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Probability. Quantitative Methods in HPELS 440:210. Agenda. Introduction Probability and the Normal Distribution Probability and the Binomial Distribution Inferential Statistics. Introduction. Recall: Inferential statistics: Sample statistic  PROBABILITY  population parameter

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probability

Probability

Quantitative Methods in HPELS

440:210

agenda
Agenda
  • Introduction
  • Probability and the Normal Distribution
  • Probability and the Binomial Distribution
  • Inferential Statistics
introduction
Introduction
  • Recall:
    • Inferential statistics: Sample statistic  PROBABILITY  population parameter
  • Marbles Example
slide4
Assume:

N = 100 marbles

50 black, 50 white

Assume:

N = 100 marbles

90 black, 10 white

What is the probability of drawing a black marble?

What is the probability of drawing a black marble?

introduction1
Introduction
  • Using information about a population to predict the sample is the opposite of INFERENTIAL statistics
  • Consider the following examples
slide6
While blindfolded, you choose n=4 marbles from one of the two jars

Which jar did you PROBABLY choose your sample?

introduction2
Introduction
  • What is probability?
    • The chance of any particular outcome occurring as a fraction/proportion of all possible outcomes
  • Example:
    • If a hat is filled with four pieces of paper lettered A, B, C and D, what is the probability of pulling the letter A?
    • p = # of “A” outcomes / # of total outcomes
    • p = 1 / 4 = 0.25 or 25%
introduction3
Introduction
  • This definition of probability assumes that the samples are obtained RANDOMLY
  • A random sample has two requirements:
    • Each outcome has equal chance of being selected
    • Probability is constant (selection with replacement)
slide9
What is probability of drawing Jack of Diamonds from 52 card deck? Ace of spades?

What is probability of drawing Jack of Spades if you do not replace the first selection?

agenda1
Agenda
  • Introduction
  • Probability and the Normal Distribution
  • Probability and the Binomial Distribution
  • Inferential Statistics
probability normal distribution
Probability  Normal Distribution
  • Recall  Normal Distribution:
    • Symmetrical
    • Unified mean, median and mode
  • Normal distribution can be defined:
    • Mathematically (Figure 6.3, p 168)
    • Standard deviations (Figure 6.4, 168)
slide13
With either definition, the predictability of the Normal Distribution allows you to answer PROBABILITY QUESTIONS
probability questions
Probability Questions
  • Example 6.2
  • Assume the following about adult height:
    • µ = 68 inches
    •  = 6 inches
  • Probability Question:
    • What is the probability of selecting an adult with a height greater than 80 inches?
    • p (X > 80) = ?
probability questions1
Probability Questions
  • Example 6.2:
  • Process:
    • Draw a sketch:
    • Compute Z-score:
    • Use normal distribution to determine probability
slide16
Step 1: Draw a sketch for p(X>80)

Step 2: Compute Z-score:

Z = X - µ / 

Z = 80 – 68/6

Z = 12/6 = 2.00

Step 3: Determine probability

There is a 2.28% probability that you would select a person with a height greater than 80 inches.

probability questions2
Probability Questions
  • What if Z-score is not 0.0, 1.0 or 2.0?
  • Normal Table  Figure 6.6, p 170
slide18
Column A: Z-score Column C: Tail = smaller side

Column B: Body = larger side Column D: 0.50 – p(Z)

using the normal table
Using the Normal Table
  • Several applications:
    • Determining a probability from a specific Z-score
    • Determining a Z-score from a specific probability or probabilities
    • Determining a probability between two Z-scores
    • Determining a raw score from a specific probability or Z-score
determining a probability from a specific z score
Determining a probability from a specific Z-score
  • Process:
    • Draw a sketch
    • Locate the probability from normal table
  • Examples: Figure 6.7, p 171
slide21
p(X > 1.00) = ?

Tail or Body?

p = 15.87%

p(X < 1.50) = ?

Tail or Body?

p = 93.32%

p(X < -0.50) = ?

p(X > 0.50) = ?

Tail or Body?

p = 30.85%

using the normal table1
Using the Normal Table
  • Several applications:
    • Determining a probability from a specific Z-score
    • Determining a Z-score from a specific probability or probabilities
    • Determining a probability between two Z-scores
    • Determining a raw score from a specific probability or Z-score
determining a z score from a specific probability
Process:

Draw a sketch

Locate Z-score from normal table

Examples: Figure 6.8a and b, p 173

Determining a Z-score from a specific probability
slide24
20%

(0.200)

20%

(0.200)

What Z-score is associated with a raw score that has 90% of the population below and 10% above?

What two Z-scores are associated with raw scores that have 60% of the population located between them and 40% located on the ends?

Column B (body)  p = 0.900

Z = 1.28

Column C (tail)  p = 0.100

Z = 1.28

Column C (tail)  p = 0.200

Z = 0.84 and -0.84

Column D (0.500 – p(Z))  0.300

Z = 0.84 and – 0.84

30%

(0.300)

30%

(0.300)

using the normal table2
Using the Normal Table
  • Several applications:
    • Determining a probability from a specific Z-score
    • Determining a Z-score from a specific probability or probabilities
    • Determining a probability between two Z-scores
    • Determining a raw score from a specific probability or Z-score
determining a probability between two z scores
Determining a probability between two Z-scores
  • Process:
    • Draw a sketch
    • Calculate Z-scores
    • Locate probabilities normal table
    • Calculate probability that falls between Z-scores
  • Example: Figure 6.10, p 176
    • What proportion of people drive between the speeds of 55 and 65 mph?
slide27
Step 1: Sketch

Step 2: Calculate Z-scores:

Z = X - µ /  Z = X - µ / 

Z = 55 – 58/10 Z = 65 – 58/10

Z = -0.30 Z = 0.70

Step 2: Locate probabilities

Z = -0.30 (column D) = 0.1179

Z = 0.70 (column D) = 0.2580

Step 4: Calculate probabilities between Z-scores

p = 0.1179 + 0.2580 = 0.3759

using the normal table3
Using the Normal Table
  • Several applications:
    • Determining a probability from a specific Z-score
    • Determining a Z-score from a specific probability or probabilities
    • Determining a probability between two Z-scores
    • Determining a raw score from a specific probability or Z-score
determining a raw score from a specific probability or z score
Determining a raw score from a specific probability or Z-score
  • Process:
    • Draw sketch
    • Locate Z-score from normal table
    • Calculate raw score from Z-score equation
  • Example: Figure 6.13, p 178
    • What SAT score is needed to score in the top 15%?
slide30
Step 1: Sketch

Step 2: Locate Z-score

p = 0.150 (column D)

Z = 1.04

Step 3: Calculate raw score from Z-score equation

Z = X - µ /   X = µ + Z 

X = 500 + 1.04(100)

X = 604

agenda2
Agenda
  • Introduction
  • Probability and the Normal Distribution
  • Probability and the Binomial Distribution
  • Inferential Statistics
probability binomial distribution
Probability  Binomial Distribution
  • Binomial distribution?
    • Literally means “two names”
    • Variable measured with scale consisting of:
      • Two categories or
      • Two possible outcomes
  • Examples:
    • Coin flip
    • Gender
probability questions binomial distribution
Probability Questions  Binomial Distribution
  • Binomial distribution is predictable
  • Probability questions are possible
  • Statistical notation:
    • A and B: Denote the two categories/outcomes
    • p = p(A) = probability of A occurring
    • q = p(B) = probability of B occurring
  • Example 6.13, p 185
slide34
If you flipped the coin twice (n=2), how many combinations are possible?

Heads

p = p(A) = ½ = 0.50

Heads

Heads

Tails

q = p(B) = ½ = 0.50

Heads

Tails

Each outcome has an equal chance of occurring  ¼ = 0.25

Tails

Heads

What is the probability of obtaining at least one head in 2 coin tosses?

Figure 6.19, p 186

Tails

Tails

normal approximation binomial distribution
Normal Approximation  Binomial Distribution
  • Binomial distribution tends to be NORMAL when “pn” and “qn” are large (>10)
  • Parameters of a normal binomial distribution:
    • Mean: µ = pn
    • SD:  = √npq
  • Therefore:
    • Z = X – pn / √npq
normal approximation binomial distribution1
Normal Approximation  Binomial Distribution
  • To maximize accuracy, use REAL LIMITS
  • Recall:
    • Upper and lower
    • Examples: Figure 6.21, p 188
slide38
Note: The binomial distribution is a histogram, with each bar extending to its real limits

Note: The binomial distribution approximates a normal distribution under certain conditions

normal approximation binomial distribution2
Normal Approximation  Binomial Distribution
  • Example: 6.22, p 189
  • Assume:
    • Population: Psychology Department
    • Males (A) = ¼ of population
    • Females (B) = ¾ of population
  • What is the probability of selecting 14 males in a sample (n=48)?
    • p(A=14)  p(13.5
normal approximation binomial distribution3
Normal Approximation  Binomial Distribution
  • Process:
    • Draw a sketch
    • Confirm normality of binomial distribution
    • Calculate population µ and :
      • µ = pn
      •  = √npq
    • Calculate Z-scores for upper and lower real limits
    • Locate probabilities in normal table
    • Calculate probability between real limits
slide41
Step 1: Draw a sketch

Step 2: Confirm normality

pn = 0.25(48) = 12 > 10

qn = 0.75(48) = 36 > 12

Step 3: Calculate µ and 

µ = pn  = √npq

µ = 0.25(48) = √48*0.25*0.75

µ = 12  = 3

Step 5: Locate probabilities

Z = 0.50 (column C) = 0.3085

Z = 0.83 (column C) = 0.2033

Step 4: Calculate real limit Z-scores

Z = X–pn/√npq Z = X-pn/√npq

Z = 13.5-12/3 Z = 14.5-12/3

Z = 0.50 Z = 0.83

slide42
Z = 0.50 (column C) = 0.3085

Z = 0.83 (column C) = 0.2033

Step 6: Calculate probability between the real limits

p = 0.3085 – 0.2033

p = 0.1052

There is a 10.52% probability of selecting 14 males from a sample of n=48 from this population

normal approximation binomial distribution4
Normal Approximation  Binomial Distribution
  • Example extended
  • What is the probability of selecting more than 14 males in a sample (n=48)?
    • p(A>14)  p(A>14.5) = ?
  • Process:
    • Draw a sketch
    • Calculate Z-score for upper real limit
    • Locate probability in normal table
slide44
Step 1: Draw a sketch

Step 2: Calculate Z-score of upper real limit

Z = X–pn/√npq

Z = 14.5 – 12 / 3

Z = 0.83

There is a 20.33% probability of selecting more than 14 males in a sample of n=48 from this population

Step 3: Locate probability

Z = 0.83 (column C) = 0.2033

agenda3
Agenda
  • Introduction
  • Probability and the Normal Distribution
  • Probability and the Binomial Distribution
  • Inferential Statistics
looking ahead inferential statistics
Looking Ahead  Inferential Statistics
  • PROBABILITY links the sample to the population  Figure 6.24, p 191
textbook assignment
Textbook Assignment
  • Problems: 1, 3, 6, 8, 12, 15, 17, 27
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