

Course

Introduction to probability and the
concepts of estimation and hypothesis testing for use in
experimental, social, and professional sciences. One and twosample
analyses, nonparametric tests, contingency tables,
sample surveys, simple linear regression, various statistical packages
including SAS, SPSS, Minitab, and R. 
Purpose 
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. 
Textbook 
Introduction to the Practice of Statistics (5^{th} edition),
by D.S. Moore and G.P. McCabe, W.H. Freeman and Co., 2006. 
Grading 
Grading (%): A 90; B+ 87; B
80; C+ 77; C 70; D+ 67;
D 60; F
<60;

Software 
One statistical package of your choice:
SAS, SPSS, Minitab, R, or other package

Topics 
Data description and
graphical representation

Probability,
discrete random variables, binomial distribution 
Continuous random
variables, the normal distribution 
Sampling 
Sampling distributions, the
central limit theorem 
Estimation,
confidence intervals 
Hypothesis
testing 
Comparing two treatments or
populations 
Categorical data analysis

Correlation and simple
linear regression 
Twoway tables analysis



