**Statistics for Engineers and Life
Scientists**

**NCSU courses ST311, ST361 and ST371**

Over the
past couple of decades there has been unprecedented
efforts to obtain data in almost all fields of sciences and engineering. This
requires that we provide training of basic statistical and applied mathematical
methods that are necessary for handling such vast amounts of data that would
enable any practitioners to extract `signals’ by separating the `noises’ from
vast amount of data. Knowledge of modern data science methods equipped with
computational techniques are becoming necessary tools for any scientists
ranging from astronomy to zoology. Statistical methods provide systematic
approaches for describing and interpreting information so that we can make the
most informed decisions in practice. The proposed course aims to equip students
with basic ideas and necessary computational tools in statistics, which range
from systematic data collection to making probabilistic inference and
decisions. The course will begin with a collection of introductory concepts and
commonly used methods in statistics, illustrate the use of modern computational
approaches to statistics by introducing the use a commonly used software for
modern data science. Students will be working in teams to exploring ideas on
data examples and case studies using the statistical software R. The course may
not have time to cover all theoretical underpinnings and advanced mathematical
tools but the students would have access to a multitude of online materials to
gain further insights to understand the basic technical methods of mathematical
statistics. The course will cover the following topics in class:

1. Basic
Probability Theory (brief review)

2. Data
Collection and Visualization (using R)

3. Sampling
Distributions (using Monte Carlo methods)

4. Point and
Interval Estimation Methods

5. Statistical
Hypothesis testing

6.
Regression Methods (multiple linear regression, logistic regression and Poisson
regression)

7.
Statistical Model diagnostics, Predictive inference and Goodness-of-fit methods

8. Optional
Topics: Maximum likelihood principle and Elementary Nonparametric methods

Prerequisite:
Students are expected to be fluent with elementary level quantitative
probabilistic reasoning and analysis (for example, conditional probability,
independence of events, random variables, univariate and multivariate
probability distributions, correlation and covariance). Some of the course
assignments will involve coding and statistical analysis on datasets provided.
It is recommended that students familiarize themselves
with the use R for this purpose using online resources provided in class.