5.2.a
S=[(S,L)(S,T) (S,B) (P,L) (P,T) (P,B) (C,L) (C,T) (C,B)]
5.2.b
Yes, the elementary event would likely be equal because all outcomes are possible.
5.4.a
S=[(H,1) (H,2) (H,3) (H,4) (H,5) (H,6) (T,1) (T,2) (T,3) (T,4) (T,5) (T,6)]
5.4.b
No, the element
Case Study Analysis
One eye on the customer and another on the future vividly captures the business strategy adopted
by Jeff Bezos the CEO of Amazon. Jeff greatest accomplishment is in transforming Amazon
from an online bookstore that sells other stuff to
Frequency data of last names in the US
14
12
12
10
10
8
8
7
7
6
6
4
2
0
Brown
Smith
Miller
Johnson
Frequency
Williams
Jones
Percentage frequency of last names in the U.S
Brown
Smith
Miller
14%
Johnson
Williams
Jones
14%
16%
24%
20%
12%
Last Name
Brown
Smi
Frequency
14
12
12
10
10
8
8
7
7
6
6
4
2
0
Brown
Smith
Miller
Johnson
Frequency
Williams
Jones
Percentage frequency of last names in the U.S
Brown
Smith
Miller
14%
Johnson
Williams
Jones
14%
16%
24%
20%
12%
Last Name
Brown
Smith
Miller
Johnson
Williams
Wi
Last Name
Brown
Smith
Miller
Johnson
Williams
Williams
Johnson
Jones
Miller
Jones
Williams
Jones
Smith
Smith
Miller
Johnson
Smith
Jones
Jones
Johnson
Williams
Smith
Brown
Smith
Johnson
Jones
Smith
Smith
Williams
Brown
Williams
Johnson
Williams
Johnson
Wil
Statistics 601 - Assignment 7
due Wednesday, 26 October 2016
Do not turn in unedited computer output for problems worked on a computer. Cut/paste the
relevant plots and/or tables and include them into your interpretation as if you were writing a
technical
Statistics 601 - Assignment 5
due Monday, 10 October 2016
This assignment involves substantial, if simple, calculus. Pay attention to variables and limits of
integration! You do not need to show every single step of algebra or integration. We assume
you k
Partial Solutions to Assignment 7
Note: These are partial solutions meant to give most of the final results but they do not include those
with answers in the book. Your solutions should involve more detail and/or discussion.
1. (b) H0: 1 = 2 = 0, H1: 1 0
Statistics 601 - Assignment 8
due Friday, 4 November 2016
As always, do not turn in unedited computer output for problems worked on a computer.
Cut/paste the relevant plots and/or tables and include them into your interpretation as if you
were writing a t
Statistics 601 - Assignment 6
due Tuesday, 18 October 2016
Do not turn in unedited computer output for problems worked on a computer. Cut/paste the
relevant plots and/or tables and include them into your interpretation as if you were writing a
technical r
Partial Solutions to Assignment 8
Note: These are partial solutions meant to give most of the final results but they do not include those
with answers in the book. Your solutions should involve more detail and/or discussion.
2. (a) t = 1.19 with 28 df. p-
Partial Solutions to Assignment 6
Note: These are partial solutions meant to give most of the final results but they may not include those
with answers in the book. Your solutions should involve more detail and/or discussion.
1. (a)
1344.1
0.9155 ; (0.8
Partial Solutions to Assignment 5
Note: These are partial solutions meant to give most of the final results but they do not include those
with answers in the book. Your solutions should involve more detail and/or discussion.
For Problems 1-3, it helps to
Partial Solutions to Assignment 9
Note: These are partial solutions meant to give most of the final results but they do not include those
with answers in the book. Your solutions should involve more detail and/or discussion.
1. (a) Yijk = ij+ ijk, = + i +
Statistics 601 - Assignment 9
due Friday, 11 November 2016
As always, do not turn in unedited computer output for problems worked on a computer.
Cut/paste the relevant plots and/or tables and include them into your interpretation as if you
were writing a
HANDOUT #8: GRAPHICAL SUMMARIES OF DATA AND
COMPARISON GRAPHS
TYPES OF GRAPHS
1. Quantile-Reference Distribution Plots
2. Quantile-Quantile Plots
3. Quantile Plots for Mixture Distributions
4. Mixtures of Normal Distributions
5. Comparison Plots: Q-Q plot
HANDOUT #11: INTERVAL ESTIMATORS
I. Condence Intervals (CI) for a Parameter
(a) Pivot Method
(b)
(c)
(d)
(e)
Exact CI
Asymptotic CI
Bootstrap CI
Example of Improper Use of CLThm
(f) Sample Size Determination
(g) Distribution-Free CI for Q(u)
II. Predicti
HANDOUT #13: HYPOTHESES TESTING FOR MULTIPLE POPULATIONS
Tests Comparing K Population/Process Parameters
I. Tests about Dierences in Population Means/Location Parameter
1. Normal distribution with 1 = 2 :
Pooled t-Test
Robust of pooled t-Test
2. Normal
HANDOUT # 1 - INTRODUCTION TO STATISTICS
TOPICS
1. Deniton of Statistics
2. Statistics and the Scientic Method
3. Research Process
4. Why Study Statistics?
5. Some Current Applications of Statistics
6. Preparation of Data
7. Guidelines for a Statistical A
STATISTICS 641 Methods of Statistics, I - Fall 2014
STAT 641 is intended for statistics graduate students who are planning a career as an applied
statistician. The course will provide an introduction to data analysis and a wide-variety of statistical
infe
HANDOUT #3 - Summaries of Population Distributions
TOPICS
1. Denition of Population/Process
2. Denition of Random Variable
3. Types of Random Variables
4. Functions which Characterize Random Variables/Populations
5. Families of Distributions Indexed by Pa
HANDOUT #9: GOODNESS OF FIT AND
BOX-COX TRANSFORMATIONS
I.
GOF for Discrete Distributions
1. Completely Specied Distributions
(a) Chi-square Measure of Fit
(b) Binomial Distribution Example
(c) Poisson Distribution Example
2. Distributions with Unspecied
HANDOUT #12: HYPOTHESES TESTING
I. Principles of Testing
1. Selection of Null and Alternative Hypotheses
2.
3.
4.
5.
Type I and Type II Errors
Power of Test
Level of Signicance
Signicance Probability (p-value)
6. Sample Size Determination
7. Eect Size
8.
HANDOUT #10: SAMPLING DISTRIBUTIONS
1. Sampling Distributions
(a) Denition
(b) Expected Value and Variance of Statistic
(c) MSE and Bias
2. Methods for Determining Sampling Distribution:
(a) Enumeration of All Possible Outcomes: Age of Coin Example
(b) Th
HANDOUT #4: SAMPLE ESTIMATORS OF THE
CDF, PDF, AND QUANTILE FUNCTION
1. Sample Estimator of Cumulative Distribution Function
(a) Raw Estimator
(b) Two versions of Smoothed Estimator
2. Sample Estimator of Quantile Function
(a) Raw Estimator
(b) Many versi
HANDOUT #5: PARAMETRIC SUMMARIES OF
POPULATIONS AND PROCESSES
1. Summaries of Center/Location in a Distribution
(a) Population/Process Mean ()
(b) Population/Process Median ()
(c) Population/Process Quartiles (Q(.25), , Q(.75)
(d) Population/Process Trimm
HANDOUT #7: CENSORED DATA
1. Type I Censoring - Fixed Censoring Time
2. Type II Censoring - Fixed Number of Observed Failures
3. Random Censoring
4. Many other Types of Censoring
5. Form of Censored Data
6. Parametric Estimation When Data is Censored
7. D
HANDOUT #6: NUMERICAL SUMMARIES OF DATA
ESTIMATORS FOR PARAMETRIC FAMILIES
1. Graphical Estimators of Location-Scale Parameters
2. Method of Moment Estimators (MOM)
3. Maximum Likelihood Estimators (MLE)
4. Pdf Based Estimators of Summary Parameters
5. Di
STATISTICS 641 - ASSIGNMENT 3
DUE DATE: Noon (CDT), WEDNESDAY, OCTOBER 1, 2014
Name
Email Address
Please TYPE your name and email address. Often we have diculty in reading the
handwritten names and email addresses. Make this cover sheet the rst page of
yo