Course Home | Schedule & R Code

Math 3930: Introduction to Statistical Analysis
Spring 2016
9:00-9:50 MWF in RH 249

Darrin Speegle
Contact Info: speegled - at - slu.edu
Office: 139 Ritter Hall
Office Hours: Monday 12:30-1:15pm, Wednesday 1:30-2:15, Thursday 1:30-2:30pm, Friday 2:00-2:30pm


Here is what we have covered this semester. The date is given, together with a reading outline and relevant R code. Homework is listed on the day that it is due. Reading is either from the online textbook Probability OR from the Introductory statistics with R textbook. If it is from the physical book, I preface it with ISWR.

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