Print out
& go through these files before class and bring them to class.
List of Topics:
1
Introduction (Probability Theory and Random Variables)
1.1 Basic Probability,
Operation of Sets, Kolmogorov’s Axioms
1.2 Counting Methods
Last part: multiple continuous random variables
etc.
A
gallery of common distributions
2 Data,
Sampling and Basic Statistical Inference
2.1 Data Summary, Graphical and Tabular Representations: part 1, part 2
(a list of
common graphical techniques)
2.2 Sampling from a population-Parameter and Statistics,
sampling results (supplementary handout – T,
F, chi-square distributions)- see also sampling applet here
2.3 Central Limit Theorem
(CLT)
Sampling Handout (CORRECTED)
2.4
Parameter Estimation (Method
of Moments-example)
2.5
Confidence intervals (Another example –
Non-normal-small sample, smallest width C.I)
2.6 Hypothesis Testing
(Tests
for Variance)
2.7 Goodness of Fit Tests,
Learning R, classnotes
from 3rd march …………. More examples of paired t-test
3.1 Simple linear Regression and Inference. (R)
3.2 Multiple Linear Regression (handout) … (R)
3.3
Regression Diagnostics (see class notes, some R-commands)
3.4. Confounding and interaction (another webpage)
3.5 GLM, Logistic and Poisson regression … (R)
4. Basic concepts in experimental
design and ANOVA
4.2 Factorial Designs: intro, details for 2^k factorial expt, another reference
5.1 Introduction, Bernoulli Process, Splitting & Merging
Bernoulli Processes,
5.3
Brownian Motion, Gaussian White Noise
5.2
Different methods for generating from distributions
5.3 Simulating from Special Distributions Another ref.
5.4 Monte Carlo Integration … MCMC (example in Lecture V)… Prof. John Liu’s talk