Class 15 the central limit theorem
This presentation is the property of its rightful owner.
Sponsored Links
1 / 12

Class 15. The Central Limit Theorem PowerPoint PPT Presentation


  • 74 Views
  • Uploaded on
  • Presentation posted in: General

P 288. Class 15. The Central Limit Theorem. Sprigg Lane. Confidence Interval for the mean. If you know and s. There is a 95% p this interval will cover μ . 95% confidence interval for . Standard error goes down with 1/. 2T inv t-value goes down as dof goes up…slowly.

Download Presentation

Class 15. The Central Limit Theorem

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Class 15 the central limit theorem

P 288

Class 15. The Central Limit Theorem

Sprigg Lane


Class 15 the central limit theorem

Confidence Interval for the mean

If you know and s

There is a 95% pthis interval will cover μ.

95% confidence interval for

Standard error goes down with 1/

2T inv t-value goes down as dof goes up…slowly.

Confidence interval gets narrower with n.

In this example, we kept sample mean and sample standard deviation constant.


Hypothesis tests

Hypothesis Tests

  • Hypotheses about p’s

    • Binomial (she’s guessing)

    • Normal approximation when n is big (Wunderdog)

    • CHI-squared goodness of fit (Roulette Wheel)

    • CHI-squared independence (Supermarket Survey)

  • Hypotheses about means

    • One-sample z-test (IQ μ=100 with σ=15)

    • One-sample t-test (IQ μ=100)

    • Two-sample t-test (heights μM = μF)

    • Two-sample paired t-test (Weight before and after)

    • ANOVA single factor (heights for three IT groups)


Using excel function to calculate p values

Using Excel function to calculate p-values

  • =norm.dist(X,μ,σ,true)

  • =norm.s.dist(Z,true)

  • =t.dist(T,dof,true)

  • =chisq.dist(chi2,dof,true)

  • =t.dist.2t(T,dof)

  • =t.dist.rt(T,dof)

  • =chidist(chi2,dof)

  • =chisq.dist.rt(chi2,dof)

The first four are LEFT TAIL

The last three are RIGHT TAIL


Sprigg lane

Sprigg Lane

  • Sprigg Lane is an Investment Company

  • The Bailey Prospect is the site of a potential well that has a 90% probability of natural gas.

  • Federal Tax laws were recently changed to encourage development of energy.

  • The Bailey prospect will be packaged with 9 other similar wells

    • Sprigg Lane plans to sell a large portion of the package to outside investors.


Bailey prospect uncertainties

Bailey Prospect Uncertainties

  • Total Well Cost

    • $160K +/- $5,400 (95% probability, normal)

  • Enough Gas there to proceed?

    • P=0.9

  • Initial Amount in million cubic feet?

    • lognormal(33,4.93)

  • Btu content?

    • 1055 to 1250 with 1160 most likely (BTU per cubic feet)

  • Production Decline Rate multiplier

    • .5 to 1.75 with 1 most likely

  • Average Inflation (affecting costs and future gas prices)

    • Normal(0.035,0.005)


Best guess valuation

Best-Guess Valuation


Analysis agenda

Analysis Agenda

  • Analyze the riskiness of the baily prospect project

    • Replace each of the six uncertainties with a probability distribution

    • Find out the resulting probability distribution of NPV.

  • Analyze the riskiness of a 1/10th share of an investment package of ten wells.

    • This will be the distribution of a sample average of ten NPVs.


Summary the properties of the npv of the bailey prospect

Summary: The properties of the NPV of the Bailey prospect

NPV is a random variable

is a random variable

$82,142

The same

The mean is

$77,430

$77,430/

The standard deviation is

Weird and not normal

Close to normal

The distribution is


The probability distribution of npv

The probability distribution of NPV


The probability distribution of 1 10th share of ten identical wells

The probability distribution of 1/10th share of ten “identical” wells


Central limit theorem

Central Limit Theorem

P 288

In selecting simple random samples of size n from a population, the sampling distribution of the sample mean can be approximated by the normal distribution as the sample size becomes large.

Implications of the CLT

If the population (underlying probability distribution) is normal, our tests of hypotheses about means WORK FINE.

If the population (underlying probability distribution) is NOT normal, our tests will still work fine if n is big (>30 is a rule of thumb).


  • Login