The University of New Haven
The College of Arts and Sciences
Department of Mathematics and Physics
Course: MATH 2228-04
Title: Elementarry Statistics
Semester: Fall 2014
Meeting Times: MWF 1:40am2:55pm
Classroom: Kaplan 208
Credit Hours: 4
Office Hours: M
Binomial Distribution Problems
1. A company owns 400 laptops. Each laptop has an 8% probability of not working. You randomly select
20 laptops for your salespeople.
a) What is the likelihood that 5 will be broken?
(b) What is the likelihood that they will
Lab 5: Foundations for Statistical Inference - Sampling Distributions
In this lab, we investigate the ways in which the statistics from a random sample of data can
serve as point estimates for population parameters. Were interested in formulating a sampli
Chapter 9 Moore IPS 7e
For Questions 1 2
A large study was done to compare two treatments for recurring ear infections in young children. A total
of 150 subjects were recruited for the study, with 50 subjects assigned at random to treatment 1 and the
rema
Chapter 6 Moore IPS 7e
1. I use computer software to do the following. I generate ten random numbers from a N(500, 100)
distribution. From these ten numbers I compute a 95% confidence interval for the mean using the
formula
x 1.96
100
10
where x is the m
Chapter 4 Moore IPS 7e
1. A college basketball player makes 80% of his free throws. At the beginning of a game he misses his first two
free throws. We may correctly conclude
a) he will make his next eight shots.
b) he will make eight shots in a row someti
The Binomial Distribution
In many cases, it is appropriate to summarize a group of independent observations by the number of observations in
the group that represent one of two outcomes. For example, the proportion of individuals in a random sample who
su
Negative Binomial and Geometric Distributions
As we will see, the geometric distribution is a special case of the negative binomial
distribution.
Negative Binomial Experiment
A negative binomial experiment is a statistical experiment that has the followin
R Handouts Hands On!
Why R? Should I use the R statistical software for introductory statistics?
I assume you know that statistics is done with software and that learning a reputable software package
should be a part of any applied statistics course, so t
Lab 7: Inference for Numerical Data
North Carolina births
In 2004, the state of North Carolina released a large data set containing information on births
recorded in this state. This data set is useful to researchers studying the relation between habits
a
Lab 4.3: Distributions of Random Variables
In this lab well investigate the probability distribution that is most central to statistics: the
normal distribution. If we are confident that our data are nearly normal, that opens the door to
many powerful sta
Lab 6: Foundations for Statistical Inference - Confidence Levels
Sampling from Ames, Iowa
If you have access to data on an entire population, say the size of every house in Ames, Iowa, its
straight forward to answer questions like, How big is the typical
Lab 1.4: The Standard Normal Distribution using
R
One of the most fundamental distributions in all of statistics is the Normal Distribution or the
Gaussian Distribution. According to Wikipedia, "Carl Friedrich Gauss became associated with
this set of dist
Lab 4.2: Probability
Hot Hands
Basketball players who make several baskets in succession are described as having a hot hand.
Fans and players have long believed in the hot hand phenomenon, which refutes the assumption
that each shot is independent of the
Lab 2.4: Introduction to Linear Regression II
We will work with data on the fat and protein content of items on the Burger King menu.
In RStudio, Environment in Quadrant I, goto Import Data, and paste in the URL
http:/statland.org/AP/R/BKmenu.txt
Console
Lab 2.2: Introduction to Linear Regression I
Batter up
The movie Moneyball focuses on the quest for the secret of success in baseball. It follows a
low-budget team, the Oakland Athletics, who believed that underused statistics, such as a
players ability t
Lab 0: Simple Plot in R
This section will begin a very gentle introduction to plotting in R. The goal is to show how one can
draw the plot of a function using R and annotate the resulting plot. The tutorial is easy to follow and
it gives you a nice overvi
Lab 1.3: Introduction to Data
Some define Statistics as the field that focuses on turning information into knowledge. The first step
in that process is to summarize and describe the raw information - the data. In this lab, you will gain
insight into publi
Assignments for MATH 2228-04, Statistics, Spring 2015, MWF 1:40am2:55pm, Kaplan 208
Capt Jim Celone, Kaplan 205A, 203-933-9997, jcel@aol.com. Office hours: MWF 12:15pm1:30pm, M 3:00 4:15
N
01
02
03
04
Date
Class
F 01/23 Lecture 1.1 1.2
M 01/26 Lecture 1.3
Poisson and Exponential Distributions
Poisson Distribution using R
The Poisson distribution is the probability distribution of independent event occurrences in an
interval. If is the mean occurrence per interval, then the probability of having k occurrenc
MATH 2228 E-Resources
Goto
http:/angel.bfwpub.com/section/default.asp?id=ips8e%2Denhanceddemo%2Dpool%2D7%2D9%2D2014%2D3
%2D23%2D49%2DPM
and
http:/bcs.whfreeman.com/ips8e/default.asp#t_922171_
There you will find resources to supplement the text
E-book wh
Z-elay 1
1. The population of professional hockey players is approximately normally distributed with
mean height 73 inches and standard deviation 2 inches. What proportion of hockey players
are over 64 (76 inches) tall? Express the answer as a percent rou
The Standard Normal Distribution
The Standard Normal Distribution is certainly the most important of all the hundreds of other probability
distributions.
Definition
1.
The Standard Normal distribution ranges from + to
2.
Like all distributions the area u
Normal Distribution
A continuous probability distribution a function that tells the probability that an observation in
some context will fall between any two real numbers.
A normal distribution P ( X = k ) is
k
P(X k) =
1
e
2
( x )2
2 2
The parameter in t
THE NORMAL DISTRIBUTION
In real life, most data tends to have a normal distribution. In other words, most data tends to lie near the
population mean . Furthermore, there tends to be roughly as much data below the mean as above the mean.
Thus if we were to
The Simple Linear Regression Model
Population Regression Line gives the mean response variable y for all values of the
explanatory variable, x.
y = 0 + 1 x + e
Data = Fit + Residual
yi = y + ( yi y )
yi = y + ei
ei = yi y
ei = yi b0 b1 x
yi = b0 + b1 x +
Rubiks Cube
Calculating the Number of Positions of Rubiks Cube
There are 6 centers that can take 1 possible arrangement (since they are fixed and
exclude rotations); 8 corners, each of which have 3 possible orientations and 8 possible
positions; and 12 ed
Lab 7: On Your Own
Kristin Jones
Score _
1. Calculate a 95% confidence interval for the average length of pregnancies (weeks) and
interpret it in context. Note that since youre doing inference on a single population parameter,
there is no explanatory vari
Lab 9 On Your Own
Kristin Jones
Score _
1. Sexual harassment in middle and high schools. A nationally representative survey of students
in grades 7 to 12 asked about the experience of these students with respect to sexual harassment.
One question asked ho
Lab 6: On Your Own
Kristin Jones
Score _
1. Using the following function (which was downloaded with the data set), plot all intervals.
What proportion of your confidence intervals include the true population mean? Is this
proportion exactly equal to the c
Lab 8: On Your Own
Kristin Jones
Score _
The question of atheism was asked by WIN-Gallup International in a similar survey that was
conducted in 2005. (We assume here that sample sizes have remained the same.) Table 4 on page
13 of the report summarizes s
Lab 4.2: On Your Own
Kristin Jones
Score _
Comparing Kobe Bryant to the Independent Shooter
Using calc streak, compute the streak lengths of sim basket.
1. Describe the distribution of streak lengths. What is the typical streak length for this simulated
i