1. Common Core Math III Unit 1: Statistics

2. We will discuss the following four topics during this unit:
1. Normal Distributions
2. Sampling and Study Design
3. Estimating Population Parameters
4. Expected Value and Fair Game

3. Characteristics of Normal Distribution
symmetric with respect to the mean
mean = median = mode
100% of the data fits under the curve

4. Some New Symbols parameter statistic mean µ proportion p
standard deviation σ s

5. The Normal Distribution Curve
σ = 1
µ = 0

6. Z-Score
The z-score is number of standard deviations (σ) a value is from the mean (µ) on the normal distribution curve.

7. What is the z-score of the value indicated on the curve?

8. What is the z-score of the value indicated on the curve?

9. What is the meaning of a positive z-score?
Instead of estimating, we are given a formula to help us find a precise z-score:
What is the meaning of a positive z-score?
What about a negative z-score?

10. How do you use this?
The mean score on the SAT is 1500, with a standard deviation of The ACT, a different college entrance examination, has a mean score of 21 with a standard deviation of 6.
If Bobby scored 1740 on the SAT and Kathy scored 30 on the ACT, who scored higher?

11. z = 1.5 z = 1 Bobby Kathy Kathy scored higher.
Kathy’s z-score shows that she scored 1.5 standard deviations above the mean.
Bobby only scored 1 standard deviation above the mean.

12. The Empirical Rule
In statistics, the 68–95–99.7 rule, also known as the empirical rule, states that nearly all values lie within three standard deviations of the mean in a normal distribution.

13. 68% of the data falls within ± 1σ

14. 95% of the data falls within ± 2σ

15. 99.7% of the data falls within ± 3σ

16. When you break it up… μ-3σ μ-2σ μ-σ μ μ+σ μ+2σ μ+3σ 34% 34% .15% .15%
13.5%
13.5%
2.35%
2.35%
μ-3σ
μ-2σ
μ-σ μ
μ+σ
μ+2σ
μ+3σ

17. How do you use this?
The scores on the CCM3 midterm were normally distributed. The mean is 82 with a standard deviation of 5. Create and label a normal distribution curve to model the scenario.

18. Draw the curve, add the mean, then add the standard deviations above and below the mean.
34%
34%
.15%
.15%
13.5%
13.5%
2.35%
2.35% 82 87 92 97 77 72 67

19. Find the probability that a randomly selected person:
a. scored between 77 and 87
68%
b. scored between 82 and 87
34%
c. scored between 72 and 87
81.5%
d. scored higher than 92
2.5%
e. scored less than 77
16%

20. You might be wondering…
what happens if you’re looking for scores that are not perfect standard deviations away from the mean?
normalcdf (lower bound, upper bound, µ, σ)

21. b. What’s the probability that a randomly
a. What’s the probability that a randomly
selected student scored between 80 and 90?
normalcdf (80, 90, 82, 5) =
b. What’s the probability that a randomly
selected student scored below 70?
normalcdf (0, 70, 82, 5) =
c. What’s the probability that a randomly
selected student scored above 79?
normalcdf (79, 100, 82, 5) =

22. You can also work backward to find percentiles!
d. What score would a student need in order to be in the 90th percentile?
invnorm (percent of area to left, , )
invnorm (0.9, 82, 5) = 88.41, or 89

23. e. What score would a student need in order to be in top 20% of the class?
invnorm (0.8, 82, 5) = 86.21, or 87

24. The average waiting time at Walgreen’s drive-through window is 7
The average waiting time at Walgreen’s drive-through window is 7.6 minutes, with a standard deviation of 2.6 minutes. When a customer arrives at Walgreen’s, find the probability that he will have to wait
a. between 4 and 6 minutes
b. less than 3 minutes
c. more than 8 minutes
d. Only 8% of customers have to wait longer than Mrs. Jones. Determine how long Mrs. Jones has to wait.
0.186
0.037
0.439
11.25 minutes

25. Questions about normal distribution?

26. Sampling and Study Design

27. Main Questions
What’s the difference between an experiment and an observational study?
What are the different ways that a sample can be collected?
When is a sample considered random?
What is bias and how does it affect the data you collect?

28. There are three ways to collect data:
1. Surveys
2. Observational Studies
3. Experiments

29. Surveys Surveys most often involve the use of a questionnaire to
measure the
characteristics and/or attitudes of people.
ex. asking students their opinion about extending the school day

30. Observational Studies
Individuals are observed and certain outcomes are measured, but no attempt is made to affect the outcome.

31. Correlation is not causation!
Experiments
Treatments are imposed prior to observation. Experiments are the only way to show a cause-and-effect relationship.
Remember:
Correlation is not causation!

32. Observational Study or Experiment?
Fifty people with clinical depression were divided into two groups. Over a 6 month period, one group was given a traditional treatment for depression while the other group was given a new drug. The people were evaluated at the end of the period to determine whether their depression had improved.
Experiment

33. Observational Study or Experiment?
To determine whether or not apples really do keep the doctor away, forty patients at a doctor’s office were asked to report how often they came to the doctor and the number of apples they had eaten recently.
Observational Study

34. Observational Study or Experiment?
To determine whether music really helped students’ scores on a test, a teacher who taught two U. S. History classes played classical music during testing for one class and played no music during testing for the other class.
Experiment

35. Types of Sampling
In order to collect data, we must choose a sample, or a group that represents the population. The goal of a study will determine the type of sampling that will take place.

36. Simple Random Sample (SRS)
All individuals in the population have the same probability of being selected, and all groups in the sample size have the same probability of being selected.

37. Putting 100 kids’ names in a hat and picking out 10 - SRS
Putting 50 girls’ names in one hat and 50 boys’ names in another hat and picking out 5 of each – not a SRS

38. Stratified Random Sample
If a researcher wants to highlight specific subgroups within the population, they divide the entire population into different subgroups, or strata, and then randomly selects the final subjects proportionally from the different strata.

39. Systematic Random Sample
The researcher selects a number at random, n, and then selects every nth individual for the study.

40. Convenience Sample
Subjects are taken from a group that is conveniently accessible to a researcher, for example, picking the first 100 people to enter the movies on Friday night.

41. Cluster Sample
The entire population is divided into groups, or clusters, and a random sample of these clusters are selected. All individuals in the selected clusters are included in the sample.

42. Name that sample!
The names of 70 contestants are written on 70 cards, the cards are placed in a bag, the bag is shaken, and three names are picked
from the bag.
Simple random sample
Stratified sample
Convenience sample
Cluster sample
Systematic sample

43. Name that sample!
To avoid working late, the quality control manager inspects the last 10 items produced that day.
Simple random sample
Stratified sample
Convenience sample
Cluster sample
Systematic sample

44. Name that sample!
A researcher for an airline interviews all of the passengers on five randomly selected flights.
Simple random sample
Stratified sample
Convenience sample
Cluster sample
Systematic sample

45. Name that sample!
A researcher randomly selects and interviews fifty male and fifty female teachers.
Simple random sample
Stratified sample
Convenience sample
Cluster sample
Systematic sample

46. Name that sample!
Every fifth person boarding a plane is searched thoroughly.
Simple random sample
Stratified sample
Convenience sample
Cluster sample
Systematic sample

47. Types of Bias in Survey Questions
Bias occurs when a sample systematically favors one outcome.
1. Question Wording Bias
In 1992, a Roper poll conducted for the American Jewish Community of the Holocaust asked: “Does it seem possible or does it seem impossible to you that the Nazi extermination of the Jews never happened?”

48. The use of double negatives in this question caused confusion in the way people responded to the survey. 22% of those surveyed said that it was possible that the holocaust did not occur.
This is an example of question wording bias!

49. Later, a new survey was conducted in which the question was rephrased:
“Does is seem possible to you that the Nazi extermination of the Jews never happened, or do you feel certain that it happened?”
In the new survey, only 1% of those surveyed stated that it was possible that the holocaust never occurred.

50. 2. Undercoverage bias occurs when the sample is not representative of the population.
3. Response bias occurs when survey respondents lie or misrepresent themselves.
4. Nonresponse bias occurs when an individual is chosen to participate, but refuses.
5. Voluntary response bias occurs when people are asked to call or mail in their opinion.

51. Name that bias! On the twelfth anniversary of the
death of Elvis Presley, a Dallas
record company sponsored a
national call-in survey. Listeners of
over 1000 radio stations were asked
to call a number (at a charge of $2.50) to voice an opinion concerning whether or not Elvis was really dead. It turned out that 56% of the callers felt Elvis was alive.
Voluntary response bias

52. Name that bias! Undercoverage bias
In 1936, Literary Digest magazine conducted the most extensive public opinion poll in history to date. They mailed out questionnaires to over 10 million people whose and addresses they had obtained from telephone books and vehicle registration lists. More than 2.4 million people responded, with 57% indicating that they would vote for Republican Alf Landon in the upcoming Presidential election. However, Democrat Franklin Roosevelt won the election, carrying 63% of the popular vote.
Undercoverage bias

53. Why is this question biased?
Do you think the city should risk an increase in pollution by allowing expansion of the Northern Industrial Park?
Can you rephrase it to remove the bias?

54. Why is this question biased?
If you found a wallet with $100 in it on the street, would you do the honest thing and return it to the person or would you keep it?
Can you rephrase it to remove the bias?

55. Questions about sampling?

56. Estimating Population Parameters
Vocabulary for this lesson is important!
Parameter a value that represents a population
Statistic a value based on a sample and used to estimate a parameter

57. population
sample
parameter
statistic
mean
proportion p
standard deviation σ s

58. You poll 1,000 people and ask who they will vote for president
You poll 1,000 people and ask who they will vote for president say they'll vote for Obama, while 450 say they'll vote for Romney.     500/1000 = 50%     450/1000 = 45%
Would you think that overall, Obama leads 50% to 45%?  
Would you think overall Romney is down 5 points?
 If you said, "yes" to either, then you are not correct.  Now you might ask, why?

59. You only sampled 1000 people!
Even if you picked a sample that was a perfect representation of the population, the outcome might not accurately reflect the population. There will be some error!

60. Finding a Margin of Error
Margin of error is a “cushion” around a statistic.
ME =
n = sample size

61. Suppose that 900 American teens were surveyed about their favorite event of the Winter Olympics. Ski jumping was the favorite of 20% of those surveyed. This result can be used to predict the proportion of ALL American teens who favor ski jumping.
We can confidently state that the true proportion of American teens who favor ski jumping falls between 17% and 23%.

62. How does sample size affect margin of error?
If your sample size is 400 and you wish to cut the margin of error in half, what will your new sample size be?
1600

63. What sample size produces a given margin of error?
If you want your margin of error to be 2%, what size sample will you need?
2500

64. Simulation Why run a simulation?
Simulation is a way to model random events, so that simulated outcomes closely match real-world outcomes.
Why run a simulation?
Some situations may be difficult, time-consuming, or expensive to analyze. In these situations, simulation may approximate real-world results while requiring less time, effort, or money.

65. Carole and John are playing a dice game
Carole and John are playing a dice game. Carole believes that she can roll six dice and get each number, one through six, on a single roll. John knows the probability of this occurrence is low. He bets Carole that he will wash her car if she can get the outcome she wants in twenty tries.

66. BEFORE YOU START!
You are running 20 trials, so make 20 blanks on your paper. This will keep you from losing count of how many trials you’ve run. It also makes recording easy!
__ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __     

67. What is the problem that we are simulating?
Can Carole get one of each number in a roll of six dice?
What random device will you use to simulate the problem and how will you use it?
We will use the calculator to generate random numbers.

68. Seeding
Since a calculator is a type of computer, it can never be truly random.
For this reason, we can configure our calculators to give everyone the same set of “random” data (so we can all work together!).
The process of calibrating our calculators in this way is called seeding.

69. How to seed the calculator:

70. How will you conduct each trial? How many trials will you conduct?
I will use the randInt( command in my calculator to generate random integers.
randInt(min value, max value, number of data in set)
randInt(1, 6, 6)

71. What are the results of these trials?
We received all 6 numbers only 1 out of 20 times.
What predictions can be made based on these results?
There’s approximately a 5% chance of this occurring.
The more trials you run, the closer you will get to the theoretical probability (Law of Large Numbers).

72. On a certain day the blood bank needs 4 donors with type O blood
On a certain day the blood bank needs 4 donors with type O blood.  If the hospital brings in groups of five, what is the probability that a group would arrive that satisfies the hospitals requirements, assuming that 45% of the population has type O blood?   
Let 1-45 represent people with type O blood.
Let represent people with other blood types.
Remember to seed the calculator to 5!
Then, run RandInt(1, 100, 5) twenty times.
Record how many trials satisfy the hospital’s requirements.

73. To seed your calculator:
MATH, PRB, 5, STO →, RAND, ENTER
To run the simulation:
MATH, PRB, RandInt(1, 100, 5), ENTER
Five of the twenty groups have four or more members with type O blood. Therefore, there is a 25% chance that they hospital will get the Type O blood they need.

74. Questions about simulations?

75. Expected Value and Fair Games

76. Expected Value
Expected value is the weighted average of all possible outcomes.
For example, a trial has the outcomes 10, 20 and 60.
The average of 10, 20, and 60 = 30
This assumes an even distribution:   

77. Sometimes, outcomes will not have equal likelihoods.X 1 2 3
P(X) .5
.25
E(X) = .5(1) + .25(2) + .25(3) = 1.75

78. You play a game in which you roll one fair die
You play a game in which you roll one fair die. If you roll a 6, you win $5. If you roll a 1 or a 2, you win $2. If you roll anything else, you lose.
Create a probability model for this game:   6 
1, 2 
3, 4, 5  X 
$5 
$2 
$0 
P(X) 
1/6 
1/3 
1/2 
What would you be willing to pay to play?
E(X) = 5(1/6) + 2(1/3) + 0(1/2) = 1.50
A price of $1.50 makes this a fair game.

79. E(X) = .05(3500) + .1(2500) + .25(500)+.6(-1000) = -$50.
At Tucson Raceway Park, your horse, My Little Pony, has a probability of 1/20 of coming in first place, a probability of 1/10 of coming in second place, and a probability of ¼ of coming in third place. First place pays $4,500 to the winner, second place $3,500 and third place $1,500. Is it worthwhile to enter the race if it costs $1,000?
1st
2nd
3rd
Other X
$3500
$2500
$500
-$1000
P(X)
.05
.10
.25
.60
E(X) = .05(3500) + .1(2500) + .25(500)+.6(-1000) = -$50.

80. This is the Law of Large Numbers!
What does an expected value of -$50 mean?
Its important to note that nobody will actually lose $50—this is not one of the options. Over a large number of trials, this will be the average loss experienced.
This is the Law of Large Numbers!
Insurance companies and casinos build their businesses based on the law of large numbers.

81. Questions about expected value?

82. questions about statistics?
Any
questions about statistics?