Homework no 4 assignment.
Problem 1. (6.28) The Internal Revenue Service decides on the percentage of income tax
returns to audit for each state by randomly selecting for each state a value from a normal
distribution with a mean equal to 1,55% and a stand
Statistics 13: Lab 6
Instructions: Here is a draft of Lab 6. I copied some of the odds problems from the last lab
since you didnt get a chance to work them for the last midterm. Also, I purposely added fewer
problems for the TAs to do in class since I wan
Statistics 13: Lab 5
LAB PROBLEMS
Detailed solution:
Chebyshevs theorem says
1. (Level 2) Let x be a random draw from the following box:
4
10
2
90
20
P ( 2 x + 2 ) 1
1
.
4
If we simplify the above formula using = 10 and = 5 we
get
P ( 0 x 20 ) 0.75.
you
Lecture 24
Matt Dawson
Review
The p-value
STATISTICS 13
Lecture 24
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
March 11, 2015
Tests for 1 2
Topics for Today
Lecture 24
Matt Dawson
Review
The p-value
Tests for 1 2
Review
The p-value
Te
Lecture 21
Matt Dawson
Review
Dierence in
Population Means
STATISTICS 13
Lecture 21
Condence Intervals
for 1 2
Dierence in
Population
Proportions
Condence Intervals
for p1 p2
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
March 4, 2015
R
Lecture 22
Matt Dawson
Review
Introduction to
Hypothesis Testing
STATISTICS 13
Lecture 22
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
March 6, 2015
Motivating Examples
Components of
Hypothesis Testing
Hypothesis Tests
for
Topics for
Lecture 17
Matt Dawson
Review
The Population
and the Sample
STATISTICS 13
Lecture 17
Denitions and
Sampling
Introduction to
Sampling
Distributions
Examples
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
February 23, 2015
Topics for Today
Lecture 19
Matt Dawson
Review
Other Uses of The
CLT
STATISTICS 13
Lecture 19
Binomial
Approximation
Sampling Distribution
of p
Introduction to
Statistical
Inference
Estimation
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
February 27, 2
Lecture 18
Matt Dawson
Review
More on Sampling
Distributions
STATISTICS 13
Lecture 18
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
February 25, 2015
Sampling Distribution
of the Sample Mean
The Central Limit
Theorem
Topics for Today
Le
Lecture 16
Matt Dawson
Review
STATISTICS 13
Lecture 16
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
February 18, 2015
Applications for
Normal Random
Variables
The Central Limit
Theorem
Where Are We
Headed?
First Version of the
CLT
Topi
Answer Key
Testname: MIDTERM2 FALL14
1)
c
2)
3)
4)
5)
6)
7)
A
D
D
A
A
A
8) B
9) A
10) c
c
11)
12) B
13) B
14) c
15) A
16) c
17) A
18) c
19) D
20) A
21) B
22) D
23)
c
24) D
25) A
A-7
Answer Key
Testname: MIDTERM2 FALL14
1)
2)
3)
4)
5)
6)
7)
D
A
B
A
A
D
A
8
Statistics 13: Elementary Statistics
Lecture 24
June 1, 2015
1/12
Review
I
Hypothesis testing
I
I
I
null and alternative
framework: analogue of a murder trial
Type I and Type II errors
2/12
Two-Sided Test of a Population Mean
I
Data : a sample of size n
Lecture 25
Matt Dawson
Review
Hypothesis Tests
for p
STATISTICS 13
Lecture 25
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
March 13, 2015
Hypothesis Tests
for p1 p2
Beyond This Class
Topics for Today
Lecture 25
Matt Dawson
Review
Hypot
Statistics 13: Elementary Statistics
Lecture 23
May 27, 2015
1/12
Review
I
Estimating dierences between
I
I
I
Means
Proportions
Two-sided and one-sided Condence intervals
2/12
Hypothesis Testing: Introduction
I
Suppose that an economic policymaker wants t
Statistics 13: Elementary Statistics
Lecture 18
May 13, 2015
1/11
Review
I
I
Properties of normal random variables
Use of normal table
I
Inverting normal probability
2/11
Consider the following
I
suppose an investigator wants to know something about a cla
Statistics 13: Elementary Statistics
Lecture 17
May 11, 2015
1/12
Review
I
Continuous random variables
I
I
I
I
Probability density function
Area under the density curve
Cumulative distribution function
Normal random variables
I
I
I
I
Probability density f
Statistics 13: Elementary Statistics
Lecture 10
April 20, 2015
1/11
Review
I
Probability: basic concepts
I
I
experiment, sample space, simple events
events
I
Long run relative frequency view of probability
I
Sample space with equally likely simple events
Statistics 13: Elementary Statistics
Lecture 25
June 3, 2015
1/15
Final Exam
I
Wednesday, June 10 at 6:00 pm. Comprehensive.
I
40 multiple choice questions
I
About 30% from material after midterm 3
I
No practice nal will be given, but sample questions for
Statistics 13: Elementary Statistics
Lecture 1
March 30, 2015
1/6
Material to be Covered
Part I: Summarize data
Summarizing data through graphs and charts
Numerical summary through various averages (measures of center of
the data, spread of the data)
Asso
Statistics 13: Elementary Statistics
Lecture 14
May 1, 2015
1/15
Review
I
Conditional Probability
I
Independence
I
Multiplication rule
2/15
Random Variables
I
A random variable is a mapping from the sample space S to the real
line R.
I
That is, for each s
Statistics 13: Elementary Statistics
Lecture 7
April 13, 2015
1/15
Review
I
z-score
z=
I
x
s
percentiles, Q1 , Q3 and IQR
I
x
boxplots
2/15
Bivariate Data
I
When two variables are measured on a single experimental unit, the
resulting data are called bivar
Lecture 20
Matt Dawson
Review
Error in Point
Estimates
STATISTICS 13
Lecture 20
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
March 2, 2015
Margin of Error
Condence
Intervals
Topics for Today
Lecture 20
Matt Dawson
Review
Error in Point
Lecture 23
Matt Dawson
Review
STATISTICS 13
Lecture 23
Large Sample
Hypothesis Tests
for
The p-value
Examples
Where We Are
Going?
Matt Dawson
University of California, Davis
mwdawson@ucdavis.edu
March 15, 2015
Topics for Today
Lecture 23
Matt Dawson
Revi
Practice Final Problem Set 1 (from TA)
1. Packaging Error Due to a manufacturing error, three cans of regular soda were
accidentally filled with diet soda and placed into a 12-pack. Suppose that two cans
are randomly selected from the case.
(a) Determine
Important:
FOCUS AREAS FOR FINAL:
1) Descriptive data analysis, empirical rule, Tchebyshev rule
2) Probability: law of total probability, Bayes rule
3) Binomial and hypergeometric distribution
4) Normal probability , z scores, calculations
5) One sample l
University of California, Davis
Department of Statistics
Statistics 13: Elementary Statistics
Fall, 2016
Professor David Lang
Contact Information: Email:
Office Hours:
Class:
lang@csus.edu
M 9-10 Student Community Center
MWF 8:00-8:50 GIEDT 1001
Course De
Example (cont.)
p - value = P(| Z |>| 3.03 |)
= P( Z > 3.03) + P( Z < 3.03)
= 2 P( Z < 3.03) = 2(.0012) = .0024
Therefore, it is very unlikely
to observe a test statistic as
extreme as 3.03 or -3.03 if H0
is true.
Practice Midterm 2
THIS IS JUST A SAMPLE OF QUESTIONS THAT WERE EASY FOR ME TO PULL OUT
OF THE TEST BANK IN LAUNCHPAD. I MAKE NO PROMISE THESE QEUSTIONS
COVER ALL OF THE MATERIAL ON THE EXAM.
The exam covers
sections 2.5-2.7, 3.1-3.4, and 5.1,
lectures 4/
STA13 practice final. I will update as I add questions.
I am still working on adding question to this. (Last updated 6/1.)
The usual disclaimer applies. No promises that these are the only types of questions you will see
on the final. The final is cumulat
Answer of Discussion 1 Questions
Lifeng Wei
October 1, 2016
Question 1 The manager of a store conducted a customer survey to determine
why customers shopped at the store. The results are shown in the figure.
What proportion of customers responded that mer
tin. Q: E ci.%).t2.m (3.113
B=i (5.5), (6.935
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Discussion 6
Chapter 6
Name_
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Solve the problem.
1) How many tissues should a package of tissues contain? Researchers have determined that a
person uses