Stat20:LecSec2.Spring2014.HowardDAbreraMIDTERMScore:[/60]
NAME:_(AsonBearfacts)SID:_
Signature: _ GSI:_ Section time: _
Instructions: Fill in all applicable answers and give reasons/show work where required.
_
1. A continuous random variable X has a cumul
Introduction
A histogram is a graph that summarizes data.
How to read histograms
A histogram does not need a vertical
scale.
But there is a horizontal scale.
Example: The incomes from 50,000 American families in 1973.
Basic properties
The histogram: a
Summation and Correlation
The correlation coecient r can be written either
1 n (xi x) (yi y )
n i=1
SDx
SDy
n
i=1
1
n
or
xi y i xy
SDx SDy
The proof is as follows:
1 n (xi x) (yi y )
n i=1
SDx
SDy
=
=
=
=
n
1
n SDx SDy
1
SDx SDy
1
n
n
i=1
n
1
=
n SDx SDy
Stat 20: Intro to Probability and Statistics
Lecture 9: Regression Methods
Tessa L. Childers-Day
UC Berkeley
7 July 2014
Todays Goals
The Intuition
The Mechanics
Some Caveats
Simplication
By the end of this lecture.
You will be able to:
Decide if regressi
Stat 20: Intro to Probability and Statistics
Lecture 10: Errors in Regression
Tessa L. Childers-Day
UC Berkeley
8 July 2014
Todays Goals
Why error?
Estimation/Interpretation
Residuals
Strip Methods
By the end of this lecture.
You will be able to:
Decide i
Stat 20: Intro to Probability and Statistics
Lecture 18: Simple Random Sampling
Tessa L. Childers-Day
UC Berkeley
24 July 2014
Recap
Simple Random Samples
EV and SE
Examples
By the end of this lecture.
You will be able to:
Draw box models for real-world s
Stat 20: Intro to Probability and Statistics
Lecture 19: Condence Intervals for Percentages
Tessa L. Childers-Day
UC Berkeley
28 July 2014
Recap
Unknown Box
Condence Intervals
Examples
By the end of this lecture.
You will be able to:
Estimate a 0-1 box mo
Stat 20: Intro to Probability and Statistics
Lecture 21: Intro to Hypothesis Testing
Tessa L. Childers-Day
UC Berkeley
30 July 2014
Recap
Natural Questions
Hypothesis Testing
Example
Recap: From Samples to Boxes
Spent the past 3 days reasoning from a samp
Stat 20: Intro to Probability and Statistics
Lecture 15: Law of Averages
Tessa L. Childers-Day
UC Berkeley
21 July 2014
Exams
Todays Goals
Recap
Law of Averages
Box Models
Exam Performance
In general, scores were about typical
Total Possible Points = 70
M
Stat 20: Intro to Probability and Statistics
Lecture 17: Using the Normal Curve with Box Models
Tessa L. Childers-Day
UC Berkeley
23 July 2014
Todays Goals
Probability Histograms
Probability Histogram Normal Curve
Central Limit Theorem
By the end of this
Stat 20: Intro to Probability and Statistics
Lecture 16: More Box Models
Tessa L. Childers-Day
UC Berkeley
22 July 2014
Todays Goals
EV and SE
Normal Curve
Classifying and Counting
By the end of this lecture.
You will be able to:
Determine what we expect
Stat 20: Intro to Probability and Statistics
Lecture 25: Pitfalls and Limits In Testing
Tessa L. Childers-Day
UC Berkeley
6 August 2014
Recap
Interpreting Signicance
Data Snooping
Role of Model
Questions Matter
Outline
1 Recap
2 Interpreting Signicance
3
Stat 20: Intro to Probability and Statistics
Lecture 23: Two Sample Testing
Tessa L. Childers-Day
UC Berkeley
4 August 2014
Recap
Surveys
Experiments
Recap: Hypothesis Testing
Steps in Hypothesis Testing:
1
State the hypotheses
Null: The dierence between
Stat 20: Intro to Probability and Statistics
Lecture 4: Data Displays (cont.)
Tessa L. Childers-Day
UC Berkeley
26 June 2014
Todays Goals
Recap
Displaying Quantitative Data
By the end of this lecture.
You will be able to:
Comprehend displays of quantitati
Stats 20 Introduction to Probability and Statistics Lecture 1 Notes
Example 1: New Antiperspirant
(Hypothetical) A new women's antiperspirant has been developed. We are interested in
determining if it actually impedes perspiration. How do we do this?
Use
Exam 1
Instructor: Tessa Childers-Day
Stat 20
Please write your name and student ID below, and circle your section. With your
signature, you certify that you have not observed poor or dishonest conduct on the part of
your classmates. You also certify that
Final Exam
Instructor: Tessa Childers-Day
Stat 20
9 August 2012
Please write your name and student ID below, and circle your section. With your
signature, you certify that you have not observed poor or dishonest conduct on the part of
your classmates. You
Final Exam
Instructor: Tessa Childers-Day
Stat 20
15 August 2013
Please write your name and student ID below, and circle your section. With your
signature, you certify that you have not observed poor or dishonest conduct on the part of
your classmates. Yo
Midterm Exam
Instructor: Tessa Childers-Day
Stat 20
1 May 2014
Please write your name and student ID below, and circle your section. With your
signature, you certify that you have not observed poor or dishonest conduct on the part of
your classmates. You
Midterm Exam
Instructor: Tessa Childers-Day
Stat 20
11 March 2014
Please write your name and student ID below, and circle your section. With your
signature, you certify that you have not observed poor or dishonest conduct on the part of
your classmates. Y
Midterm Exam
Instructor: Tessa Childers-Day
Stat 20
18 July 2013
Please write your name and student ID below, and circle your section. With your
signature, you certify that you have not observed poor or dishonest conduct on the part of
your classmates. Yo
Midterm Exam
Instructor: Tessa Childers-Day
Stat 20
12 July 2012
Please write your name and student ID below, and circle your section. With your
signature, you certify that you have not observed poor or dishonest conduct on the part of
your classmates. Yo
Stat 20: Intro to Probability and Statistics
Lecture 7: Measurement Error
Tessa L. Childers-Day
UC Berkeley
2 July 2014
Todays Goals
Repeated Measurements
Outliers
Errors
By the end of this lecture.
You will be able to:
Explain why we measure repeatedly
C
Stat 20: Intro to Probability and Statistics
Lecture 8: Bivariate Data and Correlation
Tessa L. Childers-Day
UC Berkeley
3 July 2014
Todays Goals
Summary Statistics
Association
Correlation
Properties
By the end of this lecture.
You will be able to:
Constr
Stats 20 Introduction to Probability and Statistics Lecture 3 Notes
Example: High School Hobbies
(Hypothetical:) Imagine we are interested in exploring what kindsof hobbies high school students
have below. We construct a survey:
(1) Circle your gender: Fe
Stat 20: Intro to Probability and Statistics
Lecture 14: Exam 1 Review
Tessa L. Childers-Day
UC Berkeley
15 July 2014
Exam 1 Guidelines
Material Covered
Q&A
Details
Wednesday, 16 July 2014
In lecture, this room
Lasts 80 minutes (9:10am to 10:30am)
Worth 3
Hank Ibser Statistics 20 Summer 2015
Study Guide for the Midterm
The midterm (Friday July 17 in lecture) will cover the following
chapters with certain parts omitted:
Ch 1—2: Concepts and terminology in these chapters will be included as
relevant to Ch 12
Syllabus, Statistics 20, Summer 2015
Instructor: Hank Ibser
Lectures: M-F 11-12, 101 LSA
Email: hankibser@berkeley.edu
Oce Hours: Mon 2:10-3:30 and Fri 12:10-1:30, 349 Evans Hall. Ill be coming from lecture so I
might sometimes be a bit later but Ill be t
Summation, Average, and Standard Deviation
Denitions and Formulas
Lists of numbers are often written as xi . Each number on the list is one of
these xi values. Thus the list 1, 4, 6 would have x1 = 1, x2 = 4, and x3 = 6.
Here are a few formulas we will us
Company
Morningstar Rating Business Risk
Ameren Corp
3 Star
Average
Apple Inc
4 Star
Average
At&T Inc
2 Star
Average
Bank of New York Mellon Corp
4 Star
Above Average
BlackRock Inc
4 Star
Average
Buckeye Partners
4 Star
Below Average
Cardinal Health Inc
3
Total payroll in thousands:
Phillies
Dodgers
Rays
Red Sox
$96,870
$136,373
$42,334
$120,460
Median Salary in thousands:
Phillies
Dodgers
Rays
Red Sox
$ 3,459.64
$ 4,870.46
$1,511.93
$4,302.14
Five number summary:
Phillie
s
Dodgers
Rays
Red Sox
390
390
390
Midterm(1practice(test(questions:(Part(1(of(2)(Chapters(1:6(
Chapter(1,2:(
1)(
Read the scenario below and identify the following:
1. Is it a randomized, controlled experiment? Explain.
2. What are the possible confounders, if any? (List 1-2)
3. What conc