1. Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Spring 2017 Room 150 Harvill Building 9:00 - 9:50 Mondays, Wednesdays & Fridays.
Welcome

2. A note on doodling

3. By the end of lecture today 3/1/17
Confidence Intervals
Review for Exam 2

4. Review previous homeworks to help prepare for Exam 2
No new homework
Review previous homeworks to help prepare for Exam 2

5. Before next exam (March 3rd)
Schedule of readings
Before next exam (March 3rd)
Please read chapters in OpenStax textbook
Please read Chapters 10, 11, 12 and 14 in Plous
Chapter 10: The Representativeness Heuristic
Chapter 11: The Availability Heuristic
Chapter 12: Probability and Risk
Chapter 14: The Perception of Randomness

6. Lab sessions Everyone will want to be enrolled
in one of the lab sessions
Labs
Exam 2 Prep

8. What are they for? Confidence Intervals:
Combining these three skills to build confidence intervals
What are they for? Confidence Intervals:
We are estimating a value but providing two scores between which we believe the true value lies. We can be 95% confident that our mean falls between these two scores.
Central Limit Theorem
Central Limit Theorem highlights importance of standard error of the mean (which is built on standard deviation)
Using standardized scores (z scores) to find raw score values that border the middle part of the curve.
Building towards confidence intervals

9. Calculating Two Scores that Border Middle 95% of curve
Step 1: Find mean (expected value)
x is mean of sample
Step 2: Find standard deviation and standard error of the mean
“σ” if population
Step 3: Decide on area of interest
- Middle 95% α = .05  z = 1.96
- Middle 99% α = .01  z = 2.58
Step 4: Calculations for confidence intervals
Step 5: Conclusion
- tie findings with statement about what you are estimating

10. 95% ? ? Find the scores that border the middle 95%
Mean = 50 Standard deviation = 10
Find the scores that border
the middle 95% ? ?
95%
x = mean ± (z)(standard deviation)
30.4
69.6
.9500
.4750
.4750
Please note:
We will be using this same logic for “confidence intervals” ? ?
1) Go to z table - find z score for
for area .4750
z = 1.96
2) x = mean + (z)(standard deviation)
x = 50 + (-1.96)(10)
x = 30.4
30.4
3) x = mean + (z)(standard deviation)
x = 50 + (1.96)(10)
x = 69.6
69.6
Scores capture the
middle 95% of the curve

11. Calculating Confidence Intervals
Step 1: Find mean (expected value)
x is mean of sample
Step 2: Find standard deviation and standard error of the mean
“σ” if population
Step 3: Decide on level of confidence
- 95% Confident α = .05  z = 1.96
- 99% Confident α = .01  z = 2.58
Step 4: Calculations for confidence intervals
Step 5: Conclusion
- tie findings with statement about what you are estimating

12. σ n 95% ? ? 10 = = √ 100 √ Construct a 95% confidence interval
Mean = 50 Standard deviation = 10
n = 100
s.e.m. = 1 ? ?
95%
48.04
51.96 ?
.9500
.4750
For “confidence intervals”
same logic – same z-score
But - we’ll replace standard deviation with the standard error of the mean
standard error
of the mean σ √ n = 10 =
x = mean ± (z)(s.e.m.) √
100
x = 50 + (1.96)(1)
x =
x = 50 + (-1.96)(1)
x =
95% Confidence Interval
is captured by the scores – 51.96

13. ? ? Construct a 95 percent confidence interval around the mean
95% ?
We know this
raw score = mean ± (z score)(s.d.)
Some Mean Some Variability
We used this one when finding raw scores
associated with an area under the curve.
We had all population info. Not really a
“confidence interval” because we know the mean of the population,
so there is nothing to estimate or be “confident about”.
Hint always draw a picture!
We used this one when finding raw scores associated with an
area under the curve. We used this to provide an interval within
which we believe the mean falls. We have some level of confidence about our guess. We know the population standard deviation.
raw score = mean ± (z score)(s.e.m.)
Similar, but uses
standard error the mean
based on population s.d.

14. Confidence interval uses SEM
Homework Worksheet:
Confidence interval uses SEM

15. .99 2.58 sd 2.58 sd ? 55 ? Homework Worksheet: Problem 1
29.2
Upper boundary raw score
x = mean + (z)(standard deviation)
x = 55 + (+ 2.58)(10)
x = 80.8
80.8
Lower boundary raw score
x = mean + (z)(standard deviation)
x = 55 + (- 2.58)(10)
x = 29.2
Standard deviation = 10
Mean = 55
2.58 sd
2.58 sd
.99
29.2 ? 55
80.8 ?

16. .99 49 2.58 sem 2.58 sem 1.42 ? 55 ? Homework Worksheet: Problem 1
29.2
Upper boundary raw score
x = mean + (z)(standard error mean)
x = 55 + (+ 2.58)(1.42)
x = 58.7
80.8
51.3
58.7
Lower boundary raw score
x = mean + (z)(standard error mean)
x = 55 + (- 2.58)(1.42)
x = 51.3
Standard deviation = 10
Mean = 55 10 49
2.58 sem
2.58
sem
1.42
.99
51.3 ? 55
58.7 ?

17. Homework Worksheet: Problem 5
29.2
80.8
51.3
58.7
10.2
29.8
16.9
23.1
4.09
13.11
8.02
9.18
2.67
7.8
14.5
9.4
WIDER!

18. Exam 2 Review

19. Let’s try one

20. Let’s try one Correct Answer
Alia is looking at a normal distribution and wants to know what proportion of the
distribution that falls between 1 and -1 standard deviations from the mean. What is the correct proportion? a b c d
Correct
Answer
Let’s try one

21. This will be so helpful now that we know these by heart
1 sd above and below mean
68%
2 sd above and below mean
95%
3 sd above and below mean
99.7%
This will be so helpful now that we know these by heart

22. Let’s try one Correct Answer
When Stephan created this normal distribution, what did he plot on the “y” axis? (Remember to draw a picture.)
a. memory test performance
b. age of preschoolers c. frequency
d. products advertised during their shows
Correct
Answer
Let’s try one

23. Remember, there is an implied axis measuring frequency
Variability and means
Remember, there is an implied axis measuring frequency f f

24. Let’s try one Correct Answer
“If random samples of a fixed N are drawn from any population, as N becomes larger, the distribution of sample means approaches normality, with the overall mean approaching the theoretical population mean.” This is an example of?
a. law of large numbers
b. hypothesis test
c. central limit theorem
d. multiplication law for independent events
Correct
Answer
Let’s try one

25. Central Limit Theorem x will approach µ
Proposition 1: If sample size (n) is large enough (e.g. 100)
The mean of the sampling distribution will
approach the mean of the population
As n ↑
x will approach µ
Proposition 2: If sample size (n) is large enough (e.g. 100)
The sampling distribution of means will
be approximately normal, regardless of the
shape of the population
As n ↑ curve
will approach normal shape
Proposition 3: The standard deviation of the sampling
distribution equals the standard deviation of
the population divided by the square root of the
sample size. As n increases SEM decreases.
As n ↑ curve
variability gets smaller X

26. Law of large numbers: As the number of measurements
increases the data becomes more stable and a better
approximation of the true (theoretical) probability.
Larger sample sizes tend to be associated with stability.
As the number of observations (n) increases or the number of times the experiment is performed, the estimate will become more accurate.

27. Let’s try one Correct Answer
Elle Woods is applying to law school and hopes to attend Harvard Law. She took
the LSAT (Law School Admission Test). Which of the following percentile ranks would represent the best score on this test?
a. 2 percentile
b. 45 percentile
c. 75 percentile
d. 98 percentile
Correct
Answer
Let’s try one

28. Let’s try one Correct Answer
When estimating the population mean (µ) using a “point estimation”, one should use
a. standard deviation
b. standard error of the mean of the sample
c. the mean of the sample
d. the population mean (µ) cannot be estimated using the “point estimation”
Correct
Answer
Let’s try one

29. Let’s try one Correct Answer
Which of the following is inconsistent with the Central Limit Theorem: If random samples of a fixed N are drawn from any population as N becomes larger,
a. the distribution of sample means approaches normality (regardless of the shape of the population distribution)
b. the overall mean approaches the theoretical population mean
c. The standard deviation of the sampling distribution equals the standard
deviation of the population divided by the square root of the sample size.
d. the dispersion of scores also becomes larger
Correct
Answer
Let’s try one

30. Central Limit Theorem x will approach µ
Proposition 1: If sample size (n) is large enough (e.g. 100)
The mean of the sampling distribution will
approach the mean of the population
As n ↑
x will approach µ
Proposition 2: If sample size (n) is large enough (e.g. 100)
The sampling distribution of means will
be approximately normal, regardless of the
shape of the population
As n ↑ curve
will approach normal shape
Proposition 3: The standard deviation of the sampling
distribution equals the standard deviation of
the population divided by the square root of the
sample size. As n increases SEM decreases.
As n ↑ curve
variability gets smaller X

31. Let’s try one Correct Answer
Imagine that a Fatima takes a random sample from the population, and calculates the mean of that sample. Then, the procedure is repeated with a new sample (of the same size) generating a new mean, and then repeated again several times. If the Fatima drew a graph that represented the frequency of all of these means, the graph would be called a
a. regularity distribution of central limits
b. sampling distribution of sample means
c. mean distribution of samples
d. variance distribution of deviations
Correct
Answer
Let’s try one

32. Sampling distribution: is a theoretical probability distribution of
the possible values of some sample statistic that would
occur if we were to draw an infinite number of same-sized
samples from a population
Eugene
Frequency distributions of individual scores
derived empirically
we are plotting raw data
this is a single sample X X
Melvin X X X X X X X X X X X
Sampling distributions sample means
theoretical distribution
we are plotting means of samples
23rd sample
2nd sample

33. Let’s try one Correct Answer
According to the Central Limit Theorem, for very large sample sizes, the mean of the sampling distribution of means
a. underestimates the population mean
b. equals the population mean
c. overestimates the population mean
d. has no relationship with the population mean
Correct
Answer
Let’s try one

34. Central Limit Theorem x will approach µ
Proposition 1: If sample size (n) is large enough (e.g. 100)
The mean of the sampling distribution will
approach the mean of the population
As n ↑
x will approach µ
Proposition 2: If sample size (n) is large enough (e.g. 100)
The sampling distribution of means will
be approximately normal, regardless of the
shape of the population
As n ↑ curve
will approach normal shape
Proposition 3: The standard deviation of the sampling
distribution equals the standard deviation of
the population divided by the square root of the
sample size. As n increases SEM decreases.
As n ↑ curve
variability gets smaller X

35. Let’s try one Correct Answer
According to the Central Limit Theorem, for very large sample sizes, the distribution of sample means from a skewed population is
a. skewed
b. approximately normal
c. binomial
d. bimodal
Correct
Answer
Let’s try one

36. Central Limit Theorem x will approach µ
Proposition 1: If sample size (n) is large enough (e.g. 100)
The mean of the sampling distribution will
approach the mean of the population
As n ↑
x will approach µ
Proposition 2: If sample size (n) is large enough (e.g. 100)
The sampling distribution of means will
be approximately normal, regardless of the
shape of the population
As n ↑ curve
will approach normal shape
Proposition 3: The standard deviation of the sampling
distribution equals the standard deviation of
the population divided by the square root of the
sample size. As n increases SEM decreases.
As n ↑ curve
variability gets smaller X

37. Let’s try one Correct Answer
According to the Central Limit Theorem as sample sizes get larger, the
variability of the sampling distribution of means _______.
a. becomes more positively skewed
b. becomes smaller
c. remains the same
d. becomes larger
Correct
Answer
Let’s try one

38. Central Limit Theorem x will approach µ
Proposition 1: If sample size (n) is large enough (e.g. 100)
The mean of the sampling distribution will
approach the mean of the population
As n ↑
x will approach µ
Proposition 2: If sample size (n) is large enough (e.g. 100)
The sampling distribution of means will
be approximately normal, regardless of the
shape of the population
As n ↑ curve
will approach normal shape
Proposition 3: The standard deviation of the sampling
distribution equals the standard deviation of
the population divided by the square root of the
sample size. As n increases SEM decreases.
As n ↑ curve
variability gets smaller X

39. Let’s try one Correct Answer
Which of the following is not one of the three propositions of the Central Limit Theorem
a. If sample size (n) is large enough, the mean of the sampling distribution will approach the mean of the population
b. If sample size (n) is large enough, the sampling distribution of means will be approach normality
c. If sample size (n) is large enough, the standard deviation of the sampling distribution equals the standard deviation of the population plus the square root of the sample size.
d. As n increases SEM decreases
Correct
Answer
Let’s try one

40. Central Limit Theorem x will approach µ
Proposition 1: If sample size (n) is large enough (e.g. 100)
The mean of the sampling distribution will
approach the mean of the population
As n ↑
x will approach µ
Proposition 2: If sample size (n) is large enough (e.g. 100)
The sampling distribution of means will
be approximately normal, regardless of the
shape of the population
As n ↑ curve
will approach normal shape
Proposition 3: The standard deviation of the sampling
distribution equals the standard deviation of
the population divided by the square root of the
sample size. As n increases SEM decreases.
As n ↑ curve
variability gets smaller X

41. Let’s try one Correct Answer
Fred was examining his company’s historical real estate data in attempt to predict what percentage of households built pools after buying a property without one. He counted all of the households that sold in the past couple years, and found that 60% of households actually built pools after buying houses without one. This is an example of: a. classic probability b. subjective probability c. empirical probability d. conditional probability
Correct
Answer
Let’s try one

42. 91% chance of hitting a corvette
What is probability
1. Empirical probability: relative frequency approach
Number of observed outcomes
Number of observations
Probability of hitting the corvette
Number of carts that hit corvette
Number of carts rolled
182
= .91
200
91% chance of hitting a corvette

43. 16% chance of getting a two
2. Classic probability: a priori probabilities based on logic
rather than on data or experience. We assume we know
the entire sample space as a collection of equally likely
outcomes (deductive rather than inductive).
Number of outcomes of specific event
Number of all possible events
In throwing a die what is the probability of getting a “2”
Number of sides with a 2 1
16% chance of getting a two =
Number of sides 6
In tossing a coin what is probability of getting a tail
Number of sides with a 1 1
50% chance
of getting a tail =
Number of sides 2

44. 3. Subjective probability: based on someone’s personal
judgment (often an expert), and often used when empirical
and classic approaches are not available.
There is a 50% chance that AT&T will
merge with Cingular
Bob says he is 90% sure he could swim across the river

45. Let’s try one Correct Answer
There are 60 students in Kaisa’s physics class. The grades on the first midterm followed a normal distribution. What percent of the students were not within two standard deviations of the mean?
a. 2.5%
b. 5%
c. 50%
d. 95%
Correct
Answer
Let’s try one

46. Let’s try one Correct Answer
Louisa is excited that she scored 2.5 standard deviations above the mean on her chemistry test. The average was 50, and the standard deviation was 10. What grade did she receive?
a. 75
b. 85
c . 95
d. 100
Correct
Answer
Let’s try one

47. Let’s try one Correct Answer
Arthur knows that the probability of drawing the ace of spades from a standard deck of 52 is , even though he has never actually tried it himself. What kind of probability is this?
a. classic probability
b. subjective probability
c. empirical probability
d. conditional probability
Correct
Answer
Let’s try one

48. 91% chance of hitting a corvette
What is probability
1. Empirical probability: relative frequency approach
Number of observed outcomes
Number of observations
Probability of hitting the corvette
Number of carts that hit corvette
Number of carts rolled
182
= .91
200
91% chance of hitting a corvette

49. 16% chance of getting a two
2. Classic probability: a priori probabilities based on logic
rather than on data or experience. We assume we know
the entire sample space as a collection of equally likely
outcomes (deductive rather than inductive).
Number of outcomes of specific event
Number of all possible events
In throwing a die what is the probability of getting a “2”
Number of sides with a 2 1
16% chance of getting a two =
Number of sides 6
In tossing a coin what is probability of getting a tail
Number of sides with a 1 1
50% chance
of getting a tail =
Number of sides 2

50. 3. Subjective probability: based on someone’s personal
judgment (often an expert), and often used when empirical
and classic approaches are not available.
There is a 50% chance that AT&T will
merge with Cingular
Bob says he is 90% sure he could swim across the river

51. Let’s try one Correct Answer
Afra was interested in whether caffeine affects time to complete a cross-word
puzzle, so she randomly assigned people to two groups. One group drank caffeine and the other group did not. She then timed them to see how quickly they could complete a crossword puzzle. This is an example of a
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
Correct
Answer
Let’s try one

52. Let’s try one Correct Answer
An advertising firm wanted to know whether the size of an ad in the margin of a website affected sales. They compared 4 ad sizes (tiny, small, medium and large). They posted the ads and measured sales. This is an example of a
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
Correct
Answer
Let’s try one

53. Let’s try one Correct Answer
Stephan is researching the television-watching behavior of preschoolers. He gave them a memory test for products advertised during their favorite shows. He used these test results to create a normal distribution. This distribution had a mean of 30 and a standard deviation of 2. Find the raw score associated with the 13th percentile. (Remember to draw a picture.) a
b c d
Correct
Answer
Let’s try one

54. 13th percentile
Go to
table
nearest z = 1.13
.3700
x = mean + z σ = 30 + (-1.13)(2) = 27.74
.37
.50
.13 24 ? 26
27.74 30 32 34 36

55. Let’s try one Correct Answer
Taylor is attending a conference about social networking, and the people’s ages in
the room have a mean of 26 and a standard deviation of 3. What proportion of people’s ages were between 20 and 32?
(Remember to draw a picture.) a b c d
Correct
Answer
Let’s try one

56. Homework Worksheet: Problem 2
2 sd
2 sd
.9544 26 28 30 32 34

57. Let’s try one Correct Answer
A distribution has a mean of 50 and a standard deviation of 10. Find the raw score associated with the 77th percentile. (Hint: it may be helpful to draw a picture)
a b c d
Correct
Answer
Let’s try one

58. 77th percentile
Go to
table
nearest z = .74
.2700
x = mean + z σ = 30 + (.74)(2) = 31.48
x = mean + z σ = 50 + (.74)(10) = 57.4
Correct
Answer for test
.7700
.27
.5000 24 26 28 30 ? 34 36
31.48

59. Let’s try one Correct Answer
A distribution has a mean of 50 and a standard deviation of 10. Find the raw score associated with the 33rd percentile. (Hint: it may be helpful to draw a picture)
a b c d
Correct
Answer
Let’s try one

60. 33th percentile
Go to
table
nearest z = -.44
.1700
x = mean + z σ = 50 + (-.44)(10) = 45.6
.17
.50
.33
45.6 ? 50

61. Notice only one is bigger than .50
A distribution has a mean of 50 and a standard deviation of 10. Find the area under the curve associated with a score of 35 and above.
(Hint: it may be helpful to draw a picture) a
b c d
Correct Answer
Notice only one is bigger than .50
Let’s try one

62. A distribution has a mean of 50 and a standard deviation of 10.
Find the area under the curve associated with a score of 35 and above.
(Hint: it may be helpful to draw a picture)
35-50
Go to
table
z =
z = 1.5
.4332 10
.9332
.4332
.5000 35 50 36

63. Let’s try one Correct Answer
Spud Webb was an NBA basketball player who won the annual NBA “slam dunk” contest despite being one of the shortest NBA players of all time. He is 67” tall (5’7”). If we assume that the average height of NBA players is 80” (6’8”) with a standard deviation of 4”, we can calculate the z-score for Mr. Webb to be This z-score would be classified as which of the following:
a. not an unusual score
b. an unusual score
c. an outlier
d. an extreme outlier
Correct
Answer
Let’s try one

64. If score is within 2 standard deviations (z < 2)
“not unusual score”
If score is beyond 2 standard deviations (z ≥ 2)
“is unusual score”
If score is beyond 3 standard deviations (z ≥ 3)
“is an outlier”
If score is beyond 4 standard deviations (z ≥ 4)
“is an extreme outlier”

66. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. Which of the following is true?
a. The IV is gender while the DV is time to finish a race b. The IV is time to finish a race while the DV is gender

67. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The independent variable is a(n) _____
a. Nominal level of measurement b. Ordinal level of measurement c. Interval level of measurement d. Ratio level of measurement

68. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The dependent variable is a(n) _____
a. Nominal level of measurement b. Ordinal level of measurement c. Interval level of measurement d. Ratio level of measurement

69. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The independent variable is a(n) _____
a. Discrete b. Continuous

70. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The dependent variable is a(n) _____
a. Discrete b. Continuous

71. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. Which of the following is true?
a. This is a quasi, between participant design b. This is a quasi, within participant design c. This is a true, between participant design d. This is a true, within participant design

72. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. Which of the following is true?
a. This is t-test b. This is an ANOVA c. This is a correlational design d. This is a chi square

73. According to the Central Limit Theorem, which is false?
As n ↑ x will approach µ b.
As n ↑ curve will approach normal shape c.
As n ↑ curve variability gets bigger
correct
As n ↑ d.

74. Relationship between advertising space and sales
An advertising firm wanted to know whether the size of an ad in the margin of a website affected sales. They compared 4 ad sizes (tiny, small, medium and large). They posted the ads and measured sales. This is an example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
correct

75. Victoria was also interested in the effect of vacation time on productivity
of the workers in her department. In her department some workers took
vacations and some did not. She measured the productivity of those
workers who did not take vacations and the productivity of those workers
who did (after they returned from their vacations).
This is an example of a _____.
a. quasi-experiment
b. true experiment
c. correlational study
correct
Let’s try one

76. Ian was interested in the effect of incentives for girl scouts on the number
of cookies sold. He randomly assigned girl scouts into one of three groups.
The three groups were given one of three incentives and he looked to see
who sold more cookies. The 3 incentives were: 1) Trip to Hawaii, 2) New
Bike or 3) Nothing. This is an example of a ___.
a. quasi-experiment
b. true experiment
c. correlational study
correct
Let’s try one

77. Ian was interested in the effect of incentives and age for girl scouts on the
number of cookies sold. He randomly assigned girl scouts into one of three
groups. The three groups were given one of three incentives and he looked
to see who sold more cookies. The 3 incentives were: 1) Trip to Hawaii,
2) New Bike or 3) Nothing. He also measured their age. This is an example
of a ___.
a. quasi-experiment
b. true experiment
c. correlational study
d. mixed design
correct
Let’s try one

78. Let’s try one Relationship between movie times and
amount of concession purchases.
Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
vs. evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). She compared these two means. This is an
example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
correct
Let’s try one

79. Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
vs. evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). She compared these two means. This is an
example of a _____.
a. between participant design
b. within participant design
c. mixed participant design

80. Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
vs. evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). She compared these two means. This is an
example of a _____.
a. quasi experimental design
b. true experimental design
c. mixed participant design
quasi

81. Let’s try one c. a. d. b. Relationship between movie times and
amount of concession purchases.
Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
and evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). Which of the following would be the appropriate
graph for these data
Matinee
Evening
Concession purchase a. c.
Concession purchase
Movie Times
correct
Movie Times
Concession purchase d.
Movie Time
Concession b.
Let’s try one

82. Relationship between daily fish-oil capsules
and cholesterol levels in men.
Pharmaceutical firm tested whether fish-oil capsules taken daily
decrease cholesterol. They measured cholesterol levels for 30 male
subjects and then had them take the fish-oil daily for 2 months and
tested their cholesterol levels again. Then they compared the mean
cholesterol before and after taking the capsules. This is an example
of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
correct

83. Let’s try one Relationship between GPA and starting salary
Elaina was interested in the relationship between the grade point
average and starting salary. She recorded for GPA. and starting
salary for 100 students and looked to see if there was a relationship.
This is an example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
correct
GPA
Starting
Salary
Relationship between
GPA and Starting salary
Let’s try one

84. What type of analysis is this?
Elaina was interested in the relationship between the grade point
average and starting salary. She recorded for GPA and starting
salary for 100 students and looked to see if there was a relationship.
This is an example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
correlation
GPA
Starting
Salary
Relationship between
GPA and Starting salary
Let’s try one

85. What type of analysis is this?
An automotive firm tested whether driving styles can affect gas efficiency
in their cars. They observed 100 drivers and found there were four
general driving styles. They recruited a sample of 100 drivers all of
whom drove with one of these 4 driving styles. Then they asked all 100
drivers to use the same model car for a month and recorded their gas
mileage. Then they compared the mean mpg for each driving style.
This is an example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
One-way ANOVA
Let’s try another one
Between
Let’s try another one
Quasi-experiment
This is an example of a
a. between participant design
b. within participant design
c. mixed participant design
This is an example of a
a. true experimental design
b. quasi-experimental design
c. mixed design

86. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. In her experiment she rewarded the employees in her Los Angeles stores with bonuses and fun prizes whenever they sold more than 5 items to any one customer. However, the employees in Houston were treated like they always have been treated and were not given any rewards for those 2 months. Judy then compared the number of items sold by each employee in the Los Angeles (rewarded) versus Houston (not rewarded) stores. In this study, a _____________ design was used.
a. between-participant, true experimental
b. between-participant, quasi experimental
c. within-participant, true experimental
d. within-participant, quasi experimental

87. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. (As described in previous question). She wants to use her findings with these two samples to make generalizations about the population, specifically whether rewarding employees will affect sales to all of her stores. She wants to generalize from her samples to a population, this is called
a. random assignment
b. stratified sampling
c. random sampling
d. inferential statistics

88. Let’s try one
Naomi is interested in surveying mothers of newborn infants, so she uses the following sampling technique. She found a new mom and asked her to identify other mothers of infants as potential research participants. Then asked those women to identify other potential participants, and continued this process until she found a suitable sample. What is this sampling technique called?
a. Snowball sampling
b. Systematic sampling
c. Convenience sampling
d. Judgment sampling

89. Let’s try one
Steve who teaches in the Economics Department wants to use a simple random sample of students to measure average income. Which technique would work best to create a simple random sample?
a. Choosing volunteers from her introductory economics class
to participate
b. Listing the individuals by major and choosing a proportion
from within each major at random
c. Numbering all the students at the university and then using a random number table pick cases from the sampling frame.
d. Randomly selecting different universities, and then sampling everyone within the school.

90. Let’s try one
Marcella wanted to know about the educational background of
the employees of the University of Arizona. She was able to
get a list of all of the employees, and then she asked every employee how far they got in school. Which of the following
best describes this situation?
a. census
b. stratified sample
c. systematic sample
d. quasi-experimental study

91. Let’s try one
Mr. Chu who runs a national company, wants to know how his Information Technology (IT) employees from the West Coast compare to his IT employees on the East Coast. He asks each office to report the average number of sick days each employee used in the previous 6 months, and then compared the number of sick days reported for the West Coast and East Coast employees. His methodology would best be described as:
a. time-series comparison
b. cross-sectional comparison
c. true experimental comparison d. both a and b

92. Let’s try one
A researcher wrote the following item stem for a five point rating scale. "Don't you agree that the University needs a football team.” What is the problem with this item?
a. It uses unfamiliar language.
b. It uses double negatives.
c. It is a double-barreled question.
d. It is a "leading" question.

93. Let’s try one
A researcher wrote the following item for a survey on school financing (they were to agree or disagree with the statement), "Parents should support the schools and taxes should be increased." What is the problem with this item?
a. It uses unfamiliar language.
b. It uses double negatives.
c. It is a double-barreled question.
d. It is a "leading" item.

94. Let’s try one
When several items on a questionnaire are rated on a five point scale, and then the responses to all of the questions are added up for a total score (like in a miniquiz), it is called a:
a. Checklist
b. Likert scale
c. Open-ended scale
d. Ranking

95. Let’s try one
Which of the following is a measurement of a construct (and not just the construct itself)
a. sadness
b. customer satisfaction
c. laughing
d. love

96. How many levels of the IV are there?
What if we were looking to see if our new management
program provides different results in employee happiness
than the old program. What is the independent variable?
a. The employees’ happiness
b. Whether the new program works better
c. The type of management program (new vs old)
d. Comparing the null and alternative hypothesis

97. What if we were looking to see if our new management
program provides different results in employee happiness
than the old program. What is the dependent variable?
a. The employees’ happiness
b. Whether the new program works better
c. The type of management program (new vs old)
d. Comparing the null and alternative hypothesis

98. What type of analysis is this?
Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
vs. evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). She compared these two means. This is an
example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
t-test
Let’s try another one
Let’s try one
This is an example of a
a. between participant design
b. within participant design
c. mixed participant design
Between

99. What type of analysis is this?
Marietta is a manager of a movie theater. She wanted to know whether there is a difference in concession sales for people of all ages. She simply measured their age and how much they spent on treats.
This is an example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
Correlation
Let’s try one

100. What type of analysis is this?
Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
and evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). Which of the following would be the appropriate
graph for these data
Matinee
Evening
Concession purchase a. c.
Concession purchase
Movie Times
Two means
t-test
Movie Times
Concession purchase d.
Movie Time
Concession b.
Let’s try one

101. What type of analysis is this?
Gabriella is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales between teenage couples and
middle-aged couples. She also wanted to know whether time of day
makes a difference (matinee versus evening shows). She gathered
the data for a sample of 25 purchases from each pairing.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
Two-way ANOVA
What are the two IV?
What are the levels of each?
Let’s try one

102. What type of analysis is this?
Gabriella is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales between teenage couples and
middle-aged couples. She also wanted to know whether time of day
makes a difference (matinee versus evening shows). She gathered
the means for a sample of 25 purchases from each pairing.
Matinee
Older couples
Evening
Teenagers
Concession purchase a. c.
Concession purchase
Matinee
Older couples
Evening
Teenagers
Movie Times
Concession purchase d.
Older couples
Teenagers
Movie Time
Old / young b.
Matinee
Evening
Four means
Let’s try one

103. What type of analysis is this?
Pharmaceutical firm tested whether fish-oil capsules taken daily
decrease cholesterol. They measured cholesterol levels for 30 male
subjects and then had them take the fish-oil daily for 2 months and
tested their cholesterol levels again. Then they compared the mean
cholesterol before and after taking the capsules. This is an example
of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
t-test
Let’s try another one
Let’s try one
This is an example of a
a. between participant design
b. within participant design
c. mixed participant design
Within

104. What type of analysis is this?
Elaina was interested in the relationship between the grade point
average and starting salary. She recorded for GPA and starting
salary for 100 students and looked to see if there was a relationship.
This is an example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
correlation
GPA
Starting
Salary
Relationship between
GPA and Starting salary
Let’s try one

105. What type of analysis is this?
An automotive firm tested whether driving styles can affect gas efficiency
in their cars. They observed 100 drivers and found there were four
general driving styles. They recruited a sample of 100 drivers all of
whom drove with one of these 4 driving styles. Then they asked all 100
drivers to use the same model car for a month and recorded their gas
mileage. Then they compared the mean mpg for each driving style.
This is an example of a _____.
a. correlation
b. t-test
c. one-way ANOVA
d. two-way ANOVA
Let’s try one
One-way ANOVA
Let’s try another one
Between
Let’s try another one
Quasi-experiment
This is an example of a
a. between participant design
b. within participant design
c. mixed participant design
This is an example of a
a. true experimental design
b. quasi-experimental design
c. mixed design

106. Thank you!
See you next time!!