Preliminaries Exercises
P.1 For each of the following research questions identify the observational units and variable(s).
a) An article in a 2006 issue of the Journal of Behavioral Decision Making reports on a
study involving 47 undergraduate students at
Statistical Models/Method-1 STAT23400
Lecture 09
Coverage:
I
Review (2010 Midterm Problem)
I
3.3 Higher Moments and mgf
I
3.4 Normal Distribution
Multivariate Continuos Random Variables
I
I
3.7.4 mgf and Independence
Review: Using cdf to Compute Probabili
Statistical Models/Method-1 STAT23400
Lecture 07
Coverage:
I
I
2.6.3 Linear Combination of Random Variable
2.7 More Discrete Random Variable Examples
I
I
I
Hypergeometric Distribution
Poisson Distribution
Chapter Review (homework & pre-class quiz)
Linear
Statistical Models/Method-1 STAT23400
Lecture 2
Coverage:
I 1.2 Univariate Data
I
I
I
1.3 Multivariate Data
I
I
I
1.2.3. More comment on mean and median
1.2.4. Variance and Standard Deviation
Scatter plot
Simpsons paradox and two-way table
Chapter Review.
Statistical Models/Method-1 STAT23400
Lecture 09
Coverage:
I
Transforming Random Variables (cdf, expectation, moment
generation functions)
I
3.4 Normal Distribution
I
3.6 Q-Q plot
Transforming Random Variables
I
Transforming Random Variables
I
CDF method
Statistical Models/Method-1 STAT23400
Lecture 06
Coverage:
I
I
Review of Univariate Discrete Random Variable (handout)
2.6 Joint Distribution of Random Variables
I
I
I
Marginal and Conditional Distribution
Independence of Random Variables
Covariance
I
Man
Statistical Models/Method-1 STAT23400
Lecture 05
Coverage:
I
Review: Discrete Random Variable
I
I
2.4 Hypothesis Tests and p-Values
I
I
I
Bernoulli(p), Bin(n, p), Geo(p), NBinom(s, p)
2.4.1 Binomial Test
2.4.2 Types of Error and Statistical Power
2.5 Disc
Statistical Models/Method-1 STAT23400
Lecture 03
Coverage:
I
Review of Lecture 02
I
2.1 Basic Ideas of Probability
2.2 Counting Formulas
I
I
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I
Multiplication Rule
Number of Permutations
Number of Combinations = Binomial Coefficient
R demonstration
Rev
Statistical Models/Method-1 STAT23400
Lecture 1
Textbook Coverage
I
I
1.1 Data in R
1.2 Summarizing univariate data
I
Graphical Tools :
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I
I
Histogram
Box and Whiskers Plot
Numerical Tools :
I
I
1.2.3 Measures of locations: mean, median, trimmed mean
1.2
Statistical Models/Method-1 STAT23400
Lecture 04
Coverage:
I
Review of L03
2.2 Conditional Probability and Independence
I
Conditional Probability and Bayes Theorem
Independence
2.3 Discrete Random Variables
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I
pmfs and cdfs
Bernoulli, Binomial, Geo
Statistical Models/Method-1 STAT23400
Lecture 08
Coverage: Random Variables
I 3.1 Continuous Random Variables
I
I
I
I
pdf & cdf
Uniform Distribution
Exponential Distribution
3.2 Expected Value, Variance and Standard Deviation
Continuous Random Variables
A
Statistical Models/Method-1 STAT23400
Lecture 13
Recall by methods of moments, most of the estimator can be
expressed as a function of sample mean or sample variance.
Coverage:
I
4.3 Sampling Distribution of Sample Mean
I
4.4 Central Limite Theorem
Review
1.3
Here is a histogram of the distribution of last digits. The majority of remainders are on
the low side (like between 0 and 4) so it is possible that there is some bias or error
introduced in taking self-reported pulse rates. One possibility is that pe
Pruim 1.2.5
(a)
histogram(~ births, xlab="Number of U.S. births each day in 1978")
(b)
xyplot(births ~ dayofyear, xlab="1978 day of year", ylab="Number of
U.S. births each day in 1978")
There seem to be days when there are many fewer births and days when
Statistical Models/Method-1 STAT23400
Lecture 14
Recall by methods of moments, most of the estimators can be
expressed as a function of sample mean or sample variance.
Coverage:
I
4.3 Sampling Distribution of Sample Mean
I
4.6 Sampling Distribution of Sam
Statistical Models/Method-1 STAT23400
Lecture 15
Coverage:
I
4.5 Confidence Interval for the population mean (Variance
Known)
I
4.7 Confidence Interval for the population mean (Variance
Unknown)
I
4.9 Paired Test
Example
I (Pre-Class quiz) A particular ca
Statistical Models/Method-1 STAT23400
Lecture 11
Coverage:
I
3.6 Normal-Quantile plot
I
3.7.4 Linear Combination of Independent r.v.s
Q-Q Plot
I
Q-Q plot is a graphical method for comparing two probability
distributions. In particular, Normal-Quantile plo
A summary of the data analysis process
1. identify population or process
2. design and summarize data collection process
3. determine source of randomness
4. identify parameter of interest (an unknown constant)
5. state hypotheses about the parameter (in
STAT 23400: Spring 2016
Homework #5
Submit Online Friday, April 29 by 11:59pm
Dont panic near midnight! There is a grace period built in. The Canvas submission system stays
open a while after 11:59am in case of submission problems. However, the submission
STAT 23400: Spring 2016
Homework #3
Submit Online Friday, April 15 by 11:59pm
NOTE: The Canvas submission system stays open a bit after 11:59am in case of submission problems right at the due time. However, late homework is not accepted (next day, several
STAT 23400: Spring 2016
Homework #8
Not collected or graded, but ALL relevant to Final Exam
Readings in IPS Textbook:
7.2
8.2
2.6
9.1
9.2
Comparing Two Means
Comparing Two Proportions
Data Analysis for Two-Way Tables
Inference for Two-Way Tables
Goodness
STAT 23400: Spring 2016
Homework #2
Submit Online Friday, April 8 by 11:59pm
Click the HW must be handed in online (at most 2 pdf files) link on the Canvas Syllabus page.
HW #2 Readings = Sections in both IPS* and the Math Supplement:
1.1 Data
1.2 Display
STAT 23400: Spring 2016
Homework #4 (Solutions)
c 2016 Linda Collins.
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