Economics 105 statistics
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Economics 105: Statistics. http://www.davidson.edu/academic/economics/foley/105/index.html Powerpoint slides meant to help you listen in class print out BEFORE you do the day ’ s reading! things will seem fast if you do the reading AFTER lecture

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Economics 105: Statistics

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Economics 105 statistics

Economics 105: Statistics

http://www.davidson.edu/academic/economics/foley/105/index.html

Powerpoint slides

meant to help you listen in class

print out BEFORE you do the day’s reading!

things will seem fast if you do the reading AFTER lecture

I expect you to do the reading prior to class. Everyone does the first few weeks, hard part is continuing to do so (before class). If you don’t, things will seem to go fast.

Stats is one of those classes where you can teach yourself quite a bit via the reading. I expect you to do so.

yes, these are high expectations.

Not everyone likes Powerpoint ...


Economics 105 statistics1

Economics 105: Statistics

Today

What is Statistics?

Presenting data

For next time: Read Chapters 1 – 3.5


Organizing and presenting data graphically

Organizing and Presenting Data Graphically

Data in raw form are usually not easy to use for decision making

Some type oforganizationis needed

Table

Graph

Techniques reviewed in Chapter 2:

Bar charts and pie charts

Pareto diagram

Ordered array

Stem-and-leaf display

Frequency distributions, histograms and polygons

Cumulative distributions and ogives

Contingency tables

Scatter diagrams


Raw form of data

Raw Form of Data

Example: A manufacturer of insulation randomly selects 20 winter days and records the daily high temperature

24, 35, 17, 21, 24, 37, 26, 46, 58, 30,

32, 13, 12, 38, 41, 43, 44, 27, 53, 27


Tabulating numerical data frequency distributions

Tabulating Numerical Data: Frequency Distributions

What is a Frequency Distribution?

A frequency distribution is a list or a table …

containing class groupings (ranges within which the data fall) ...

and the corresponding frequencies with which data fall within each grouping or category


Why use a frequency distribution

Why Use a Frequency Distribution?

It is a way to summarize numerical data

It condenses the raw data into a more useful form

It allows for a quick visual interpretation of the data


Class intervals and class boundaries

Class Intervals and Class Boundaries

  • Each class grouping has the same width

  • Determine the width of each interval by

  • Usually at least 5 but no more than 15 groupings

  • Class boundaries never overlap

  • Round up the interval width to get desirable endpoints


Frequency distribution example

Frequency Distribution Example

Example: A manufacturer of insulation randomly selects 20 winter days and records the daily high temperature

24, 35, 17, 21, 24, 37, 26, 46, 58, 30,

32, 13, 12, 38, 41, 43, 44, 27, 53, 27


Frequency distribution example1

Frequency Distribution Example

(continued)

  • Sort raw data in ascending order:12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

  • Find range: 58 - 12 = 46

  • Select number of classes: 5(usually between 5 and 15)

  • Compute class interval (width): 10 (46/5 then round up)

  • Determine class boundaries (limits):

    • 10, 20, 30, 40, 50, 60

  • Compute class midpoints: 15, 25, 35, 45, 55

  • Count observations & assign to classes


Frequency distribution example2

Frequency Distribution Example

(continued)

Data in ordered array:

12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

Relative

Frequency

Class Frequency

Percentage

10 but less than 20 3 .15 15

20 but less than 30 6 .30 30

30 but less than 40 5 .25 25

40 but less than 50 4 .20 20

50 but less than 60 2 .10 10

Total 20 1.00 100


Tabulating numerical data cumulative frequency

Tabulating Numerical Data: Cumulative Frequency

Data in ordered array:

12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

Cumulative Frequency

Cumulative Percentage

Class

Frequency

Percentage

10 but less than 20 3 15 3 15

20 but less than 30 6 30 9 45

30 but less than 40 5 25 14 70

40 but less than 50 4 20 18 90

50 but less than 60 2 10 20 100

Total 20 100


Graphing numerical data the histogram

Graphing Numerical Data: The Histogram

  • A graph of the data in a frequency distribution is called a histogram

  • The class boundaries(orclass midpoints) are shown on the horizontal axis

  • the vertical axisis eitherfrequency, relativefrequency,orpercentage

  • Bars of the appropriate heights are used to represent the number of observations within each class


Histogram

Histogram

Class Midpoint

Class

Frequency

10 but less than 20 15 3

20 but less than 30 25 6

30 but less than 40 35 5

40 but less than 50 45 4

50 but less than 60 55 2

(No gaps between bars)

Class Midpoints


Graphing numerical data the frequency polygon

Graphing Numerical Data: The Frequency Polygon

Class Midpoint

Class

Frequency

10 but less than 20 15 3

20 but less than 30 25 6

30 but less than 40 35 5

40 but less than 50 45 4

50 but less than 60 55 2

(In a percentage polygon the vertical axis would be defined to show the percentage of observations per class)

Class Midpoints


Graphing cumulative frequencies the ogive cumulative polygon

Graphing Cumulative Frequencies: The Ogive (Cumulative % Polygon)

Lower class boundary

Cumulative Percentage

Class

Less than 10 0 0

10 but less than 20 10 15

20 but less than 30 20 45

30 but less than 40 30 70

40 but less than 50 40 90

50 but less than 60 50 100

Class Boundaries (Not Midpoints)


Summary measures

Summary Measures

Describing Data Numerically

Central Tendency

Quartiles

Variation

Shape

Arithmetic Mean

Range

Skewness

Median

Interquartile Range

Mode

Variance

Geometric Mean

Standard Deviation

Coefficient of Variation


Measures of central tendency

Measures of Central Tendency

Overview

Central Tendency

Mode

Geometric Mean

Arithmetic Mean

Median

Midpoint of ranked values

Most frequently observed value


Arithmetic mean

Arithmetic Mean

  • The arithmetic mean (mean) is the most common measure of central tendency

    • For a sample of size n:

Sample size

Observed values


Arithmetic mean1

Arithmetic Mean

(continued)

  • Mean = sum of values divided by the number of values

  • Affected by extreme values (outliers)

0 1 2 3 4 5 6 7 8 9 10

0 1 2 3 4 5 6 7 8 9 10

Mean = 3

Mean = 4


Median

Median

  • In an ordered array, the median is the “middle” number (50% above, 50% below)

  • Not affected by extreme values

0 1 2 3 4 5 6 7 8 9 10

0 1 2 3 4 5 6 7 8 9 10

Median = 3

Median = 3


Finding the median

Finding the Median

  • The location of the median:

    • If the number of values is odd, the median is the middle number

    • If the number of values is even, the median is the average of the two middle numbers

  • Note that is not the value of the median, only the position of the median in the ranked data


Economics 105 statistics

Mode

  • A measure of central tendency

  • Value that occurs most often

  • Not affected by extreme values

  • Used for either numerical or categorical (nominal) data

  • There may may be no mode

  • There may be several modes

0 1 2 3 4 5 6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

No Mode

Mode = 9


Review example

Five houses on a hill by the beach

Review Example

House Prices: $2,000,000 500,000 300,000 100,000 100,000


Review example summary statistics

Mean: ($3,000,000/5)

= $600,000

Median: middle value of ranked data = $300,000

Mode: most frequent value = $100,000

Review Example:Summary Statistics

House Prices: $2,000,000

500,000 300,000 100,000 100,000

Sum $3,000,000


Which measure of location is the best

Meanis generally used, unless extreme values (outliers) exist

Then medianis often used, since the median is not sensitive to extreme values.

Example: Median home prices may be reported for a region – less sensitive to outliers

Which measure of location is the “best”?


Geometric mean

Geometric Mean

  • Geometric mean

    • Used to measure the rate of change of a variable over time

  • Geometric mean rate of return

    • Measures the status of an investment over time

    • Where Ri is the rate of return in time period i


Example

Example

An investment of $100,000 declined to $50,000 at the end of year one and rebounded to $100,000 at end of year two:

50% decrease 100% increase

The overall two-year return is zero, since it started and ended at the same level.


Example1

Example

(continued)

Use the 1-year returns to compute the arithmetic mean and the geometric mean:

Arithmetic mean rate of return:

Misleading result

Geometric mean rate of return:

More accurate result


Quartiles

Quartiles

  • Quartiles split the ranked data into 4 segments with an equal number of values per segment

25%

25%

25%

25%

Q1

Q2

Q3

  • The first quartile, Q1, is the value for which 25% of the observations are smaller and 75% are larger

  • Q2 is the same as the median (50% are smaller, 50% are larger)

  • Only 25% of the observations are greater than the third quartile


Quartile formulas

Quartile Formulas

Find a quartile by determining the value in the appropriate position in the ranked data, where

First quartile position:Q1 = (n+1)/4

Second quartile position:Q2 = (n+1)/2(the median position)

Third quartile position:Q3 = 3(n+1)/4

where n is the number of observed values


Quartiles1

Quartiles

  • Example: Find the first quartile

Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22

(n = 9)

Q1 is in the(9+1)/4 = 2.5 positionof the ranked data, so use the value half way between the 2nd and 3rd values, so Q1 = 12.5

Q1 and Q3 are measures of noncentral location

Q2 = median, a measure of central tendency


Quartiles2

Quartiles

(continued)

  • Example:

Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22

(n = 9)

Q1 is in the(9+1)/4 = 2.5 positionof the ranked data,

so Q1 = 12.5

Q2 is in the(9+1)/2 = 5th positionof the ranked data,

so Q2 = median = 16

Q3 is in the3(9+1)/4 = 7.5 positionof the ranked data,

so Q3 = 19.5


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