Lab 1: Batter Up
1
Veronica Gutierrez
Discussion 3D
Lab Questions
Question 1: Does the number of at-bats predict the number of runs a team will score? Create a
graph that shows the relationship between runs and at bats from the Batting11 Collection. What
Stat 101A HW#4
ZHIYI PAN
October 24, 2016
Question one.
airfares <- read.delim("C:/Users/zhiyi/Desktop/Stat 101A/HW4/airfares.txt")
attach(airfares)
(a) Provide a detailed critique of this conclusion.
Based on the output for model(3.7), we can see that th
1. How many chromosomes occur in a normal human diploid cell?
Question 1 options:
a. 44
b. 48
c. 46
d. 53
e. 23
Question 2 (1 point)
2. In Europe during the Middle Ages, it was believed that
Question 2 options:
a. all species had evolved from a common anc
Question 1
0 / 1 point
1. The term "archaic Homo sapiens"
refers to forms that were transitional from Homo habilis to Homo erectus.
is another term for Homo erectus.
refers only to Neanderthals.
refers to forms that display some derived features of Homo s
Question
1 / 1 point
1
1. Hominids have been variably defined as having
a large brain.
toolmaking abilities.
adaptations for bipedalism.
all of these.
A and C only.
Question 2
1 / 1 point
2. Adaptive advantages of bipedalism include
freeing the hands for
Stat 101A
Summer 17 Esfandiari and Manoukian
Homework Two
Problem one. Given the following information, calculate.
a) The slope
b) The standard error of the slope
c) Then using the above test the null hypothesis that
b
1
= 0 and interpret the results
with
Statistics 101A
Syllabus Summer 2017
Instructor: Mahtash Esfandiari
Time of lecture: MW 3:00 to 4:50
Place of lecture: Boelter Hall 2760
Instructor email: [email protected], [email protected]
Office Hours: Tuesday 2:00 to 3:00, Thursday 2:00 to 3:00
!
1!
Statistics 101B
Week two/Lecture two
Professor Esfandiari
The objective of this part of the lecture is to
Remind you of the different sums of squares about which we talked
last session
Show you analysis of variance in regression analysis
Interpret
Total variance in
discrimination against
women is divided to two
parts
SS TOTAL
The part of the total
variance that WE CAN
EXPLAIN by the
predictor or level of
education This is the
ETWEEN PART
OR THE REGRESSION
PART
SS
!
1!
!
Exercise!one!week!two!stat!101A!
!
!
Professor!Esfandiari!
!
Problem!one.!Question eight
a)
b)
c)
d)
Explain the following command: > newdata=data.frame(ucladiscp=50)
Interpret the prediction interval
Interpret the confidence interval
Compare and c
Stat 101A
Answers In-class exercise four Professor Esfandiari
The following formula is used for calculation of leverage
hii
1 ( xi x)
n
SXX
2
The following is the formula for residual
^
r
i
e
i
S
e
1 hii
Given the following data
Data set one
X1
Y1
4
7
9
QUALITY ISSUES
AND STANDARDS
Extrapolation of Correlation Between
2 Variables in 4 General Medical Journals
Yen-Hong Kuo, ScM, MS
A
Context An estimated correlation between 2 variables is valid only within the range
of observed data. Extrapolation is risk
1"
"
"
"
Lecture seven
Hat Matrix and Leverage Continued
Professor Esfandiari
"
"
The objective of this part of the lecture is to clarify
hii " " and h ij " " by referring to the article by Hoaglin, et
l on The Hat Matrix in Regression and ANOVA
published
!
1!
Introduction to Linear Models
Professor Esfandiari
Lecture one
The objective of the lectures is to
Give you a general overview of regression.
Remind you of the mean and standard deviation of a continuous
random variable.
Elaborate that the mean mi
32127ad073750d03e596413179396faba9be0e29
id
female
70 male
121 female
86 male
141 male
172 male
113 male
50 male
11 male
84 male
48 male
75 male
60 male
95 male
104 male
38 male
115 male
76 male
195 male
114 male
85 male
167 male
143 male
41 male
20 male
Statistics 101A
Answers to Homework one Summer 17
Professor Esfandiari
All of the relevant data sets for this homework are posted on week one of CCLE
Problems one to six are review.
Problem one: Download the following data files to your hard drive, and th
1
Introduction to Linear Models
Week Two Linear models continued
Professor Esfandiari
The objective of this part of this lecture is to
Test the assumption of regression including linearity, normality, and equality
of varian
Outline
Lecture 7.1
Partial F-tests
Relation between F-tests in ANOVA table and the ttests in the summary() output
Adjusted R-squared
Working with Categorical variables
Many different tests
possible
1. Test for the entire model (F-test to Fit)
(standard o
Stat 101A
In-class exercise four Professor Esfandiari
The following formula is used for calculation of leverage
1 ( x x)
hii n i
SXX
2
The following is the formula for residual
^
r
i
e
i
S
e
1 hii
Given the following data
Data set one
X1
Y1
4
7
9
11
15
17
Chapter 5 updated
Dr. Akram Almohalwas
Sunday, November 09, 2014
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and
MS Word documents. For more details on using R Markdown see http:/rmarkdown.rstudio.com.
W
outline
Finding Meaning in
your Model
Interpreting the model
Interpreting slopes for log transforms
Weighted Least Squares
Intro to Multiple Regression
conditions
Normal condition
Bad. But transforms can sometimes help. Weighted least squares can help.
In
outline
methods in R
Fixing problems
Reminder of diagnostic plots
upload magazines.csv (Data folder in Sites Info section
of CCLE)
Using log transformation to fix problems with trend
The polynomial transformation
Conclusion: the model trend should match t
Omitting variables that belong leads to bias.
slope of storks, controlling for
number of women, is 0
Model Building
slope of storks is positive
adding variables that dont
belong leads to overfitting
#Truth
x<- 1:10
y <- 1 + 2*x + rnorm(length(x),0, 4)
goo
Bootstrap Intro
Why Bootstrap?
What is Bootstrapping?
Lecture 10.2
outline
when should we use it?
yi =
Bootstrap Confidence intervals for one sample
percentile
semi-parametric
0
+
1 xi
+ i
i N (0, )
but what if i ?(0, )
and we want CIs for the parameters?
Lecture 8.1
holding other variables constant and checking
conditions
What does it mean:
adjusting for other
variables?
Is there salary discrimination against women at
a certain (and anonymous) college?
Before considering these data, lets look again at
th
Statistics 101A
Chapter 6
The objective of this lecture is to talk about checking assumptions for multiple linear
regression.
The assumptions of MLR are the same as SLR. However, there are cases where more
needs to be done.
This is the checklist we need t
Stat 101A HW#6
ZHIYI PAN
November 13, 2016
Part one: Using the UCLA stress echo data set
Stress <- read.delim("C:/Users/zhiyi/Desktop/Stat 101A/HW6/Stress2.txt")
attach(Stress)
m1n <- aov(maxhr ~ gender + newPTCA + gender:newPTCA)
summary(m1n)
#
#
#
#
#
D
Statistics 101A
Chapter 6
The objective of this lecture is to talk about checking assumptions for multiple linear
regression.
The assumptions of MLR are the same as SLR. However, there are cases where more
needs to be done.
This is the checklist we need t
Statistics 101A:
Homework # 7
Fall 2016
Problems assigned from the book
Problem Two from Chapter seven page 255
Q3 from Chapter 7:
An avid fan of the PGA tour with limited background in statistics has sought your help in
answering one of the age-old quest
1
Statistics 101A
Sections 6.3 and 6.4: Multicollinearity
Prepared by: Professor Esfandiari
Edited by: Akram Almohalwas
The objective of this part of the lecture is to introduce the matrix notation for multiple
linear regression.
The linear model we have
Chapter 7
Akram Almohalwas
October 21, 2015
This is an R Markdown document. Markdown is a simple formatting syntax for authoring
HTML, PDF, and MS Word documents. For more details on using R Markdown see
http:/rmarkdown.rstudio.com.
When you click the Kni
What you need to know for the final exam
Statistics 101B/Winter 2012
Hypothesis testing in SLR, MLR, and logistic regression
Regression diagnostics and the assumptions that need to be met
Linear t