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Statisti cs Dealing With Uncertainty

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Page 1: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Statistics

Dealing With Uncertainty

Page 2: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Objectives Describe the difference between a sample

and a population Learn to use descriptive statistics (data

sorting, central tendency, etc.) Learn how to prepare and interpret

histograms State what is meant by normal distribution

and standard normal distribution. Use Z-tables to compute probability.

Page 3: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Statistics

“There are lies, d#$& lies, and then there’s statistics.”

Mark Twain

Page 4: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Statistics is... a standard method for...

- collecting, organizing, summarizing, presenting, and analyzing data - drawing conclusions - making decisions based upon the

analyses of these data. used extensively by engineers (e.g.,

quality control)

Page 5: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Populations and Samples Population - complete set of all of

the possible instances of a particular object e.g., the entire class

Sample - subset of the population e.g., a team

We use samples to draw conclusions about the parent population.

Page 6: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Why use samples? The population may be large

all people on earth, all stars in the sky. The population may be dangerous to

observe automobile wrecks, explosions, etc.

The population may be difficult to measure subatomic particles.

Measurement may destroy sample bolt strength

Page 7: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Team Exercise: Sample Bias

To three significant figures, estimate the average age of the class based upon your team.

When would a team not be a representative sample of the class?

Page 8: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Measures of Central Tendency

If you wish to describe a population (or a sample) with a single number, what do you use?

Mean - the arithmetic average Mode - most likely (most common)

value. Median - “middle” of the data set.

Page 9: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

What is the Mean? The mean is the sum of all data

values divided by the number of values.

Page 10: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Sample Mean

Where: is the sample mean xi are the data points n is the sample size

n

iixn

x1

1

x

Page 11: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Population Mean

Where: μ is the population mean

xi are the data points

N is the total number of observations in the population

N

iixN 1

1

Page 12: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

What is the Mode? mode - the value that occurs the

most often in discrete data (or data that have been grouped into discrete intervals)

Example, students in this class are most likely to get a grade of B.

Page 13: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Mode continued

Example of a grade distribution with mean C, mode B

0

5

10

15

20

25

F D C B A

Page 14: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

What is the Median?

Median - for sorted data, the median is the middle value (for an odd number of points) or the average of the two middle values (for an even number of points). useful to characterize data sets

with a few extreme values that would distort the mean (e.g., house price,family incomes).

Page 15: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

What Is the Range? Range - the difference between

the lowest and highest values in the set. Example, driving time to Houston is 2

hours +/- 15 minutes. Therefore... Minimum = 105 min Maximum = 135 minutes Range = 30 minutes

Page 16: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Standard Deviation

Gives a unique and unbiased estimate of the scatter in the data.

Page 17: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Standard Deviation Population

Sample

2

1

)(1

N

iix

N

2

1

)()1(

1xx

ns

n

ii

Deviation

Variance = 2

Variance = s2

Page 18: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

The Subtle Difference Between and σ

N versus n-1n-1 is needed to get a better

estimate of the population from the sample s.

Note: for large n, the difference is trivial.

Page 19: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

A Valuable Tool Gauss invented standard deviation circa 1700 to explain the error observed in measured star positions.

Today it is used in everything from quality control to measuring financial risk.

Page 20: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Team Exercise In your team’s bag of M&M

candies, count the number of candies for each color the total number of candies in the bag

When you are done counting, have a representative from your team enter your data on the board

Using Excel, enter the data gathered by the entire classMore

Page 21: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Team Exercise (con’t)

For each color, and the total number of candies, determine the following:

maximum modeminimum medianrange standard deviationmean variance

Page 22: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Individual Exercise: Histograms

Flip a coin EXACTLY ten times. Count the number of heads YOU get.

Report your result to the instructor who will post all the results on the board

Open Excel Using the data from the entire class,

create bar graphs showing the number of classmates who get one head, two heads, three heads, etc.

Page 23: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Data Distributions The “shape” of the data is described

by its frequency histogram. Data that behaves “normally”

exhibit a “bell-shaped” curve, or the “normal” distribution.

Gauss found that star position errors tended to follow a “normal” distribution.

Page 24: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

The Normal Distribution The normal distribution is

sometimes called the “Gauss” curve. 22 /

2

1

2

1RF

xe

mean

x

RF

RelativeFrequency

Page 25: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Standard Normal Distribution

Define:

Then

/ xz

2RF

2

2

1z

e

0.0

0.1

0.2

0.3

0.4

0.5

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0

Area = 1.00

z

Page 26: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Some handy things to know.

50% of the area lies on each side of the mid-point for any normal curve.

A standard normal distribution (SND) has a total area of 1.00.

“z-Tables” show the area under the standard normal distribution, and can be used to find the area between any two points on the z-axis.

Page 27: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Using Z Tables (Appendix C, p. 624)

Question: Find the area between z= -1.0 and z= 2.0 From table, for z = 1.0, area = 0.3413 By symmetry, for z = -1.0, area = 0.3413 From table, for z= 2.0, area = 0.4772 Total area = 0.3413 + 0.4772 = 0.8185 “Tails” area = 1.0 - 0.8185 = 0.1815

Page 28: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

“Quick and Dirty” Estimates of and

(lowest + 4*mode + highest)/6 For a standard normal curve, 99.7%

of the area is contained within ± 3 from the mean.

Define “highest” = Define “lowest” = Therefore, (highest - lowest)/6

Page 29: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Example: Drive time to Houston

Lowest = 1 h Most likely = 2 h Highest = 4 h (including a flat tire,

etc.) = (1+4*2+4)/6 = 2.16 (2 h 12 min) = (4 - 1)/6= 0.5 h

This technique (Delphi) was used to plan the moon flights.

Page 30: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Team Exercise You want to put a scale on your

rubber-band car to relate a given scale setting and an expected distance traveled.

Design an experiment to establish a scale for your car.

More

Page 31: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Team Exercise continued. Some Issues to consider:

Sample size Range of distances Desired accuracy

Page 32: Statistics Dealing With Uncertainty. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting,

Review Central tendency

mean mode median

Scatter range variance standard deviation

Normal Distribution