estimation and uncertainty 12-706/ 19-702 / 73-359 lecture 2 - august 31, 2005
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Estimation and Uncertainty
12-706/ 19-702 / 73-359Lecture 2 - August 31, 2005
Announcements / Etc.
HW 1 Handed OutBrief LectureFriday Sessions? When?FYI Finding textbooks - www.addall.com
Prices $23-40 Campbell, $28-$80 Clemen Chapters 2-3 in Cambpell assigned for next Wed! If you don’t have Campbell by next Wed, read Chapter
6 of “Project Management for Construction” (Hendrickson) at http://www.ce.cmu.edu/pmbook/
Sorry, I am not allowed to put our text on reserve Or Chapter 6 in Boardman, “Cost-Benefit Analysis”,
Second Edition
Estimation in the Course
We will encounter estimation problems in sections on demand, cost and risks.
We will encounter estimation problems in several case studies.
Projects will likely have estimation problems.
Need to make quick, “back-of-the-envelope” estimates in many cases. Don’t be afraid to do so!
Problem of Unknown Numbers
If we need a piece of data, we can: Look it up in a reference source Collect number through survey/investigation Guess it ourselves Get experts to help you guess it
Often only ‘ballpark’, ‘back of the envelope’ or ‘order of magnitude needed Situations when actual number is unavailable or
where rough estimates are good enough E.g. 100s, 1000s, … (102, 103, etc.)
Source: Mosteller handout
Notes about Reference Sources
Some obvious: Statistical Abstract of US Always check sources and secondary
sources of data Usually found in footnotes – also tells you
about assumptions/conditions for using Sometimes the summarized data is wrong!
Look in multiple sources Different answers implies something about
the data and method – and uncertainty
Estimation gets no respect
The 2 extremes - and the respect thing Aristotle:
“It is the mark of an instructed mind to rest satisfied with the degree of precision which the nature of the subject permits and not to seek an exactness where only an approximation of the truth is possible.”
Archbishop Ussher of Ireland, 1658 AD: “God created the world in 4028 BC on the 9th of
September at nine o’clock in the morning.”
We consider it somewhere in between
In the absence of “Real Data”
Are there similar or related values that we know or can guess? (proxies) Mosteller: registered voters and population
Are there ‘rules of thumb’ in the area? E.g. ‘Rule of 72’ for compound interest r*t = 72: investment at 6% doubles in 12 yrs MEANS construction manual
Set up a ‘model’ to estimate the unknown Linear, product, etc functional forms Divide and conquer
Methods
Similarity – do we have data that can be made applicable to our problem?
Stratification – segment the population into subgroups, estimate each group
Triangulation – create models with different approaches and compare results
Convolution – use probability or weightings (see Selvidge’s table, Mosteller p. 181) Note – example of a ‘secondary source’!!
Notes on Estimation
Move from abstract to concrete, identifying assumptions
Draw from experience and basic data sources
Use statistical techniques/surveys if needed Be creative, BUT Be logical and able to justify Find answer, then learn from it. Apply a reasonableness test
Attributes of Good Assumptions
Need to document assumptions in course Write them out and cite your sources
Have some basis in known facts or experience Write why you make the specific assumptions
Are unbiased towards the answer Example: what is inflation rate next year?
Is past inflation a good predictor? Can I find current inflation? Should I assume change from current
conditions? We typically use history to guide us
How many TV sets in the US?
Can this be calculated? Estimation approach #1:
Survey/similarity How many TV sets owned by class? Scale up by number of people in the
US Should we consider the class a
representative sample? Why not?
TV Sets in US – another way
Estimation approach # 2 (segmenting): Work from # households and # TV’s per
household - may survey for one input Assume x households in US Assume z segments of ownership (i.e.
what % owns 0, owns 1, etc) Then estimated number of television
sets in US = x*(4z5+3z4+2z3+1z2+0z1)
TV Sets in US – sample
Estimation approach # 2 (segmenting): work from # households and # tvs per
household - may survey for one input Assume 50,000,000 households in US Assume 19% have 4, 30% have 3, 35%
2, 15% 1, 1% 0 television sets Then
50,000,000*(4*.19+3*.3+2*.35+.15) = 125.5 M television sets
TV Sets in US – still another way
Estimation approach #3 – published data
Source: Statistical Abstract of US Gives many basic statistics such as
population, areas, etc. Done by accountants/economists - hard
to find ‘mass of construction materials’ or ‘tons of lead production’.
How close are we?
How well did we do? Most recent data = 2001
But ‘recently’ increasing < 2% per year TV/HH - 125.5 tvs, StatAb – 248M TVs, % error: (248M – 125.5M)/125.5M ~ 100% What assumptions are crucial in determining
our answer? Were we right? What other data on this table validate our models?
See ‘SAMPLE ESTIMATION’ linked on web page to see how you are expected to answer these types of questions.
Also see “SAMPLE SPREADSHEET” for a suggested organization in Excel
Notes on Sample Files
The text file gives the type and structure of documentation I expect when doing assumption-based analysis. There is a question like it on Homework 1, make sure your answer looks like that.
The spreadsheet file suggests a framework for building assumptions into spreadsheets, i.e., placing them all at the top where you can see them. If needed, you can use the cell values as links in your equations.
Note the Excel plug-ins we will use later will want to see assumptions done like this.
Changing Assumptions
Statistical Abstract gave additional info: Average TVs/HH = 2.4 (ours was 2.5) Number of households: 100 million (ours
50)Thus to redo our analysis, we should
do a better job at estimating households
Significant Figures
We estimated 125,500,000 TVs in USHow accurate is this - nearest 50,000,
the nearest 500,000, the nearest 5,000,000 or the nearest 50,000,000?
Should only report estimates to your confidence - perhaps 1 or 2 “significant figures” could be reported here.
Figures are only carried along to document calculations or avoid rounding errors.
Some handy/often used data
Population of US btw 275-300 millionNumber of households ~ 100 millionAverage personal income ~$35,000
Avoiding Point Estimates
The tradeoff in this kind of work is getting away with a guess And giving an informed-enough answer that
doesn’t sound like a guess!Really what we should be doing is making
ranges of estimates We will refer to these as lower bound, mean, and
upper bound estimates You might think of lower bound as “5th percentile”
and upper as “95th percentile” So they’re not true lower/upper bounds (which
might be zero and infinity).
Exercise #2: Estimate Annual Vehicle Miles Travelled (VMT) in the US
Estimate “How many miles per year are passenger automobiles driven in the US?”
Types of models Similar to TVs: Guess number of cars,
segment population into miles driven per year
Find fuel consumption data, guess at fuel economy ratio for passenger vehicles
Other ideas? Let’s try it on the board.
Estimate VMT in the US
Table 1093 of 2003 Stat. Abstract suggests 2001 VMT was 2.28 trillion miles (yes - twice as much as 1972 implied in the Mosteller handout)! 235 billion ‘passenger car trips’ per year About 200 million cars Avg VMT 21,000 mi., about 10,000 miles per car
Note the Dept of Transportation separately specifies “passenger car VMT” as 1.62 trillion miles - does better job of separating trucks About 16k VMT per household http://www.bts.gov/publications/national_transportation
_statistics/2003/index.html (Table 1-32)
More clever: Cobblers in the US
Cobblers repair shoesOn average, assume 20 min/taskThus 20 jobs / day ~ 5000/yr
How many jobs are needed overall for US?I get shoes fixed once every 5 years
About 280M people in USThus 280M/4 = 56 M shoes fixed/year
56M/5000 ~ 11,000 => 10^4 cobblers in USActual: Census dept says 5,120 in US
An Energy Example
Energy measured in SI units = Watts (as opposed to BTUs, etc)
In practice, we usually talk about kilowatts or kilowatt-hours of energy
Rule: 1 Watt of energy used for one hour is One watt-hour (compound unit) = 1Wh 1000 Watts used for one hour = 1kWh
‘How much energy used by lighting in US residences?’
‘How much energy used by lighting in US residences?’
Assume 50 light fixtures per houseAssume each in use avg 2 hours per dayAssume average fixture is 50WThus each fixture uses 100Wh/dayEach house uses 5000Wh/day (5kWh/day)100 million households would use 500
million kWh/day 182,500 million kWh/yr
‘How much energy used by lighting in US residences?’
Our guess: 182,500 million kWh/yr DOE: “lighting is 5-10% of household elec” http://www.eren.doe.gov/erec/factsheets/eelight.html
2000 US residential Demand ~ 1.2 million million kWh (source below) 10% is 120,000 million kWh 5% is 60,000 million kWh 2000 demand source:
http://www.eia.doe.gov/cneaf/electricity/epm/ epmt44p1.html
A Random Example
Select a random panel of data from the Statistical Abstract of the U.S. Can you formulate an ‘estimation
question’? Can you estimate the answer? How close were you to the ‘actual
answer’?Let’s try this ourselves
Uncertainty
Investment planning and benefit/cost analysis is fraught with uncertainties forecasts of future are highly uncertain applications often made to preliminary designs data is often unavailable
Statistics has confidence intervals – we need them, too
We will talk in more detail about uncertainty in a few weeks.