| Date |
Topic |
Reading/R Commands |
R Code |
Homework Due |
| M Jan 11 |
Syllabus Basic rules of probability. |
Sections 1.3.1-1.3.3 |
Syllabus |
|
| W Jan 13 |
Conditional probability and independence |
|
None |
None |
| F Jan 15 |
R as a calculator Bayes' Rule and Law of Total Probability |
Sections 1.4.2 - 1.4.3 |
R Code |
Homework One |
| W Jan 20 |
Introduction to Simulation in R Simulating Probability of an Event |
No Reading |
R Code |
|
| Fr Jan 22 |
Simulation of conditional probability Simulation of Probability of an Event (II) |
No Reading |
R Code |
HW 2 |
| M Jan 25 |
Discrete Random Variables Probability mass functions Using R to estimate expected values |
Sections 3.1.1-3.1.3 |
R Code |
|
| W Jan 27 |
Standard Deviation Binomial and Geometric RV Using R to estimate standard deviation Using R commands binom, geom, pois |
Sections 3.1.5, 3.2.2, 3.2.4 |
R Code |
|
| F Jan 29 |
Poisson RV Using R with Binomial, Geometric and Poisson |
Section 3.1.5 |
R Code |
HW 3 |
| M Feb 1 |
Continuous RVs Density Functions and expected values Using seq and plot in R |
Sections 4.1.1-4.1.2 |
R Code R HTML |
|
| W Feb 3 |
Continuous RVs Uniform, exponential and normal rvs R commands supporting those rvs |
Sections 4.2.1 - 4.2.3 |
R Code R HTML |
|
| F Feb 5 |
Continuous RVs TBD TBD |
TBD |
TBD |
HW Four |
| M Feb 8 |
Correlation Transformation of RV's |
density, lines xlim, ylim, col in plot cor, cov |
R Markdown R Code |
|
| W Feb 10 |
Correlation Sampling Distributions |
xlab, ylab, main legend, matrix, apply |
R Markdown R HTML |
|
| F Feb 12 |
Descriptive Statistics Graphical Rep of Data |
ISWR 4.1-4.2 hist, summary, factor |
R Markdown R HTML |
|
| M Feb 15 |
Descriptive Statistics Graphical Rep of Data Frames |
ISWR 4.2-4.4 qqnorm, boxplot, tapply by, aggregate |
R Markdown R HTML |
|
| W Feb 17 |
Data Frames, factors histograms and boxplots |
ISWR 4.5 qqnorm, boxplot, tapply by, aggregate |
HW Solutions HW Probs |
|
| M Feb 22 |
One Sample T tests Confidence intervals Inline R code |
ISWR 5.1 t.test |
R html R mrkdwn HW6-2 Solutions |
|
| W Feb 24 |
One Sample T tests Confidence intervals Hypothesis Testing |
ISWR 5.1 t.test Sampling from bimodal |
R html R mrkdwn data |
|
| F Feb 26 |
Wilcoxon Rank Sum Test |
ISWR 5.2 wilcox.test, rank, order subset |
R html R mrkdwn |
|
| M Feb 29 |
Exam One |
|
|
|
| W Mar 2 |
Wilcoxon Rank Sum Test 2-sample T test |
ISwR 5.3 wilcox.test, t.test (~) |
R html R Rmd |
|
| F Mar 4 |
Paired T and Wilcoxon |
ISwR 5.3 |
R HTML Rmd |
HW 8 |
| M Mar 14 |
Paired Wilcoxon and prop.test |
ISwR 5.4-5.5 prop.test |
R HTML Rmd |
|
| W Mar 16 |
power of t tests Intro to Regression |
ISwR 6.1 power.t.test |
R HTML Rmd |
|
| F Mar 18 |
Intro to Regression |
ISwR 6.1 lm |
R HTML Rmd |
|
| M Mar 21 |
Intro to Regression |
ISwR 6.1 lm |
R HTML Rmd |
|
| W Mar 23 |
Residual Plots |
ISwR 6.2 plot(my.mod) |
R HTML Rmd |
|
| W Mar 30 |
Prediction and Confidence Bands |
ISwR 6.3 predict, matlines |
R HTML Rmd |
|
| F April 1 |
ANOVA and one-way layout |
ISwR 7.1 anova, pairwise.t.test |
R HTML Rmd |
|
| M April 4 |
On Using shpario to decide whether to use t.test |
No Reading shaprio.test |
R Script No RMD or html |
|
| W April 6 |
Outliers and unequal variances in ANOVA |
ISwR 7.1 oneway.test, kruskal.test |
R HTML Rmd |
|
| W April 13 |
Introduction to modeling |
ISwR 11.1-11.2 lm(v1~., data = ) |
R HTML Rmd |
|
| F April 15 |
More modeling |
ISwR 11.3-12.1 |
R HTML Rmd |
|