Prepared by Anshuman Tiwari
Statistics 20
Midterm Review Exercises part 2 Solutions
For questions 1 through 3, show your work. I roll three six-sided, fair dice. What is the
probability that:
1. All three dice show the same number (such as 4, 4, 4 or 2, 2
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
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
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
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
Solutions to Review Problems
Chapter 3:
Review Exercises 6, 7, 8, 9.
6. Lists (i) and (ii) have this histogram but list (iii) does not this is because
lists (i) and (ii) both have 25% of the people between 66.5 and 67.5 inches,
50% of the people between 6
Statistics 20 Final Study Guide Hank Ibser
The final will cover the following chapters as specified below:
Ch 1: All.
Ch 2: Omit section 4; in Ch 1—2 you need to know the ideas and jargon
but not the specific examples.
Ch 3: Omit sections 5*7.
Ch 4: Omit
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
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
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
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 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 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 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 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 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 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
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
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
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
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
7 FALL 1006:
STAT 2 : Sec 2
Course mark
before Final
(60)
Highest Mark
90th Percentile
80th Percentile
70th Percentile
60th Percentile
Median
40th Percentile
30th Percentile
20th Percentile
10th Percentile
Lowest Mark
PNE = 43'5 2
Prepared by Anshuman Tiwari
Statistics 20
Midterm Review Questions 2
For questions 1 through 3, show your work. I roll three six-sided, fair dice. What is the
probability that:
1. All three dice show the same number (such as 4, 4, 4 or 2, 2, 2)?
2. All th
_ 1T 20: Spring 2015: QUIZ 2: My GSI is:
aime: SID:
time:
ore: I30
i Ql - Q10: (10 marks in total: 1 mark for each correct option on Scantron sheet)
Inn-=-
II-
-II
-IIII
Fill in the values in the four cells
D8, 09, F6 and H6 and any
+1 for
box
model
+1 for
box
model
+1 for correct answer
+1 for box model
+1 for
alternative
hypothesis
()
The answer is (c) The result is statistically
significant.
STAT 20: Calculus Diagnostic Quiz held in Week 3
Solutions anti wac
Q1 . A continuous random variable X has a cumulative distribution function (cdf) F(x) given by:
F(x) =0 for x<0, F(x)=x3(43x) for 0 3x51, and Fm =1 for x21.
Let m be the median of the dis
HW 12 Stats 20 Spring 2017
Daniel Hwang
April 26, 2017
1
1. SD+=(1.5.5)(4.642)=5.686 avg=218/3=72.7 SE=5.686/(3.5)=3.282 t=72.7-70/3.282= .82267 p-value=25%
so machine is properly calculated since it passes at a 10% significance level.
2. SD+=(7/6).5(6.49
-title: "HW#07 - Stats20"
author: "Daniel Hwang"
Dat: "03/09/17"
output: html_document
-#Question 1
a. False, because the equation given is assuming that each event is mutually
exclusive which is not the case. It also does not factor in the at least one a
HW10 - Stats20
Daniel Hwang
Question 1
(0.2*0.8)(1/2)(400)(1/2) = 8 8/400 = 2%
Question 2
1. (72/400)*(328/400)(1/2)(400)(1/2) = 7.68 7.68/400 = 1.92% The first person estimates the
percentage of 1s in the box as 18%, and figures this estimate is likeley
HW 11
Daniel Hwang (Worked with Tucker Johnson)
4/20/2017
Question 1) SE = sqrt(100) X 10 = 100 SE of avg = 100/100 = 1 (22.7- 20)/1 = 2.7 SE
It isn't plausible
Question 2a) 32%. There is a large chance of getting an extreme statistic so there is
a