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.

Get started with R. R is a language and environment for data analysis, and available as free software under the terms of the Free Software Foundation's GNU General Public License. You can download and immediately use 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, $ 8^4 \times 12^3$ can be performed by typing

> 8^4 * 12^3

R Studio. On top of R you need R Studio, which provides an integrated development environment (IDE) for R. Follow their instruction to download and install DESKTOP R STUDIO in your PC.

idemo. Alternative to R Studio, you may download R code idemo.R, and run it by executing

> source(file.choose())
and choosing the file idemo.R from folders at the beginning of R session. It allows us to run R code interactively line by line. You can now start it with
> idemo()
and select R code of your choice from folders.

MATH 3070 Recommended reading Review materials
1 Section 1.1-1.4 Chapter 1 (Rev.01/22/18)
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.
2 Section 2.1-2.3 Chapter 2 (Rev.09/15/17)
Data sets: chirps.csv, stearicacid.txt, pizza.csv, detect.csv, realtor.csv, biomass.csv
R code examples: chirps.R, stearicacid.R, pizza.R, detect.R, realtor.R, biomass.R
3 Section 3.1-3.6 Chapter 3 (Revised on 02/15/2017)
Data sets: salmon.txt, antibio.txt, stain.csv, WORDS-GRPD.csv, tartar.txt
R code examples: salmon03.R, antibio03.R, stain.R, WORDS-GRPD03.R, tartar.R(Rev.09/28/17)
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).
4 Appendix C.2 Normal Distribution Table
5 Section 5.3-5.4 Chapter 5 (Revised on 03/23/2018)
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.
5 Appendix C.3 (Revised) t-Distribution Table
6 Section 6.1-6.5 Chapter 6 (Revised on 04/03/2018)
Examples with R (Revised on 04/15/2017)
Data sets: mileage.csv, sucrase.csv, salmon.txt, hormone1.csv, brick.txt, heart.csv, nerve.txt, time.csv, and hearing.txt.
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.

MATH 3080 Recommended reading Review materials
6 Section 6.1-6.5 Chapter 6 review (Revised on 01/22/2018)
Examples with R (Revised on 04/15/2017)
Article: Are women really more talkative than men?
Data sets: brick.txt, heart.csv, nerve.txt, time.csv, and hearing.txt, WORDS.csv, salmon.txt, bloodcell.txt
R code examples: brick.R, heart.R, nerve.R, time.R, and hearing.R, WORDS-test.R, salmon-test.R, bloodcell.R
7 (2,3) Section 7.1-7.2, 5.3 and 6.3 Chapter 7 (Revised on 09/29/2017)
Data sets: stearicacid.txt, duckweed.txt, chloro.txt, detect.csv, agefat.txt, cancer2.txt, salmon.txt, antibio.txt, cuckoo.txt, pillbug.txt, soap.csv
R code examples: stearicacid07.R, duckweed.R, chloro.R, detect07.R, agefat.R, cancer2.R, salmon07.R, antibio07.R, cuckoo.R, pillbug.R, soap.R
8 Section 8.1-8.2 Chapter 8 (Revised on 10/10/2017)
Data sets: cherry.txt, paperstr.txt, cucumber.txt, pork.txt, WORDS-GRPD.csv bodyfat.csv, ratliver.csv, and ratweight.csv, feed.csv, dhl.csv
R code examples: cherry.R, paperstr.R, cucumber.R, pork.R, WORDS-GRPD-lm.R bodyfat.R, ratliver.R, and ratweight.R, feed.R, dhl.R(dhl.R has just been revised on 10/30/2017)
(11) 12 Section 11.3 and 12.1-12.2 Chapter 11
Chapter 12
Data sets: mendel.csv, avadex.csv, urinary1.csv, drugtrial.csv, malaria-strain.csv, and family.csv.
R code examples: mendel.R, avadex.R, urinary1.R, drugtrial.R, malaria-strain.R, and family.R.
13 Section 13.1-13.4 Chapter 13
Supplementary lecture note (Rev.04/11/18)
Data sets: avadex.csv, mothsmale.csv, urinary2.csv, coalminers.csv, moths.csv, and birthwt.csv.
R code examples: avadex-logit.R, mothsmale.R, urinary2.R, coalminers.R, moths.R, and birthwt.R.

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

MATH 3070 Statistical Methods I

MATH 3080 Statistical Methods II

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


© TTU Mathematics