## Statistics 255## Statistical Modeling |

Statistics 255 is a calculus-based exploration of modern probability and statistics, including simulation, the Poisson process, law of large numbers, central limit theorem, exploratory data analysis, statistical models, unbiased estimators, maximum likelihood, confidence intervals, and hypothesis testing. Modern techniques such as non-parametric smoothing and bootstrapping, as well as more traditional normal approximations and large sample methods, all contribute to the construction and analysis of statistical models.

- demonstrate the ability to characterize, analyze and solve exercises in probability and statistics with a style and precision appropriate to the second-year level of university;
- appreciate the central role in statistical modeling of random variables and the characterization, calculation, and approximation of their distributions;
- develop a certain expertise in the interpretation of graphical representations of statistical data, such as histograms, density plots, cumulative distributions, and regression plots;
- appreciate the pervasive influence and contributions of probability and statistics to the natural and social sciences.

These notes

illustrate the use of the R programming language and programming environment for constructing short demos in support of a class on statistical modeling. The examples track discussions in the text

An important component of our course will center on working through a substantial set of exercises in statistics and probability.

- Exercises for Stat 255 TTh Fall 2006 (pdf)

The link (pdf) is to a file which was generated from an Excel spreadsheet. It can be displayed by your web browser or by an application such as Adobe Acrobat.

- Stat 255 TTh Fall 2006 (pdf)