Mitch Casey
Section 051
Writing Assignment #1
STATE:
What is a 90% confidence interval estimate for the mean daily number of hours all college
students spend using the Internet?
PLAN:
(1) Point Estimate
(2) The parameter in this context would be the avera
Maggie Jensen
Stat 151
12/05/2016
Stat 151 Final Project
Introduction. People tend to be religious when they are in less than ideal situations. This prompted a look
into if there is a correlation between a countrys gross domestic product and the percentag
#
# Stat 151: Simulating Probability Distributions
# October 5, 2016
#
#
# Dice differences example
#
#
g
#
b
#
d
Sample/simulate a value for G
<- sample(1:6, 1, prob=c(1/6,
Sample/simulate a value for B
<- sample(1:6, 1, prob=c(1/6,
Compute the differenc
#
# Stat 151 In-class code
# Problem of interest: Average number of chocolate chips on top of our cookies
# Date: October 19, 2016
# Updated: October 21, 2016 to obtain the posterior predictive distribution
#
# Pumpkin Chip Cookies (white chocolate chips)
#
# Stat 151
# In class code: October 12, 2016
# Poisson and Gamma distributions
#
# Poisson #
# The Poisson distribution is a distribution for counts (a discrete random
variable)
# Look at the probability distribution of X when X~Pois(lambda=5)
plot(0:30
#
# Stat 151
# In class code: October 17, 2016
# Problem of interest: Average number of mistakes per page for a given
typesetter
#
# Prior Distribution
gamma <- 1
phi <- 1
curve(dgamma(x, gamma, rate=phi), xlim=c(0, 5), lwd=3, cex.axis=3, cex.lab=3,
xlab=
#
# Normal-Normal Code
# October 26, 2016
#
#
# Chipotle (CMG) daily stock returns
#
#Read in the data (collected from finance.yahoo.com)
cmg <- read.csv('~/Downloads/CMG.csv', header=T)
attach(cmg)
#Calculate the daily returns from the daily closing pric
#
# Stat 151 In-class code
# Problem of interest: Average number of chocolate chips on top of our cookies
# Date: October 19, 2016
#
# Pumpkin Chip Cookies (white chocolate chips) #
# RV: X = number of chocolate chips on the pumpkin chip cookies (X ~
Pois
Glossary for terms on Exam 1
association: Values of one variable tend to occur with certain values of
another variable; detected when the conditional distributions differ from the
marginal distribution and from each other.
bar graph: a graphical represent
1. True or false: A confidence interval estimate for calculated using the formula: (x zn, x + zn) always contains the value of x.
True
2. Consider the following confidence interval interpretation: "The mean of all axle
diameters is somewhere between .98 c
1. What symbol is used to denote the response variable?
c. Y
2. A university official wants to determine if a relationship exists between whether
students choose their majors before their junior year and whether they graduate from
college. For this study,
Random Variables and Basic Distributional Quantities
Chapters 2 & 3
Stat 477 - Loss Models
Chapters 2 & 3 (Stat 477)
Review of Random Variables
Brian Hartman - BYU
1 / 29
Introduction
Introduction
Consider an experiment whose outcome is not known in advan
Stat 274
Theory of Interest
Chapter 1: The Growth of Money
Brian Hartman
Brigham Young University
What is interest?
An investment of K grows to S, then the difference (S K ) is the
interest.
Why do we charge interest?
Investment opportunities theory
Time
Stat 274
Theory of Interest
Chapter 3: Annuities
Brian Hartman
Brigham Young University
Types of Annuities
Annuity-immediate: Stream of payments at the end of each
period.
Annuity-due: Stream of payments at the beginning of each
period.
Perpetuity: Stream
Claims Frequency Distribution Models
Chapter 6
Stat 477 - Loss Models
Chapter 6 (Stat 477)
Claims Frequency Models
Brian Hartman - BYU
1 / 19
Introduction
Introduction
Here we introduce a large class of counting distributions, which are
discrete distribut
Credibility
Chapters 17-19
Stat 477 - Loss Models
Chapters 17-19 (Stat 477)
Credibility
Brian Hartman - BYU
1 / 31
Why Credibility?
You purchase an auto insurance policy and it costs $150. That price is
mainly the expected cost of a policyholder with your
Aggregate Loss Models
Chapter 9
Stat 477 - Loss Models
Chapter 9 (Stat 477)
Aggregate Loss Models
Brian Hartman - BYU
1 / 22
Objectives
Objectives
Individual risk model
Collective risk model
Computing the aggregate loss models
Approximate methods
Effect o
Parameter Estimation
Chapters 13-15
Stat 477 - Loss Models
Chapters 13-15 (Stat 477)
Parameter Estimation
Brian Hartman - BYU
1 / 23
Methods for parameter estimation
Methods for parameter estimation
Methods for estimating parameters in a parametric model:
Stat 274
Theory of Interest
Chapters 8 and 9: Term Structure and
Interest Rate Sensitivity
Brian Hartman
Brigham Young University
Yield Curves
(t) is the current market price for a t-year zero-coupon bond.
The t-year spot rate of interest, yt , is the yie
Stat 274
Theory of Interest
Chapter 6: Bonds
Brian Hartman
Brigham Young University
Bonds
A bond is a security issued by a government or a corporation which
promises payments at future dates.
Maturity (or redemption) date: the time of the last payment
Iss
Nonparametric Model Construction
Chapters 4 and 12
Stat 477 - Loss Models
Chapters 4 and 12 (Stat 477)
Nonparametric Model Construction
Brian Hartman - BYU
1 / 28
Types of data
Types of data
For non-life insurance, the types obviously come in the form of
Creating New Distributions
Section 5.2
Stat 477 - Loss Models
Section 5.2 (Stat 477)
Creating New Distributions
Brian Hartman - BYU
1 / 18
Generating new distributions
Some methods to generate new distributions
There are many methods to generate new distr
Stat 274
Theory of Interest
Chapter 5: Loan Repayment
Brian Hartman
Brigham Young University
Amortized Loan
Each time a payment is made, the interest due is paid first.
Examples:
You borrow 2000 at 5% annual interest, and pay back 800 in
one year and 1000
Severity Models - Special Families of Distributions
Sections 5.3-5.4
Stat 477 - Loss Models
Sections 5.3-5.4 (Stat 477)
Claim Severity Models
Brian Hartman - BYU
1/1
Introduction
Introduction
Given that a claim occurs, the (individual) claim size X is typ
Stat 274
Theory of Interest
Chapter 2: Equations of Value and
Yield Rates
Brian Hartman
Brigham Young University
Equations of Value
When using compound interest with a single deposit of c at time
0, the equation of value is
Ac (t) = c(1 + i)t
2
Examples
1
Frequency and Severity with Coverage Modifications
Chapter 8
Stat 477 - Loss Models
Chapter 8 (Stat 477)
Coverage Modifications
Brian Hartman - BYU
1 / 23
Introduction
Introduction
In the previous weeks, we have assumed that the loss amount X is
also the