EC999: Classification
Thiemo Fetzer
University of Chicago & University of Warwick
April 27, 2017
Regression versus Classification
I
Classification refers to cases where the yi is a categorical variable,
such as Eye color, Gender, Brand, Sentiment = Positi
EC999: K-Nearest Neighbours (KNN)
Thiemo Fetzer
University of Chicago & University of Warwick
May 4, 2017
K Nearest Neighbours
I
The first classification algorithm we present is called K nearest
neighbors or KNN.
I
KNN assigns a label to a new observation
EC999: Collocations
Thiemo Fetzer
University of Chicago & University of Warwick
April 6, 2017
Collocations
Collocations of a given word are statements of the habitual or
customary places of that word.
I
Noun phrases: strong tea and weapons of mass destruc
EC999: Named Entity Recognition
Thiemo Fetzer
University of Chicago & University of Warwick
January 24, 2017
Named Entity Recognition in Information Retrieval
I
Information retrieval systems extract clear, factual information
I
Can think of trying to answ
EC999: Describing Text
Thiemo Fetzer
University of Chicago & University of Warwick
April 6, 2017
Descriptive Statistics for Text data
Before performing analysis, you want to get to know your data - this
may inform you as to what are the necessary steps fo
Exercise 1
(a)
The airline model is:
(1-
B1
)(1-
)
= (1-0.205* )(1-0.859*
) , with
= 20.79,
B12 hsoldt
B1
B12 at
a2
AIC=3768.77.
Model checking: The standardized residuals indicate the residuals are distributed
around white noise and the ACF of residuals
Booth School of Business, University of Chicago
Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay
Solutions to Midterm
Problem A: (30 pts) Answer briefly the following questions. Each question
has two points.
1. Give two situations under which serial
Booth School of Business, University of Chicago
Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay
Midterm
ChicagoBooth Honor Code:
I pledge my honor that I have not violated the Honor Code during this
examination.
Signature:
Name:
ID:
Notes:
Open not
Booth School of Business, University of Chicago
Business 41202, Spring Quarter 2016, Mr. Ruey S. Tsay
Solutions to Midterm
Problem A: (30 pts) Answer briefly the following questions. Each question
has two points.
1. Give two reasons by which the return se
Booth School of Business, University of Chicago
Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay
Solutions to Midterm
Problem A: (30 pts) Answer briefly the following questions. Each question
has two points.
1. (Questions 1 to 5) Consider the daily l
Booth School of Business, University of Chicago
Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay
Midterm
ChicagoBooth Honor Code:
I pledge my honor that I have not violated the Honor Code during this
examination.
Signature:
Name:
ID:
Notes:
Open not
Booth School of Business, University of Chicago
Business 41202, Spring Quarter 2016, Mr. Ruey S. Tsay
Midterm
ChicagoBooth Honor Code:
I pledge my honor that I have not violated the Honor Code during this
examination.
Signature:
Name:
ID:
Notes:
Open not
Booth School of Business, University of Chicago
Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay
Solutions to Midterm
Problem A: (30 pts) Answer briefly the following questions. Each question
has two points.
1. Give two situations under which returns
Graduate School of Business, University of Chicago
Business 41202, Spring Quarter 2008, Mr. Ruey S. Tsay
Solutions to Midterm
Problem A: (30 pts) Answer briefly the following questions. Each question has two points.
1. Describe two methods for choosing a
Graduate School of Business, University of Chicago
Business 41202, Spring Quarter 2008, Mr. Ruey S. Tsay
Midterm
GSB Honor Code:
I pledge my honor that I have not violated the Honor Code during this examination.
Signature:
Name:
ID:
Notes:
Open notes and
Faculty of Actuaries
Institute of Actuaries
EXAMINATION
22 April 2010 (am)
Subject CT3 Probability and Mathematical Statistics
Core Technical
Time allowed: Three hours
INSTRUCTIONS TO THE CANDIDATE
1.
Enter all the candidate and examination details as req
Faculty of Actuaries
Institute of Actuaries
EXAMINATION
12 April 2005 (am)
Subject CT3
Probability and Mathematical Statistics
Core Technical
Time allowed: Three hours
INSTRUCTIONS TO THE CANDIDATE
1.
Enter all the candidate and examination details as req
5
Expectations
Suppose that the rv X describes the variability of individual wages in a nite
population. A reasonable measure of the center of the wage distribution is the
average of the individual wages. If x1 ; : : : ; xq are the distinct wage values, a
Generating and characteristic functions
Probability generating function
Convolution theorem
Generating
and
Characteristic Functions
Moment generating function
Power series expansion
Convolution theorem
Characteristic function
Characteristic function and m
1
INSTITUTE AND FACULTY OF ACTUARIES
2
3
4
5
6
7
EXAMINATION
8
9
20 April 2016 (am)
10
11
Subject CT3 Probability and Mathematical Statistics
Core Technical
12
13
14
Time allowed: Three hours
15
INSTRUCTIONS TO THE CANDIDATE
16
1.
Enter all the candidate
INSTITUTE AND FACULTY OF ACTUARIES
EXAMINERS REPORT
April 2015 examinations
Subject CT3 Probability and Mathematical Statistics
Core Technical
Introduction
The Examiners Report is written by the Principal Examiner with the aim of helping
candidates, both
Cost Analysis
Managerial and Cost Accounting
Larry M. Walther; Christopher J. Skousen
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Larry M. Walther
Cost Analysis
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2
Cost Analysis: Managerial and Cost Accounting
The Stochastic Growth Model
Koen Vermeylen
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The Stochastic Growth Model
BusinessSumup
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The Stochastic Growth Model
2008 Koen Vermeylen & BusinessSumup
ISBN 978-87-7681-284-3
Downloa
Introduction to Probability
Probability Examples c-1
Leif Mejlbro
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Leif Mejlbro
Probability Examples c-1
Introduction to Probability
Download free eBooks at bookboon.com
2
Probability Examples c-1 Introduction to Probability
2009 Le