Testing

6.1 Hypothesis testing methodology
6.2 Hypothesis testing for a population mean and a population proportion


*Peck, Olsen and Devore; Introduction to Statistics and Data Analysis with CD Rom, 3rd edition, Thomson Brooks/Cole, 2008. (Present text)

McClave, James T. and Terry Sincich; Statistics, 10th edition. Pearson/Prentice Hall, Upper Saddle River, N.J., 2006.

Moore, David S., and George P. McCabe; Introduction to the Practice of Statistics, 5th edition. W.H. Freeman, New York. 2006. [Might require another book for Statistics II.]

Weiss, Niel A., Introductory Statistics 7th Ed., Pearson/Addison Wesley, Boston. 2005.