e-Statistics

# Introduction

Java in the browser. This web site is designed for an introductory statistical method course, and interactive functionality is designed for students to begin their own statistical investigation immediately. In order to activate all the functionality in this site, you need to install Java Runtime Environment.
1. If you are windows or linux users, you should be able to follow Java Downloads, and find it under Java Archive section at the bottom. Or download jre-6u45-windows-i586.exe from here for windows.
2. If you use Apple computers you might have to choose the most recent Java 7 or 8 (because no Java 6 for Mac OS), for which it may be much harder to adjust all the security settings.
Once successfully installed, you might have to get 'Security Level' lowered and/or edit the 'Exception Site List' at 'Control Panel' (or 'Java Control Panel') to include http://math.tntech.edu/e-stat/.

Course contents. It will be posted here along with guidelines for course material, accompanied by recommended reading based on

Introduction to Statistical Data Analysis for the Life Sciences by Claus Thorn Ekstrom and Helle Sorensen
The set of assignment worksheets will become a substantial portfolio in this course, and should be complete and organized well for your own future reference. Assignment due dates will be announced at iLearn, and your work must be submitted to an appropriate dropbox before the deadline.

 MATH 3070 Recommended reading Review materials 1 Section 1.1-1.4 Chapter 1 Assignment No.1 Report example: Problem 1 report Data sets: FHEALTH.csv, cooling.txt, strength.csv, tree.csv, diesel.csv. R code examples: FHEALTH.R, cooling.R, strength.R, tree.R, diesel.R. Answers to Quiz 1 2 Section 2.1-2.3 Chapter 2 (Rev.09/15/17) Assignment No.2 Report example: Problem 1 report Data sets: chirps.csv, stearicacid.txt, pizza.csv, realtor.csv, biomass.csv R code examples: chirps.R, stearicacid.R, pizza.R, realtor.R, biomass.R Answers to Quiz 2 3 Section 3.1-3.6 Chapter 3 (Revised on 02/15/2017) Assignment No.3 Report example: Problem 1 report Data sets: salmon.txt, antibio.txt, WORDS-GRPD.csv, stain.csv, strength.csv, tartar.txt R code examples: salmon03.R, antibio03.R, WORDS-GRPD.R, stain.R, strength-aov.R (Rev.09/28/17), tartar.R(Rev.09/28/17) Answers to Quiz 3 4 Section 4.1-4.4 Chapter 4 (Revised on 03/03/2017) Data sets: crabs.csv R code examples: crabs.R Practice Problems in preparation for quiz & test (Rev.10/04/17). Answers to Quiz 4 4 Appendix C.2 Normal Distribution Table 1-4 Test I Answer to Test I 5 Section 5.3-5.4 Chapter 5 (Revised on 03/23/2017) Assignment No.4 Report example: Problem 1 report Data sets: crabs.csv, stearicacid.txt, mileage.csv, realtor.csv, salmon.txt. R code examples: crabs05.R, stearicacid-conf.R, mileage.R, realtor-conf.R, salmon05.R. Answers to Quiz 5 5 Appendix C.3 (Revised) t-Distribution Table 6 Section 6.1-6.5 Chapter 6 (Revised on 03/31/2017) Examples with R (Revised on 04/15/2017) Assignment No.5 Sample report for Problem 1: Sample writing, Data sets: mileage.csv, sucrase.csv, salmon.txt, hormone1.csv, brick.txt, heart.csv, nerve.txt, time.csv, and hearing.csv. R code examples: mileage-test.R, sucrase-test.R, salmon-test.R, hormone1.R, brick.R, heart.R, nerve.R, time.R, and hearing.R. 1-6 Final Project Read the instruction in Final Project, and produce a statistical report on the study of your own choice.

Get started with R. R is a language and environment for statistical computing and graphics, which is similar to S-Plus. It is available as Free Software under the terms of the Free Software Foundation's GNU General Public License. The system runs on Windows, Linux, Mac. You can download it from CRAN R project which has software packages for Unix, Linux, Windows, and Mac. R is a programming language, and runs a command'' in an interactive manner, known as interpretor.'' Each command is requested in a form of function'': For example, it is the function q() to quit the program.

> q()


Use it as a calculator. We can execute arithmetic operations at the prompt. For example, can be performed by typing

> 8^4 * 12^3


R Studio. On top of R you may want to use R Studio. It is an integrated development environment (IDE) for R. Follow their instruction to download and install DESKTOP R STUDIO in your PC.

idemo. You may want to download R code idemo.R into your machine. It defines the function idemo() by executing

> source(file.choose())

and choosing the file idemo.R from folders. It runs R code interactively line by line. You can start it with
> idemo()

and select an R code file from folders.

Syllabi. Other relevant information for Statistical Methods I and II can be found at

MATH 3070 Statistical Methods I

MATH 3080 Statistical Methods II

The series of webcast explains basic operations of statistics (exploratory graphics and descriptive statistics) using this web-based interactive environment. It may take a few seconds (a few minutes outside TTU) to download the webcast content.

 Title Summary of presentation Webcast Introduction Introduce e-Statistics Data in statistical studies Learn how to use data in e-statistics Preparing histogram from data Learn how to prepare the histogram from data Characterizing histogram Learn how to describe the shape of histogram Stem and leaf plot Learn about stem and leaf plot Measures of center Learn the measures of center, mean and median, and discuss their properties Measures of variability Learn the measure of variability, standard deviation, and coefficient of variation, and discuss empirical rules Quartiles and boxplot Introduce another measure of variability, quartles, interquartile range, and learn how to use a boxplot as a schematic presentation of data Grouped data Learn how to explore the grouped data graphically in boxplot and numerically in summary statistics Frequency Function Learn random variable, frequency function, and the concept of probability distribution Binomial Distribution Learn about binomial experiment and binomial distribution Normal Density Function Learn the concept of probability distribution and normal density function Normal Distribution and Critical Point Learn how to calculate the probability from the normal density function, discuss the empirical rules, and introduce the concept of critical point Z-score Explain the idea of z-socre, and learn how to use it Central Limit Theorem Explore the idea of central limit theorem and its effect Estimating the population mean Introducing the estimation of mean and the confidence interval of mean.