Physics- atmospheric Sciences (PAS) - Room 201

Physics- atmospheric Sciences (PAS) - Room 201
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MGMT 276: Statistical Inference in Management Fall 2015
Welcome

Just for Fun Assignments Go to D2L - Click on “Content”
Click on “Interactive Online Just-for-fun Assignments” Please note: These are not worth any class points and are different from the required homeworks

Exam 2 – This Tuesday (10/20/15)
Study guide is online now Bring two calculators (only simple calculators, we can’t use calculators with programming functions) Bring 2 pencils (with good erasers) Bring ID OpenStax Chapters 1 – 11 Plous (10, 11, 12 & 14) - Chapter 10: Representativeness Heuristic - Chapter 11: The Availability Heuristic - Chapter 12: Probability and Risk - Chapter 14: The Perception of Randomness Stats Review by Jonathon & Nick When: Monday evening October 19th 6:30 – 7:30pm Room: ILC 120 Cost: $5.00

No homework just prepare for Exam 2

By the end of lecture today 10/15/15
Confidence Intervals Logic of hypothesis testing Steps for hypothesis testing Levels of significance (Levels of alpha) what does p < 0.05 mean? what does p < 0.01 mean? One-tail versus Two-tail test Type I versus Type II Errors Review for Exam 2

Reject the null hypothesis Support for alternative
. Reject the null hypothesis 95% .. Relative to this distribution I am unusual maybe even an outlier X 95% X Relative to this distribution I am utterly typical Support for alternative hypothesis Review

Rejecting the null hypothesis
. Rejecting the null hypothesis . null notnull big z score x x If the observed z falls beyond the critical z in the distribution (curve): then it is so rare, we conclude it must be from some other distribution then we reject the null hypothesis and p < 0.05 then yes there is a significant effect Alternative Hypothesis If the observed z falls within the critical z in the distribution (curve): then we know it is a common score and is likely to be part of this distribution, we conclude it must be from this distribution then we do not reject the null hypothesis then no there is no significant effect . null x x small z score

How do we know how rare is rare enough?
Area in the tails is alpha 99% α = .01 95% α = .05 90% α = .10 How do we know how rare is rare enough? Level of significance is called alpha (α) The degree of rarity required for an observed outcome to be “weird enough” to reject the null hypothesis Which alpha level would be associated with most “weird” or rare scores? Critical z: A z score that separates common from rare outcomes and hence dictates whether the null hypothesis should be retained (same logic will hold for “critical t”) If the observed z falls beyond the critical z in the distribution (curve) then it is so rare, we conclude it must be from some other distribution

The result is “statistically significant” if: the observed statistic is larger than the critical statistic (which can be a ‘z” or “t” or “r” or “F” or x2) observed stat > critical stat If we want to reject the null, we want our t (or z or r or F or x2) to be big!! the p value is less than 0.05 (which is our alpha) p < If we want to reject the null, we want our “p” to be small!! we reject the null hypothesis then we have support for our alternative hypothesis

Confidence Interval of 95% Has and alpha of 5% α = .05
Critical z -2.58 Critical z 2.58 Confidence Interval of 99% Has and alpha of 1% α = .01 99% Area in the tails is called alpha Critical z -1.96 Critical z 1.96 Confidence Interval of 95% Has and alpha of 5% α = .05 95% Critical z separates rare from common scores Critical z -1.64 Critical z 1.64 Confidence Interval of 90% Has and alpha of 10% α = . 10 90%

Deciding whether or not to reject the null hypothesis. 05 versus
Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = 2.0? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 p < 0.05 Yes, Significant difference Reject the null Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 Not a Significant difference Do not Reject the null

Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = 1.5? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 Do Not Reject the null Not a Significant difference Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 Not a Significant difference Do Not Reject the null

Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = -3.9? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 p < 0.05 Yes, Significant difference Reject the null Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 p < 0.01 Yes, Significant difference Reject the null

Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = -2.52? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 p < 0.05 Yes, Significant difference Reject the null Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 Not a Significant difference Do not Reject the null

90% Moving from descriptive stats into inferential stats….
For scores that fall into the middle range, we do not reject the null Moving from descriptive stats into inferential stats…. Critical z 1.64 Critical z -1.64 90% Measurements that occur within the middle part of the curve are ordinary (typical) and probably belong there 5% 5% Measurements that occur outside this middle ranges are suspicious, may be an error or belong elsewhere For scores that fall into the regions of rejection, we reject the null What percent of the distribution will fall in region of rejection Critical Values

The result is “statistically significant” if: the observed statistic is larger than the critical statistic observed stat > critical stat If we want to reject the null, we want our t (or z or r or F or x2) to be big!! the p value is less than 0.05 (which is our alpha) p < If we want to reject the null, we want our “p” to be small!! we reject the null hypothesis then we have support for our alternative hypothesis A note on decision making following procedure versus being right relative to the “TRUTH”

Procedures versus outcome Best guess versus “truth”
. Decision making: Procedures versus outcome Best guess versus “truth” What does it mean to be correct? Why do we say: “innocent until proven guilty” “not guilty” rather than “innocent” Is it possible we got a verdict wrong?

We make decisions at Security Check Points
. We make decisions at Security Check Points .

Does this airline passenger have a snow globe?
. Type I or Type II error? . Does this airline passenger have a snow globe? Null Hypothesis means she does not have a snow globe (that nothing unusual is happening) – Should we reject it???!! As detectives, do we accuse her of brandishing a snow globe?

Does this airline passenger have a snow globe?
. Does this airline passenger have a snow globe? Status of Null Hypothesis (actually, via magic truth-line) Are we correct or have we made a Type I or Type II error? True Ho No snow globe False Ho Yes snow globe You are wrong! Type II error (miss) Do not reject Ho “no snow globe move on” You are right! Correct decision Decision made by experimenter You are wrong! Type I error (false alarm) Reject Ho “yes snow globe, stop!” You are right! Correct decision Note: Null Hypothesis means she does not have a snow globe (that nothing unusual is happening) – Should we reject it???!!

Type I error (false alarm)
Type I or type II error? . Decision made by experimenter Reject Ho Do not Reject Ho True Ho False Ho You are right! Correct decision You are wrong! Type I error (false alarm) Type II error (miss) Does this airline passenger have a snow globe? Two ways to be correct: Say she does have snow globe when she does have snow globe Say she doesn’t have any when she doesn’t have any Two ways to be incorrect: Say she does when she doesn’t (false alarm) Say she does not have any when she does (miss) Which is worse? What would null hypothesis be? This passenger does not have any snow globe Type I error: Rejecting a true null hypothesis Saying the she does have snow globe when in fact she does not (false alarm) Type II error: Not rejecting a false null hypothesis Saying she does not have snow globe when in fact she does (miss)

Type I error (false alarm)
Type I or type II error . Decision made by experimenter Reject Ho Do not Reject Ho True Ho False Ho You are right! Correct decision You are wrong! Type I error (false alarm) Type II error (miss) Does advertising affect sales? Two ways to be correct: Say it helps when it does Say it does not help when it doesn’t help Which is worse? Two ways to be incorrect: Say it helps when it doesn’t Say it does not help when it does What would null hypothesis be? This new advertising has no effect on sales Type I error: Rejecting a true null hypothesis Saying the advertising would help sales, when it really wouldn’t help people (false alarm) Type II error: Not rejecting a false null hypothesis Saying the advertising would not help when in fact it would (miss)

What is worse a type I or type II error?
. Decision made by experimenter Reject Ho Do not Reject Ho True Ho False Ho You are right! Correct decision You are wrong! Type I error (false alarm) Type II error (miss) What if we were looking at a new HIV drug that had no unpleasant side affects Two ways to be correct: Say it helps when it does Say it does not help when it doesn’t help Two ways to be incorrect: Say it helps when it doesn’t Say it does not help when it does Which is worse? What would null hypothesis be? This new drug has no effect on HIV Type I error: Rejecting a true null hypothesis Saying the drug would help people, when it really wouldn’t help people (false alarm) Type II error: Not rejecting a false null hypothesis Saying the drug would not help when in fact it would (miss)

Which is worse? Type I or type II error
. Which is worse? Type I or type II error What if we were looking to see if there is a fire burning in an apartment building full of cute puppies Two ways to be correct: Say “fire” when it’s really there Say “no fire” when there isn’t one Two ways to be incorrect: Say “fire” when there’s no fire (false alarm) Say “no fire” when there is one (miss) What would null hypothesis be? No fire is occurring Type I error: Rejecting a true null hypothesis (false alarm) Type II error: Not rejecting a false null hypothesis (miss)

Which is worse? Type I or type II error
. Which is worse? Type I or type II error What if we were looking to see if an individual were guilty of a crime? Two ways to be correct: Say they are guilty when they are guilty Say they are not guilty when they are innocent Two ways to be incorrect: Say they are guilty when they are not Say they are not guilty when they are What would null hypothesis be? This person is innocent - there is no crime here Type I error: Rejecting a true null hypothesis Saying the person is guilty when they are not (false alarm) Sending an innocent person to jail (& guilty person to stays free) Type II error: Not rejecting a false null hypothesis Saying the person in innocent when they are guilty (miss) Allowing a guilty person to stay free

The result is “statistically significant” if: the observed statistic is larger than the critical statistic (which can be a ‘z” or “t” or “r” or “F” or x2) observed stat > critical stat If we want to reject the null, we want our t (or z or r or F or x2) to be big!! the p value is less than 0.05 (which is our alpha) p < If we want to reject the null, we want our “p” to be small!! we reject the null hypothesis then we have support for our alternative hypothesis

Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = 2.0? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 p < 0.05 Yes, Significant difference Reject the null Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 Not a Significant difference Do not Reject the null

Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = 1.5? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 Do Not Reject the null Not a Significant difference Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 Not a Significant difference Do Not Reject the null

Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = -3.9? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 p < 0.05 Yes, Significant difference Reject the null Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 p < 0.01 Yes, Significant difference Reject the null

Deciding whether or not to reject the null hypothesis .05 versus .01 alpha levels What if our observed z = -2.52? How would the critical z change? α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.96 or +1.96 p < 0.05 Yes, Significant difference Reject the null Remember, reject the null if the observed z is bigger than the critical z -2.58 or +2.58 Not a Significant difference Do not Reject the null

How would the critical z change?
One versus two tail test of significance: Comparing different critical scores (but same alpha level – e.g. alpha = 5%) One versus two tailed test of significance z score = 1.64 95% 95% 5% 2.5% 2.5% One-tailed test: If your hypothesis is “directional” claiming that one group will have a bigger mean than the other group Two-tailed test: If your hypothesis is “non-directional” claiming only that the two groups have different means How would the critical z change? Pros and cons…

One versus two tail test of significance 5% versus 1% alpha levels
How would the critical z change? One-tailed Two-tailed α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 1% 5% 2.5% .5% .5% 2.5% -1.64 or +1.64 -1.96 or +1.96 -2.33 or +2.33 -2.58 or +2.58

What if our observed z = 2.0? How would the critical z change? One-tailed Two-tailed α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.64 or +1.64 -1.96 or +1.96 Remember, reject the null if the observed z is bigger than the critical z Reject the null Reject the null -2.33 or +2.33 -2.58 or +2.58 Do not Reject the null Do not Reject the null

What if our observed z = 1.75? How would the critical z change? One-tailed Two-tailed α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.64 or +1.64 -1.96 or +1.96 Remember, reject the null if the observed z is bigger than the critical z Do not Reject the null Reject the null -2.33 or +2.33 -2.58 or +2.58 Do not Reject the null Do not Reject the null

What if our observed z = 2.45? How would the critical z change? One-tailed Two-tailed α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 -1.64 or +1.64 -1.96 or +1.96 Remember, reject the null if the observed z is bigger than the critical z Reject the null Reject the null -2.33 or +2.33 -2.58 or +2.58 Reject the null Do not Reject the null

Five steps to hypothesis testing
Step 1: Identify the research problem (hypothesis) Describe the null and alternative hypotheses Step 2: Decision rule Alpha level? (α = .05 or .01)? One or two tailed test? Balance between Type I versus Type II error Critical statistic (e.g. z or t or F or r) value? Step 3: Calculations Step 4: Make decision whether or not to reject null hypothesis If observed z (or t) is bigger then critical z (or t) then reject null Step 5: Conclusion - tie findings back in to research problem

Confidence interval uses SEM
Homework Worksheet: Confidence interval uses SEM

How do we know which z score to use?
Level of Alpha 1.96 = .05 1.64 = .10 90% 2.58 = .01 z scores for different levels of confidence How do we know which z score to use?

.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 ?

.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 ?

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

Exam 2 Review

The type of management program (new vs old)
One or two tailed test? Two tailed because there is no prediction regarding who which will work better 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 The type of management program (new vs old)

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

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? happiness 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

“no difference between groups” (between levels of IV)
Remember the null says “no difference between groups” (between levels of IV) What if we were looking to see if our new management program provides different results in employee happiness than the old program. What would null hypothesis be? No difference a. None of the employees are happy b. The program does not affect employee happiness c. The new programs works better d. The old program works better

Which of the following is a Type I error:
False alarm Which of the following is a Type I error: a. We conclude that the program works better when it fact it doesn’t b. We conclude that the program works better when in fact it does c. We conclude that the program doesn’t work better when in fact it doesn’t d. We conclude that the program doesn’t work better when in fact it does

Which of the following would represent a one-tailed test?
a. Please test to see whether men or women are taller b. With an alpha of .05 test whether advertising increases sales c. With an alpha of .01 test whether management strategies affect worker productivity d. Does a stock trader’s education affect the amount of money they make in a year? Increases

Which of the following represents a significant finding:
a. p < 0.05 b. critical value exceeds the observed statistic c. the observed z statistic is nearly zero d. we reject the null hypothesis e. Both a and d Careful with “exceeds” p < 0.05 and “reject null” both mean “significant finding”

Marietta took a pregnancy test. The null hypothesis would be:
Let’s try one Marietta took a pregnancy test. The null hypothesis would be: a. Marietta is pregnant b. Marietta is not pregnant “nothing going on”

False alarm = Type I error
Let’s try one Marietta took a pregnancy test and it read that she was pregnant, when it fact she was not. This is an example of a a. Type I error b. Type II error c. Type III error d. Correct decision False alarm = Type I error

It is right to reject a false null
Let’s try one Kenley decided to reject the null, and then found out the null was false. This is an example of a a. Type I error b. Type II error c. Type III error d. Correct decision It is right to reject a false null

As n goes up, variability goes down
Let’s try one Agnes compared the heights of the women’s gymnastics team and the women’s basketball team. If she doubled the number of players measured (but ended up with the same means) what effect would that have on the results? a. as the sample size got larger the variability would increase b. as the sample size got larger the variability would decrease c. as the sample size got larger the variability would stay the same As n goes up, variability goes down

As n goes up, variability goes down
According to the Central Limit Theorem, which is false? a. As n ↑ x will approach µ b. As n ↑ curve will approach normal shape c. As n ↑ curve variability gets larger As n goes up, variability goes down As n ↑ d.

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 IV = gender DV = time

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? No difference a. The null hypothesis is that there is no difference in race times between the genders b. The null hypothesis is that there is a difference between the genders

Which would be a Type II error? 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. A Type I Error would claim that: Type I = False Alarm a. There is a difference when in fact there is b. There is a difference when in fact there isn’t one c. There is no difference when in fact there isn’t one d. There is no difference when in fact there is a difference Type II = Miss

“reject null” both mean “significant finding”
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.. He concluded p < 0.05 what does this mean? a. There is a significant difference between the means b. There is no significant difference between the means p < 0.05 and “reject null” both mean “significant finding”

There is no prediction regarding who will be faster, males or females
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 is true? a. This is a one-tailed test b. This is a two-tailed test There is no prediction regarding who will be faster, males or females

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 quasi, between

(two groups being compared)
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 best describes this study? a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA “t for two” (two groups being compared)

Match each level of significance to each situation. Which situation
would be associated with a critical z of 1.96? a. A b. B c. C d. D Critical z values One-tailed Two-tailed α = 0.05 Significance level = .05 α = 0.01 Significance level = .01 5% 1% 2.5% .5% 2.5% .5% -1.64 or +1.64 A -1.96 or +1.96 B Hint: Possible values 1.64 1.96 2.33 2.58 -2.33 or +2.33 C -2.58 or +2.58 D

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 More than two groups being compared

1. caffeine – two levels (yes caffeine vs no caffeine)
Afra was interested in whether caffeine affects time to complete a cross-word puzzle, and whether this affected young adults and older adults similarly. This is an example of a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA. Two separate IVs 1. caffeine – two levels (yes caffeine vs no caffeine) 2. Age – two levels (young vs old) Let’s try one

Let’s try one Relationship between movie times and
amount of concession purchases. 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 separate IVs 1. Time of day – two levels (afternoon vs evening) 2. Age – two levels (young vs old) Let’s try one

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 Quasi- experiment She did not randomly assign groups, she let the workers self-select who will go on vacation Let’s try one

He randomly assigned girls to groups
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 True- experiment He randomly assigned girls to groups Let’s try one

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 “t for two” (two groups being compared) Let’s try one

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. “t for two” (two groups being compared) c. Concession purchase Movie Times Movie Times Concession purchase d. Movie Time Concession b. Let’s try one

Let’s try one Let’s try another one
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 “t for two” (fish-oil vs no fish-oil are the two groups being compared) Within (same people measured twice) 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

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 Correlation (both variables are quantitative) GPA Starting Salary Relationship between GPA and Starting salary Let’s try one

Relationship between driving strategy and
gas mileage (miles per gallon). 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 ANOVA Four groups being compared

1. Type of display – two levels (big vs little)
Afra was interested in which characteristics of displays around the cash register will affect impulse purchases of candy bars and drinks. She was interested in the type of display (big versus little) and the location of the display (eye level versus waist level). She varied the location and type of display on different registers and recorded the number of sales of items on the displays (candy and drinks). This is an example of a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA. Two separate IVs 1. Type of display – two levels (big vs little) 2. Location – two levels (eye vs waist level) Let’s try one

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Physics- atmospheric Sciences (PAS) - Room 201

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Physics- atmospheric Sciences (PAS) - Room 201

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