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
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: 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
Lecture 16
Matt Dawson
Review
STATISTICS 13
Lecture 16
Matt Dawson
University of California, Davis
[email protected]
February 18, 2015
Applications for
Normal Random
Variables
The Central Limit
Theorem
Where Are We
Headed?
First Version of the
CLT
Topi
Final
Files to submit: Makefile, all necessary .c and .h files need to compile your program, Readme.txt
All programs must compile without warnings when using the -Wall option
If you are working in a group ALL members must submit the assignment on SmartSit
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
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
Lecture 2
1
Chapter 3
1.1
Learning Objectives of Chapter 3
3.1 What do each of these terms mean: Probability, Event, Experiment, Sample Space?
3.2 Learning to count: Tree diagrams and N
n .
3.3 Different kinds of events: Sure, Impossible, Complementary, M
Econ 1A: Principles of Microeconomics
Spring 2017
University of California, Davis
Instructor: Paul Lombardi, PhD
Email: [email protected]
Class Location and Time: Sciences Lecture Hall 123, TR 1:40 3:00 pm
Course Website: UC Davis Canvas
Office: 145 S
University of California, Davis
Department of Statistics
Statistics 13: Elementary Statistics
Fall, 2016
Professor David Lang
Contact Information: Email:
Office Hours:
Class:
[email protected]
M 9-10 Student Community Center
MWF 8:00-8:50 GIEDT 1001
Course De
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Lecture 18
Matt Dawson
Review
More on Sampling
Distributions
STATISTICS 13
Lecture 18
Matt Dawson
University of California, Davis
[email protected]
February 25, 2015
Sampling Distribution
of the Sample Mean
The Central Limit
Theorem
Topics for Today
Le
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
[email protected]
February 27, 2
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 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
[email protected]
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
[email protected]
March 15, 2015
Topics for Today
Lecture 23
Matt Dawson
Revi
Lecture 25
Matt Dawson
Review
Hypothesis Tests
for p
STATISTICS 13
Lecture 25
Matt Dawson
University of California, Davis
[email protected]
March 13, 2015
Hypothesis Tests
for p1 p2
Beyond This Class
Topics for Today
Lecture 25
Matt Dawson
Review
Hypot
Lecture 24
Matt Dawson
Review
The p-value
STATISTICS 13
Lecture 24
Matt Dawson
University of California, Davis
[email protected]
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
[email protected]
March 4, 2015
R
Lecture 22
Matt Dawson
Review
Introduction to
Hypothesis Testing
STATISTICS 13
Lecture 22
Matt Dawson
University of California, Davis
[email protected]
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
[email protected]
February 23, 2015
Topics for Today
Wine
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Example
The daily yield for a chemical plant has averaged
880 tons for several years. The quality control
manager wants to know if this average has
changed. She randomly selects 50 days and
records an average yield of 871 tons with a
standard deviation of
UWP 23
GICW #7APA & Paper 2
Directions: Complete the sections as you are prompted to. Please do not work ahead. Once you
have finished all of the activities, save your work and upload it to GICW #7 in Canvas
Assignments before you leave class.
APA Citatio
Homework 7
Files to submit: bin_str.c
Time it took Matthew to Complete: 10 mins
All programs must compile without warnings when using the -Wall and -Werror options
Submit only the files requested
Do NOT submit folders or compressed files such as .zip, .r
Homework 7
Files to submit: foo.c
Time it took Matthew to Complete: 10 mins
All programs must compile without warnings when using the -Wall and -Werror options
Submit only the files requested
Do NOT submit folders or compressed files such as .zip, .rar,
9. The effects of drugs and alcohol on the nervous system has been the subject
of considerable research. Suppose a research neurobiologist is testing the
effect of a drug on response time by injecting 100 rats with a unit dose of
the drug, subjecting each