First things first
Making money in the stock market is all about
the rate of return and not on the actual
amount of money earned
In stock market investing, it takes money to
make money
What
investing
Stock Investing 101
Learning the Game of Stocks
Tyrone Panzer Chan Pao
[email protected]
A lil something bout me
Have been investing in the stock market since
2008
Have been managing the mone
Valuation/Is this stock cheap?
Price to Net Cash ratio
One of the most conservative valuation ratios
Price to Cash ratio
One of the most conservative valuation ratios
The Case of HTC
Remember the case
What if picking stocks
on your own is not
your cup of tea?
Mutual Funds
A mutual fund is a corporation that pools
together the funds of various investors-both
individuals and corporations
The pool of
9
Parametric Inference
We now turn our attention to parametric models, that is, models of the form
.;y =
cfw_f(x; 8):
8
E
8
(9.1)
where the 8 c ~k is the parameter space and 8 = (8 1 , . , 8k ) is the
An Introduction to
Robust Estimation with
R Functions
Ruggero Bellio
Department of Statistics, University of Udine
[email protected]
Laura Ventura
Department of Statistics, University of Pad
Op and Op
In statisics, probability and machine learning, we make use of 0p and Op notation.
Recall rst, that on = 0(1) means that an > 0 as n + 00. on = 0(1) means that
(tn/b = 0(1).
on = 0(1) means
3
Expectation
3.1
Expectation of a Randorn Variable
The mean, or expectation, of a random variable X is the average value of
x.
3.1 Definition. The expected value, or mean, or first moment, of
X is de
14
Multivariate Models
In this chapter we revisit the Multinomial model and the multivariate Normal.
Let us first review some notation from linear algebra. In what follows, x and
yare vectors and A is
10
Hypothesis Testing and p-values
Suppose we want to know if exposure to asbestos is associated with lung
disease. We take some rats and randomly divide them into two groups. We
expose one group to a
4
Inequalities
4.1
Probability Inequalities
Inequalities are useful for bounding quantities that might otherwise be hard
to compute. They will also be used in the theory of convergence which is
discus
6
Models, Statistical Inference and
Learning
6.1
Introduction
Statistical inference, or "learning" as it is called in computer science, is the
process of using data to infer the distribution that gene
1
Probability
1.1
Introduction
Probability is a mathematical language for quantifying uncertainty. In this
Chapter we introduce the basic concepts underlying probability theory. We
begin with the samp
5.10 Theorem. Assume the some conditions as the GET. T hen,
EEK) w N(0,1).
Step 1. We rst show that RE, 1: 02 where
1 _
Rf, = E 2 :(X, X)?
i=1
Note that
2
1 1 "
R:=;X3(EZI:X) .
Dene Y; = X ,2 Then, us
7
Estimating the CDF and Statistical
Functionals
The first inference problem we will consider is nonparametric estimation of the
CDF F. Then we will estimate statistical functionals, which are functio
5
Convergence of Random Variables
5.1
Introduction
The most important aspect of probability theory concerns the behavior of
sequences of random variables. This part of probability is called large samp
Springer Texts in Statistics
Advisors:
George Casella Stephen Fienberg Ingram Olkin
Springer Texts in Statistics
AJfred: Elements of Statistics for the Life and Social Sciences
Berger: An Introduction
11
Bayesian Inference
11.1
The Bayesian Philosophy
The statistical methods that we have discussed so far are known as frequentist (or classical) methods. The frequentist point of view is based on the
8
The Bootstrap
The bootstrap is a method for estimating standard errors and computing
confidence intervals. Let Tn = g(Xl' . . , Xn) be a statistic, that is, Tn is any
function of the data. Suppose w
2
Random Variables
2.1
Introduction
Statistics and data mining are concerned with data. How do we link sample
spaces and events to data? The link is provided by the concept of a random
variable.
2.1 D
15
Inference About Independence
In this chapter we address the following questions:
(1) How do we test if two random variables are independent?
(2) How do we estimate the strength of dependence betwee
12
Statistical Decision Theory
12.1
Prelirninaries
We have considered several point estimators such as the maximum likelihood
estimator, the method of moments estimator, and the posterior mean. In fac
Given
PV =
i=
period
years
Year A
5.4M *0.8
0.075
12
30
8
Solution:
To Solve for Anuity
A=
PMT(0.075/12,360,-5400000*0.8,0)
$30,206.07
To Solve for balance @end of 8years
balance= 4320000-ABS(CUMPRINC
AP Calculus AB
Summer Assignment
Welcome to AP Calculus. The following pages contain 40 problems that a successful AP Calculus student should be able to
easily solve upon beginning the course. Complet
AP stats chapter 5 practice exam
Name_
Date_
Class_
1.
6.
2.
7.
3.
8.
4.
9.
5.
10.
1.
An assignment of probability must obey which of the following?
(a) The probability of any event must be a number b
AMCS 310: Assignment 1
Due on Sep. 11, 2017
1. Download the dataset trees.txt. This dataset consists of the location (x,y coordinates) and
the diameter of all trees with diameters greater than 5in loc
STAT 220: Probability and Statistics, Fall 2017
Homework 1
[A] Problems for practice:
Exercise A1: Textbook 1.10.10
[B] Problems due August 30, 2017, in class:
Exercise B1: Textbook 1.10.12
Exercise B
AMCS 245 Homework 2
Submitted by Mary Lai Salvaa
ID No. 156071
September 26, 2017
1. Consider the linear model Yn1 = Xnp p1 + n1 , with usual assumptions and notation.
a. Suppose the covariates are su