1
Economics 240A
Power One
Outline



Course Organization
Course Overview
Resources for Studying
2
I. Organization
Lectures are on Tuesdays and Thursdays, 5:00-6:15 PM in North Hall 1105.
Lecture Notes for class will cover the concepts
Text: Gerald Keller, Statistics for Management and Economics, Seventh
edition (2005)
The Computer Lab is scheduled for Wednesdays, 3:00-3:50. ;400-4:50, & 5:005:50 in Leadbetter, Phelps 1530. The capacity is 25 stations.
Software: Excel and EViews
Lab Notes will cover the procedures of analysis
TA: Stephane Verani, Office, NH 2048
Section: 140A F10:00-10:50 Girvetz 2115, 240A W 6-7:50 Phelps 3505
Exams: Midterm Tuesday, Nov. 6`
Final Tuesday, December 13, 7:30-10:30 PM
Organization ( Cont.)
Problem Sets, Pre-Midterm: #1 Oct. 4, 2007 due Oct 11, 2007
#2 Oct 11, 2007 due Oct 18, 2007
#3 Oct 18, 2007 due Oct 25, 2007
#4 Oct 25, 2007 due Nov.1, 2007
Problem Set, Post-Midterm #5 Nov. 1, 2007 due Nov. 8, 2007
Exercises: as assigned on the Lab Notes
Takehome Project: An exercise to test your quantitative and writing skills. You
can work collectively but the 2-3 page report must be yours. Last Fall we also did
group projects with PowerPoint presentations and I will probably repeat this
format.
Your grade for the course will be based on your scores on the midterm(18%), final(37%)
and 2 projects(each 18%), and your effort as indicated by problem sets and lab exercises
turned in for credit(9%). Of course the latter are more important than the weight
indicated. I distribute the grades by letter, weighing the problem sets one third of a grade
point, and by total score for the class, and reconcile the course grades.
Course Overview

Topics in Statistics
•
•
•
•
•
•
•
•
Descriptive Statistics
Exploratory Data Analysis
Probability and Distributions
Proportions
Interval Estimation
Hypothesis Testing
Correlation and Regression
Analysis of Variance
5
Concepts 1

Two types of data:
• Time series
• Cross section
6
http://research.stlouisfed.org/fred2/
Index 1982-84 =100
7
http://research.stlouisfed.org/fred2/
8
CPIAUCNS Jan 1921- Aug 2007
9
Examples of:
1. Graphical Display of Results
2. Cross-Section Data
3. Survey Sample of 12,571
1. Men & women
2. Ages 15-44
10
What is the
Message?
11
Concepts 2


Population Versus Sample
Iowa Caucuses, New Hampshire Primary
• Population: All eligible voters
• Sample: Field poll in California
sample
Pop
12
13
14
Concepts 3

Different views of the world (universe)
• Deterministic
• Stochastic
15
Statistical Inference and
Probability

Deterministic
• Newtonian physics: e g. distance = rate*time
• Einsteinian(relativistic) physics: E=m*c2

Stochastic (random)
• Quantum mechanics
16
Statistical Inference and Probability



Probability: A tool to understand chance
What is chancy about the statistical world
we will study?
Example:
• Suppose I number everyone in the class from 1
to 65?
• And draw one number a meeting to ask a
question; what is the likelihood I will call on
you today?
17
18
19
20
21
Resources for Studying

Keller
• Text Readings
• CDROM
• Applets

Instructor
•
•
•
•
Lecture Notes
Lab Notes & Exercises
Problem Sets
PowerPoint Slide
Shows
22
23
Keller CDROM
24
http://www.duxbury.com/statistics
25
Student Book Companion Site
26
Concepts 4

Three types of data
• Cardinal
• Ordinal
• Categorical
27
Keller & Warrack Slide Show

Excerpts from Ch. 2
28
Chapter 2
Graphical
Descriptive
Techniques
29
2.1 Introduction

Descriptive statistics involves the arrangement,
summary, and presentation of data, to enable
meaningful interpretation, and to support decision
making.
 Descriptive statistics methods make use of
• graphical techniques
• numerical descriptive measures.
 The methods presented apply to both
• the entire population
• the population sample
30
2.2 Types of data and information

A variable - a characteristic of population or
sample that is of interest for us.
• Cereal choice
• Capital expenditure
• The waiting time for medical services

Data - the actual values of variables
• Interval data are numerical observations
• Nominal data are categorical observations
• Ordinal data are ordered categorical observations
31
Types of data - examples
Interval data
Nominal
Age - income
55
42
75000
68000
.
.
.
.
Weight
gain
+10
+5
.
.
Person Marital status
1
2
3
married
single
single
.
.
Computer
.
.
Brand
1
2
3
.
.
IBM
Dell
IBM
.
.
32
Types of data - examples
Interval data
Nominal data
With nominal data,
all we can do is,
calculate the proportion
of data that falls into
each category.
Age - income
55
42
.
.
75000
68000
.
. gain
Weight
+10
+5
.
.
IBM
25
50%
Dell Compaq
11
8
22% 16%
Other
6
12%
Total
50
33
Types of data – analysis
 Knowing the type of data is necessary to properly
select the technique to be used when analyzing data.
 Type of analysis allowed for each type of data



Interval data – arithmetic calculations
Nominal data – counting the number of observation in each
category
Ordinal data - computations based on an ordering process
34
Cross-Sectional/Time-Series Data

Cross sectional data is collected at a certain point
in time
• Marketing survey (observe preferences by gender, age)
• Test score in a statistics course
• Starting salaries of an MBA program graduates

Time series data is collected over successive
points in time
• Weekly closing price of gold
• Amount of crude oil imported monthly
35
2.3 Graphical Techniques for
Interval Data

Example 2.1: Providing information
concerning the monthly bills of new
subscribers in the first month after signing
on with a telephone company.
• Collect data
• Prepare a frequency distribution
• Draw a histogram
36
Example 2.1: Providing information
Collect data
Bills
42.19
38.45
29.23
89.35
118.04
110.46
0.00
72.88
83.05
.
.
(There are 200 data points
Prepare a frequency distribution
How many classes to use?
Number of observations
Less then 50
50 - 200
200 - 500
500 - 1,000
1,000 – 5,000
5,000- 50,000
More than 50,000
Number of classes
5-7
7-9
9-10
10-11
11-13
13-17
17-20
Class width = [Range] / [# of classes]
[119.63 - 0] / [8] = 14.95
Largest
Largest
Largest
Largest
observation
observation
observation
observation
Smallest
Smallest
Smallest
Smallest
observation
observation
observation
observation
15
37
Example 2.1: Providing information
Draw a Histogram
Frequency
80
60
40
20
0
15 30
45 60
75 90 105 120
Bills
Bin
Frequency
15
71
30
37
45
13
60
9
75
10
90
18
105
28
120
14
38
Example 2.1: Providing information
nnnnWhat information can we extract from this histogram
60
40
Bills
120
105
90
75
60
45
0
30
20
15
Frequency
About half of all A few bills are in Relatively,
the bills are small the middle range large number
13+9+10=32 of large bills
80 71+37=108
18+28+14=60
39
Class width

It is generally best to use equal class width, but
sometimes unequal class width are called for.

Unequal class width is used when the frequency
associated with some classes is too low. Then,
• several classes are combined together to form a wider
and “more populated” class.
• It is possible to form an open ended class at the higher
end or lower end of the histogram.
40
Shapes of histograms

There are four typical shape characteristics
41
Shapes of histograms
Negatively skewed
Positively skewed
42
Modal classes
A modal class is the one with the largest number
of observations.
A unimodal histogram
The modal class
43
Descriptive Statistics

Central Tendency
• mode
• median
• mean

Dispersion
• standard deviation
• interquartile range (IQR)
44
Concepts 5

Normal Distribution
• Central tendency: mean or average
• Dispersion: standard deviation

Non-normal distributions
45
46
Concepts 6


What do we mean by central tendency?
Possibilities
• What is the most likely outcome?
• What outcome do we expect?
• What is the outcome in the middle?
47
Moving from Concepts to
Measures

Mode: most likely value.
48
49
50
Moving from Concepts to
Measures


Mode: most likely value.
Median: sort the data from largest to
smallest. The observation with half of the
values larger and half smaller is the median.
51
52
Moving from Concepts to
Measures



Median: sort the data from largest to
smallest. The observation with half of the
values larger and half smaller is the median.
Mode: most likely value.
Mean or average: sum the values of all of
the observations and divide by the number
of observations.
53
54
Concepts 7


What do we mean by dispersion?
Possibilities
• How far, on average are the values from the
mean?
• What is the range of values from the biggest to
the smallest?
55
Exploratory Data Analysis


Stem and Leaf Diagrams
Box and Whiskers Plots
56
Weight Data
Males:
140 145 160 190 155 165 150 190 195 138
160 155 153 145 170 175 175 170 180 135 170 157
130 185 190 155 170 155 215 150 145 155 155 150
155 150 180 160 135 160 130 155 150 148 155 150
140 180 190 145 150 164 140 142 136 123 155
Females:
140 120 130 138 121 125 116 145 150 112 125 130
120 130 131 120 118 125 135 125 118 122 115 102
115 150 110 116 108 95 125 133 110 150 108
58
59
Box Diagram
median
First or lowest quartile;
25% of observations below
Upper or highest quartile
25% of observations above
60
61
Whiskers



The whiskers end with points that are not
outliers
Outliers are beyond 1.5 times the
interquartile range ( in this case IQR = 31),
so 1.5*31 = 46.5
1st quartile – 1.5*IQR = 125 – 46.5 =
78.5,but the minimum is 95 so the lower
whisker ends with 95.
62
3rd Quartile + 1.5* IQR = 156 + 46.5 = 202.5; 1st value below =195
Next Tuesday Only!

Meet in Humanities and Social Sciences,
HSSB, 1203 web: www.lsit.ucsb.edu
• Exploratory data analysis using JMP
64