LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North...

38
LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Basics of Statistics Created by The North Carolina School of Science and Math for North Carolina Department of Public Instruction .

Transcript of LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North...

Page 1: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

LESSON ONEDECISION ANALYSIS

Subtopic 2 – Basic Concepts from Statistics

Basics of Statistics

Created by The North Carolina School of Science and Math for North Carolina Department of Public Instruction.

Page 2: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Today’s Menu

• Probability

• Expected Value

• Time and Discounting

Basics ofStatistics

Page 3: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Basics: Probability

• What is probability? 3 philosophies

• How do people talk about probability?

By Liam Quin, Licensed CC-BY-3.0, via Wikimedia Commons. http://upload.wikimedia.org/wikipedia/commons/5/59/Five_ivory_dice.jpg

Page 4: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

History: Probability First book on probability Modern probability math

Christiaan Huygens Andrey Kolmogorov

(Dutch, 1629-1695) (Russian, 1903-1987)

Page 5: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Axioms of Probability

• Also known as Kolmogorov Axioms

• AXIOM 1 - Probabilities cannot be negative.

• AXIOM 2 - The probability of the set of all possible outcomes is equal to one.

• AXIOM 3 - The probability of a collection of mutually exclusive events is the sum of the individual probabilities of those events.

Page 6: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Axioms of Probability

Page 7: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Axioms of Probability

Page 8: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Axioms of Probability

“or”

Page 9: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Axioms of Probability

• Also known as Kolmogorov Axioms

• AXIOM 1 - Probabilities cannot be negative.

• AXIOM 2 - The probability of the set of all possible outcomes is equal to one.

• AXIOM 3 - The probability of a collection of mutually exclusive events is the sum of the individual probabilities of those events.

Page 10: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Example

Page 11: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Conditional Probability

Page 12: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Independence

Page 13: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Conditional Probability

“and”

Page 14: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Conditional probability example

• Let E1 = {outcome is odd} and E2 = {outcome is

6}. Find P(E2|E1). Find P(E2|not E1).

“and”

Page 15: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Conditional probability example

• Let E1 = {outcome is odd} and E2 = {outcome is

6}. P(E2|E1) = 0/(1/2) = 0. P(E2|not E1)

• = (1/6)/(1/2) = 1/3

“and”

Page 16: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Conditional Probability

“and”

Page 17: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Important note• How to assign probabilities to events is a topic in

statistics (and philosophy).

• Regardless of the method (event space, relative

frequency, or subjective) that generated those

probabilities, once we believe them, the math for

using probabilities in decision making is

always the same.

Page 18: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Beliefs!

• Let B() be a belief function that assigns numbers

to statements such that the higher the number, the

stronger is the degree of belief.

• Beliefs are directly related to probabilities!

• If something is more probable, beliefs that it is

true are stronger than if it is less probable.

Page 19: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Beliefs and Axioms

• Examples: Let F, G, H be events

• Interpret: B(F) > B(G) and B(F|H) > B(G|H)

• Turns out that belief functions

can be constructed out of the

probability axioms.

• Experimentally, we can infer

beliefs by analyzing bets.

Page 20: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Expected value

Page 21: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Expected value examples

• Find the expected value of the face numbers on

one toss of a fair die.

Page 22: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Expected value examples• Find the expected value of the face numbers on

one toss of a fair die. Answer: X1 = 1, X2 = 2,

…, X6 = 6. All have probability 1/6 (fair die).

E(X) = 1(1/6)+2(1/6)

+ 3(1/6) + 4(1/6) + 5(1/6)

+ 6(1/6) = 3.5

Page 23: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Expected value examplesSuppose the prize for beating a chess grandmaster is

$2000, but you have to pay $5 for the opportunity to play

against him. Imagine you’re good at chess, but

not great, so you think it’s only 0.8%

(0.008) likely that you’ll beat him.

Who here would take those odds?

Page 24: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Expected value examplesSuppose the prize for beating a chess grandmaster is

$2000, but you have to pay $5 for the opportunity to play

against him. Imagine you’re good at chess, but

not great, so you think it’s only 0.8%

likely that you’ll beat him. What

is your expected profit/loss from

challenging him?

Page 25: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Expected value examplesX1(lose) = -$5; P(X1) = 0.992

X2(win) = $1995; P(X2) = 0.008

E(X) = X1*P(X1)+X2*P(X2)

= -$5*0.992 + $1995*0.008

= $11.00

Page 26: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Expected value

Page 27: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Discounting• Given an interest rate i = 0.03 (3%) per annum compounded annually, which is the best deal? Let’s guess by show of hands!

• A) $100 000 right now

• B) $104 000 in 18 months

• C) $117 000 in 5 years

• D) $152 000 in 15 years

Page 28: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Discounting

But they’re all at

different points

in time!

What to do??

Page 29: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Discounting

Trick to figuring it out:

Move all of the values to the

same point in time

Page 30: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Discounting• Formula:

• i - interest rate

• n - number of compounding periods

• PV - present value, or value at n = 0

• FV - future value, or value at some n > 0

Page 31: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Discounting• Given an interest rate i = 0.03 (3%) per annum compounded annually, which is the best deal?

• A) $100 000 right now

• B) $104 000 in 18 months

• C) $117 000 in 5 years

• D) $152 000 in 15 years

Page 32: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Solutions

A) PV is given: $100 000

Page 33: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Solutions

B) FV = $104 000, n = 1.5, i = 0.03

Therefore, PV = $99 489.56

Page 34: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Solutions

C) FV = $117 000, n = 5, i = 0.03

Therefore, PV = $100 925.22

Page 35: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Solutions

D) FV = $152 000, n = 15, i = 0.03

Therefore, PV = $97 563.02

Page 36: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Solutions

Best deal is (C), which gives the highest PV.

Page 37: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Discussion: Applications• Which spheres of human endeavor can the

study of decision-making inform?

• What would you guess are some academic

topics being studied in this area?

• What are some questions related to decision-

making that you find interesting?

Page 38: LESSON ONE DECISION ANALYSIS Subtopic 2 – Basic Concepts from Statistics Created by The North Carolina School of Science and Math forThe North Carolina.

Homework 1

• Aim: practice using the concepts from this

lesson.