Essay on Statistical Inference(lecture a)

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Statistical Inference
Lecture 01a

ANU - RSFAS

Semester 1, 2015

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What is Statistics?

Statistics is the Science of Data or Data Science

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Our Increasingly Quantitative World
The world is becoming quantitative. More and more professions, from the everyday to the exotic, depend on data and numerical reasoning.
Data are not just numbers, but numbers that carry information about a specific setting and need to be interpreted in that setting. With this growth in the use of data comes a growing demand for the services of statisticians, who are experts in
Producing trustworthy data,
Analyzing data to make their meaning clear, and
Drawing practical conclusions from data.

-The American Statistical Association.

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Business
Economics, Engineering,
Marketing,
Computer Science

Health &
Medicine
Genetics, Clinical Trials,
Epidemiology,
Pharmacology

Areas where
STATISTICS
are used

Physical
Sciences
Astronomy,
Chemistry, Physics

Environment
Agriculture,
Ecology, Forestry,
Animal Populations

Government
Census, Law,
National Defense

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“I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”
-Hal Varian, The McKinsey Quarterly, January 2009
Nate Silver (data journalism) - http://www.fivethirtyeight.com
Andrew Gelman (academic blog) - http://andrewgelman.com

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John Tukey (1915 - 2000)

“The best thing about being a statistician is that you get to play in everyone’s backyard.” — J. Tukey
– coined the terms ‘bit’ and ‘software’.
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Backyards that I Play In

Assessing uncertainty in weather predication → Atmospheric Science.
Developing a ‘Health’ index for streams → Environmental Science.
Statistical models for game theoretic data → Political Science,
Economics.
Statistical models for network data → Sociology, Political Science,
Economics, Biology.
Statistical models for relating gene marker data to genetic line data to phenotype data → Biology.

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Course Description

This course introduces students to the theory underlying the development and assessment of statistical techniques in the areas of: point estimation interval estimation hypothesis testing

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Statistical Inference - the course and the notes are broken into the following three sections}
Point estimation
Frequentist (maximum likelihood, method of moments, . . .)
Bayesian
Non-Parametric (frequentist)

Interval estimation
Frequentist
Bayesian
Non-Parametric (frequentist)

Hypothesis testing
Frequentist
Bayesian
Non-Parametric (frequentist)
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Format

Lectures in CBE Bld LT 2
Monday 10:00 - 11:00
Tuesday 1:00 - 2:00
Wednesday 2:00 - 3:00

Tutorial (starting in the second week)
Friday
STAT3013 10:00 - 11:00 (CBE Bld TR3)
STAT8027 1:00 - 2:00 (CBE Bld TR4)

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Texts I

Prescribed Texts
G. Casella and R. Berger
Statistical Inference (second edition)
Brooks/Cole Cengage Learning
Recommended Reading
1. P. Garthwaite, I. Jolliffe and B. Jones
Statistical Inference (second edition)
Oxford University Press
2. G. Givens and J. Hoeting
Computational Statistics (second edition)
Wiley

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Texts II

3. J. Kadane
Principles of Uncertainty http://uncertainty.stat.cmu.edu/wp-content/uploads/2011/ 05/principles-of-uncertainty.pdf
CRC Press
4. G. Parmigiani and L. Inoue
Decision Theory: Principles and Approaches
Wiley

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Texts for Revision

D. Wackerly, W. Mendenhall, and R. Scheaffer Mathematical Statistics with Applications (seventh edition)
Duxbury, Thomson, Brooks/Cole (WMS).
R. Adams and C. Essex
Calculus: A Complete Course (eigth edition)
Pearson

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Assesments

Final Examination (60% or 80%)
Mid-Semester Examination (20% or 0%) (redeemable in favour of the
Final)
Group Presentation/Project (15%)
Weekly Tutorial Solutions (5%)

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Tutorials

Before each tutorial you should submit your answers to the tutorial questions online via Wattle.
These will be graded weekly for
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