Statistics 110, Professor Utts, HW 7, page 1, Due: Nov 19th
Homework 7 Solutions
Assigned Wed, Nov 13: 4.4 and in Part c give Cp for the model you choose
4.4 Use the other variables in the Fertility d
Homework #2: Introduction to Probability
Statistics
Instructor: Jonathan Cook
Due on September 15, 2016
Problem 1. There are 13 people in our class. What is the probability that some
people in our cla
Statistics 110, Professor Utts, HW 5, page 1, Due: Nov 5th
Homework 5 Solutions
3.19, 3.32, and comment on a plot of residuals vs predicted values for the final model you choose.
(Midterm on Wed, Oct
Statistics 610 fall 2013
Solutions to sheet 1
Please attempt at least the starred problems or the starred parts of problems.
[1.1]
Suppose P is a probability distribution on the real line. A median fo
Chapter 9
Poisson approximations
The Bin(n, p) can be thought of as the distribution of a sum of independent
indicator random variables X1 + + Xn , with cfw_Xi = 1 denoting a head on
the ith toss of a
Stats 110, Section 2:
What can we do when regression assumptions are violated?
Michael Thomas Wojnowicz
October 16, 2013
The main topic for this section was using R to answer the questions: What can w
Statistics 610 fall 2013
Solutions to sheet 2
Please attempt at least the starred problems or the starred parts of problems. Of course, if you are hoping for an H (or an A) in the course you
should at
Chapter 1
Probabilities and random variables
Probability theory is a systematic method for describing randomness
and uncertainty. It prescribes a set of mathematical rules for manipulating and calcula
Chapter 1
Probabilities and random variables
Probability theory is a systematic method for describing randomness
and uncertainty. It prescribes a set of mathematical rules for manipulating and calcula
Statistics 610 fall 2013
Solutions to sheet 4
Please attempt at least the starred problems or the starred parts of problems. Of course, if you are hoping for an H (or an A) in the course you
should at
Assumptions in the Normal Linear Regression Model
A1: There is a linear relationship between X and Y.
A2: The error terms (and thus the Ys at each X) have constant variance.
A3: The error terms are in
9/30/2013
STATISTICS 110
Some Fundamental Definitions
Outline for today:
Go over syllabus and outline for the quarter
Overview of basic terminology
Cover Chapter 0
Overview of coverage in this cou
Statistics 610 fall 2013
Solutions to sheet 3
Please attempt at least the starred problems or the starred parts of problems. Of course, if you are hoping for an H (or an A) in the course you
should at
Statistics 610 fall 2013
Solutions to sheet 7
*[7.1]
Under the P distribution, suppose x1 , x2 , . . . are independent and N (, 2 )
distributed, where 2 is a known constant and R. Let be the N (, 2 )
Statistics 110
PRACTICE MIDTERM EXAM KEY
Note that pages have been condensed on this key to fit on 3 pages, to save paper if you print it.
1.
The R output below shows a regression analysis of data fro
Chapter 10
Poisson processes
The Binomial distribution and the geometric distribution describe the behavior
of two random variables derived from the random mechanism that I have called
coin tossing. T
Example illustrating how order matters for the coefficients and anova table
> Full <- lm(HeadCirc~Height+Male+RtArm, data=Caps)
> summary(Full)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Inte
Chapter 2
Multivariate normal distribution
2.1
Basic facts
Let Z1 , Z2 , . . . , Zn be independent N (0, 1) random variables. When treated
as the coordinates of a point in Rn they dene a random vector
STATISTICS 110, FALL 2013
November 18 Homework
Due Tues, November 26
CLARIFICATION ADDED ON SATURDAY, NOVEMBER 23
The file Nov18Hmwk.txt contains a subset of the data for the student and parents heigh
Chapter 13
Multivariate normal distributions
The multivariate normal is the most useful, and most studied, of the standard
joint distributions. A huge body of statistical theory depends on the propert
Homework #6
Use the PhysicalData.txt file (linked to the website) for this assignment. The data set consists of
physical measurements for n=55 college students. Measurements were made by the students
Statistics 110 Homework assignment for Wed, November 20th, due Tues, Nov 26th
Do the following exercises from Chapter 5: #6, 10, 12, 20*
*For 20c, you will need the R command to find the p-value for a
Statistics 110 Homework assignment for Mon, November 24th, due WED, Dec 4th
Use the same data set as for the November 20 assignment, linked to the class webpage, called
Student0405.txt (tab separated)
Section 6
Source lists
are taken at randombased on a juror id assigned at random to each name
on the master listin response to requests from the courts for jurors. For the
1997-98 master list, JIS wil
Statistics 110, Practice Final Exam (Note that more space would be provided to write
answers if this were a real exam.)
1. The scatterplot below shows the regression fit to predict Y = the typical tim
Statistics 110, Practice Final Exam KEY
1. The scatterplot below shows the regression fit to predict Y = the typical time of a hike in
the Adirondack Mountains (in New York) using X = length of the hi
Statistics 110
PRACTICE MIDTERM EXAM
Open notes. Calculator required.
1.
The R output below shows a regression analysis of data from 84 medium-sized counties
in the US. For each county, X = percentage
2.0
KS
WY
1.5
AK
AL
ID
MT
NM
SD
ND
1.0
NE
AR
UT
ratio trucks/cars
NV
WV
VT
MS
DE
HI ME
NH
CO
OR
OK
TX
LA
GA
W
IA MN A
AZ
WI IN
MI
KY
MO TN
NC
SC
VA
MD
0.5
RI
CT
OH
PIL
A
FL
CA
MA NJ
NY
dc
0.0
0.5
1
5
Lecture #7: Hypothesis Testing of a Single
Population Mean with Known
I. Introduction
Last week we introduced the concept of a sampling distributiona distribution
formed from a sample of sample means
Lecture #4: Probability Distributions
I. Random Variables and Probability Distributions
Random variable: numerical description of the outcome of an experiment.
The value of the random variable change
Lecture #4: Continuous Probability Distributions
I. Introduction
Last lecture we introduced a number of concepts:
(1) random variable - numerical description of the outcomes
of an experiment
P
(2) pro
The R Data Editor
R has a simple spreadsheet data editor. To create a new data
frame with the data editor:
1. Create an empty data frame with at least one variable.
> datos <- data.frame(edad = 0)
The
Lecture #6: Transformation of Variables;
Sampling and Sample Distributions
I. Transformation of Variables
When we have spoken about the probability distributions of random variables in past lectures,