# What Was Done in Stat 401B Fall 2015

```What Was Done in Stat 401B Fall 2015
Week
Monday-Lecture
WednesdayLecture
Conditioning,
independence
1
Intro, probability
basics
2
Discrete
distributions
Binomial,
geometric, and
Poisson
distributions
3
Labor Day
Normal, Weibull,
and exponential
distributions
4
Independence, iid
models and xbar
Central Limit
Theorem
xbar examples,
application of
distribution of
xbar to estimating
mu
5
testing, small
sample inference
for mu
6
Small n twosample inference
for a difference in
means, inference
for sigma
Small n inference
for mu, testing
and decision
making,
prediction
intervals
Inference for a
ratio of variances,
inference for a
proportion (or
difference in two
of them)
7
CI for sigma, Cis
for linear
combinations of
means
ANOVA and
testing that all
means are the
same
Thursday-Lab
Friday-Lecture
Lab #1 (Intro to R)
Examples,
counting, intro to
random variables
Uniform and
normal
distributions
Lab #2 (Discrete
distributions and
simulation),
Examples, intro to
continuous r.v.s,
Lab #3
(Continuous
distributions,
simulations, CLT
effect), Intro to
joint distributions
Lab #4
(simulation,
propagation of
error, coverage
probabilities for
large n CIs for
mu), Large n CI's
for mu
Exam 1
Lab #5 (PIs and
Tis, robustness of
CI's for mu, Cis
for p, Chi-square
and F
distributions), QQ and normal
plotting
Lab #6 (One-way
and SLR
analyses), Intro to
SLR, least
squares and
sample
correlation
Conditional
distributions,
independence,
simulation, linear
combinations
Large n
significance
testing for mu
Prediction and
tolerance
Intervals,
inference for a
mean difference
(paired)
One-way normal
model, residuals,
pooled s
R-squared,
residuals and
plotting, normal
SLR model and
inference for
sigma
8
Inference for
beta-, CI's for mu
at a given x, PI's
for next y at a
given x
ANOVA and SLR,
standardized
residuals for SLR,
intro to MLR
9
Overall F,
interpretation of
coefficients and
tests, multicollinearity,
extrapolation
Helps and tools
for model
building, crossvalidation
MLR and R
10
Cross-validation,
logistic regression
11
2-way factorial
analyses, main
effects
2-way factorial
analyses,
interactions and
CIs
12
More 2^ factorials
More 2^p
factorials, Intro to
fractional
factorials
13
Introduction to
modern predictive
analytics, knearest neighbors
Regression tree
predictors
More intro to
modern predictive
analytics
14
15
Neural nets,
&quot;kernel&quot;
smoothing in 1-d
Boostrapping/bag
ging,boosting,
ensembles/stacki
ng, random
forests
Kernel smoothing
and local
regression
smoothing in 1-d,
generalized
Lab #7 (SLR and
MLR analyses),
MLR and least
squares, Rsquared, normal
MLR model,
inference for
sigma
Exam 2
MLR inference for
betas, mean y for
a set of inputs,
prediction limits
for a set of inputs,
ANOVA and
overall F
Lab #8 (Crossvalidation, all
possible Rsquares, logistic
regression, nonlinear regression),
all possible Rsquares, logistic
regression
Lab #9 (Two-way
analyses), use of
dummy variables
and MLR in
factorial analyses
Lab #10 (Factorial
analyses and R,
more dummy
variables and
MLR)
Exam 3
Non-linear
regression
Lab #11(Lasso,
ridge, elastic net,
kNN, trees,
random forests,
ensembles),
Lab #12 (kernel
smoothing and
generalized
Review
random forests
and neural net
predictors
Partial F tests,
model building
generalities and
philosophy and
practice
Factorials with
only 2-level
factors
More discussion
of R analysis of
factorial data
Lasso, ridge, and
elastic net
predictors
Review
```