Stats 191 Assignment 1 Solution
Hera He
*
February 3, 2012
Question 1
1
url <- "http:/stats191.stanford.edu/data/groundhog.table"
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3
4
5
6
#
#
#
#
Only use sep=',' when the data is really seperated by commas. The
default value for the seperator is "(spac
Statistics 191
Winter 2012
Introduction to Applied Statistics
Assignment #4
Due Wednesday March 07, 2012
Prof. J. Taylor
You may discuss homework problems with other students, but
you have to prepare the written assignments yourself. Late homework will be
Statistics 191
Winter 2012
Introduction to Applied Statistics
Assignment #2
Due Monday February 06, 2012
Prof. J. Taylor
You may discuss homework problems with other students, but
you have to prepare the written assignments yourself. Late homework will be
Statistics 191
Winter 2012
Introduction to Applied Statistics
Assignment #2
Due Monday February 06, 2012
Prof. J. Taylor
You may discuss homework problems with other students, but
you have to prepare the written assignments yourself. Late homework will be
STATS 191
Assignment 4 Solution
March 16, 2012
1
a)
data=read.table("http:/stats191.stanford.edu/data/asthma.table",header=T)
lm1=lm(Y~T+P,data)
summary(lm1)
Call:
lm(formula = Y ~ T + P, data = data)
Residuals:
Min
1Q
-2.8204 -1.0569
Median
0.2916
3Q
1.1
Stat 191
Assignment 1
Winter 2016
The assignment is due on January 15 in class. You need to show your work and answers to each problem
as well as plots and R code and output to substantiate your answers. You dont have to typeset your
solution; handwritten
Simple linear regression
January 20, 2015
1
Simple linear regression
The rst type of model, which we will spend a lot of time on, is the simple linear regresssion model. One
simple way to think of it is via scatter plots. Below are heights of mothers and
Transformations
February 12, 2015
1
Transformations
Transformations to achieve linearity
We have been working with linear regression models so far in the course.
Some models are nonlinear, but can be transformed to a linear model.
We will also see that
Some tips on R
January 8, 2015
0.1
Some help for R
In this short notebook, I will go through a few basic examples in R that you may nd useful for the course.
These are just some of the things I nd useful. Feel free to search around for others.
For those o
Review
January 13, 2015
1
Course Introduction and Review
1.1
Outline
What is a regression model?
Descriptive statistics numerical
Descriptive statistics graphical
Inference about a population mean
Dierence between two population means
2
What is cours
Simple diagnostics
January 27, 2015
0.1
Diagnostics for simple regression
Goodness of t of regression: analysis of variance.
F -statistics.
Residuals.
Diagnostic plots.
0.2
Geometry of least squares
Here are three pictures that help to describe dieren
Selection
February 28, 2015
0.1
Model selection
In a given regression situation, there are often many choices to be made. Recall our usual setup
Yn1 = Xnp p1 +
n1 .
Any subset A cfw_1, . . . , p yields a new regression model
M(A) : Yn1 = X[, A][A] +
n1
by
Stat 191
Introduction to Applied Statistics
Winter 2016
Instructor: Guenther Walther
Oce: Sequoia 135
Oce Hours: MWF 12.30-1.30, or by appointment. If you have just a question or two,
you can ask me right after the lecture since I will usually stay around
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Stat 191
Assignment 3
Winter 2016
The assignment is due on February 17 in class. You need to show your work and answers to each
problem as well as plots and R code and output to substantiate your answers. You dont have to
typeset your solution; handwritte
Stat 191
Assignment 2
Winter 2016
The assignment is due on January 29 in class. You need to show your work and answers to each problem
as well as plots and R code and output to substantiate your answers. You dont have to typeset your
solution; handwritten
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Penalized regression
February 26, 2015
0.1
Bias-variance tradeo
One goal of a regression analysis is to build a model that predicts well.
This is slightly dierent than the goal of making inferences about that weve focused on so far.
What does predict w