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Screen Stage Lecturer’s desk Row C Row D Row E Row F Row G Row H Row J Row K Row L Row M 28 27 26 28 27 25 24 23 22 26 25 24 23 22 28 27 26 25 24 23 22 28 27 26 25 24 23 22 28 27 26 25 24 23 22 table 3 broke n desk 2 1 Row A Row B Row C Row D Row E Row F Row G Row H Row J Row K Row L Row M 14 13 12 11 10 9 8 7 6 5 4 3 2 1 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 21 20 19 18 17 16 13 12 11 10 9 8 7 6 5 4 3 2 1 14 13 2 1 Projection Booth Modern Languages R/L handed table 3 2 1 Row C Row D Row E Row F Row G Row H Row J Row K Row L Row M

MGMT 276: Statistical Inference in Management Spring 2015

MGMT 276: Statistical Inference in Management Spring 2015

Schedule of readings Before our next exam (March 24 th) Lind (5 – 11)

Schedule of readings Before our next exam (March 24 th) Lind (5 – 11) Chapter 5: Survey of Probability Concepts Chapter 6: Discrete Probability Distributions Chapter 7: Continuous Probability Distributions Chapter 8: Sampling Methods and CLT Chapter 9: Estimation and Confidence Interval Chapter 10: One sample Tests of Hypothesis Chapter 11: Two sample Tests of Hypothesis Plous (10, 11, 12 & 14) Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability and Risk Chapter 14: The Perception of Randomness

Use this as your study guide By the end of lecture today 3/10/15 Confidence

Use this as your study guide By the end of lecture today 3/10/15 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

Exam 2 –Tuesday March 24 th Study guide is online Bring 2 calculators (remember

Exam 2 –Tuesday March 24 th Study guide is online Bring 2 calculators (remember only simple calculators, we can’t use calculators with programming functions) Bring 2 pencils (with good erasers) Bring ID Stats Review by Jonathon & Nick When: Monday evening March 23 rd 7: 30 – 9: 30 pm (Immediately following Accounting review) Mc. Clelland Hall 207(Berger Hall) Cost: $5. 00

No Homework Just study for exam

No Homework Just study for exam

No class on Thursday . Have a safe and happy spring break

No class on Thursday . Have a safe and happy spring break

Review of Homework Worksheet just in case of questions

Review of Homework Worksheet just in case of questions

Homework Worksheet: Confidence interval uses SEM

Homework Worksheet: Confidence interval uses SEM

Level of Alpha =. 05 =. 01 =. 10 z scores for different levels

Level of Alpha =. 05 =. 01 =. 10 z scores for different levels of confidence 1. 64 90% How do we know which z score to use? 1. 96 2. 58

29. 2 80. 8 Homework Worksheet: Problem 1 Upper boundary raw score x =

29. 2 80. 8 Homework Worksheet: Problem 1 Upper boundary raw score x = mean + (z)(standard deviation) x = 55 + (+ 2. 58)(10) x = 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 sd . 99 29. 2 ? 55 80. 8 ?

29. 2 80. 8 Homework Worksheet: Problem 1 Upper boundary raw score x =

29. 2 80. 8 Homework Worksheet: Problem 1 Upper boundary raw score x = mean + (z)(standard error mean) 51. 3 58. 7 x = 55 + (+ 2. 58)(1. 42) x = 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 . 99 51. 3 ? 55 58. 7 ? 1. 42

29. 2 80. 8 Homework Worksheet: Problem 5 51. 3 58. 7 10. 2

29. 2 80. 8 Homework Worksheet: Problem 5 51. 3 58. 7 10. 2 29. 8 16. 9 23. 1 8. 02 8. 6 9. 18 8. 6 9. 4 4. 09 13. 11 8. 02 9. 18 2. 67 14. 5 7. 8 9. 4 7. 8

Confidence Interval of 99% Has and alpha of 1% Area outside confidence interval is

Confidence Interval of 99% Has and alpha of 1% Area outside confidence interval is alpha Area in the tails is called alpha α =. 10 Critical z 1. 96 Critical z -1. 96 95% α =. 05 Confidence Interval of 90% Has and alpha of 10% Area associated with most extreme scores is called alpha 99% α =. 01 Confidence Interval of 95% Has and alpha of 5% Critical z 2. 58 Critical z -2. 58 Critical z 1. 64 Critical z -1. 64 90%

Rejecting the null hypothesis The result is “statistically significant” if: • the observed statistic

Rejecting the null hypothesis 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 x 2) to be big!! • the p value is less than 0. 05 (which is our alpha) p < 0. 05 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”

. Decision making: Procedures versus outcome Best guess versus “truth” What does it mean

. 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 .

. Type I or Type II error? . Does this airline passenger have a

. 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? Are we correct or have

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

. Type I or type II error? Does this airline passenger Decision made have

. Type I or type II error? Does this airline passenger Decision made have a snow globe? by experimenter True Ho Do not Reject Ho False Ho You are right! Correct decision You are wrong! Type II error (miss) You are wrong! You are right! Type I error Correct Reject Ho (false alarm) decision 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) What would null hypothesis be? This passenger does not have any snow globe Which is worse? 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)

True Ho . Type I or type II error Does advertising affect sales? Do

True Ho . Type I or type II error Does advertising affect sales? Do not Reject Ho Decision made by experimenter You are right! Correct decision False Ho You are wrong! Type II error (miss) You are wrong! You are right! Type I error Correct Reject Ho (false alarm) decision 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 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? What if we

. What is worse a type I or type II error? What if we were looking at a new HIV drug that had no unpleasant side affects Do not Reject Ho Decision made by experimenter True Ho False Ho You are right! Correct decision You are wrong! Type II error (miss) You are wrong! You are right! Type I error Correct Reject Ho (false alarm) decision 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)

. Type I or type II error Which is What if we were looking

. Type I or type II error Which is What if we were looking to see if there is a fire worse? 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)

. Type I or type II error What if we were looking to see

. Type I or type II error What if we were looking to see if an individual were guilty of a crime? Which is worse? 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

Rejecting the null hypothesis The result is “statistically significant” if: • the observed statistic

Rejecting the null hypothesis 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 x 2) observed stat > critical stat If we want to reject the null, we want our t (or z or r or F or x 2) to be big • the p value is less than 0. 05 (which is our alpha) p < 0. 05 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

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 Remember, reject the null if the observed z is bigger than the critical z Significance level =. 05 α = 0. 01 Significance level =. 01 -1. 96 or +1. 96 p < 0. 05 Reject the null -2. 58 or Do not +2. 58 Reject the null Yes, Significant difference Not a Significant difference

Deciding whether or not to reject the null hypothesis. 05 versus. 01 alpha levels

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 Remember, reject the null if the observed z is bigger than the critical z Significance level =. 05 α = 0. 01 Significance level =. 01 -1. 96 or +1. 96 Do Not Reject the null -2. 58 or Do Not +2. 58 Reject the null Not a Significant difference

Deciding whether or not to reject the null hypothesis. 05 versus. 01 alpha levels

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 Remember, reject the null if the observed z is bigger than the critical z Significance level =. 05 α = 0. 01 Significance level =. 01 -1. 96 or +1. 96 p < 0. 05 Reject the null -2. 58 or Reject +2. 58 the null Yes, Significant difference p < 0. 01 Yes, Significant difference

Deciding whether or not to reject the null hypothesis. 05 versus. 01 alpha levels

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 Remember, reject the null if the observed z is bigger than the critical z Significance level =. 05 α = 0. 01 Significance level =. 01 -1. 96 or +1. 96 p < 0. 05 Reject the null -2. 58 or Do not +2. 58 Reject the null Yes, Significant difference Not a Significant difference

One versus two tail test of significance: Comparing different critical scores (but same alpha

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% 5% 2. 5% How would the critical z change? Pros and cons… 2. 5%

One versus two tail test of significance 5% versus 1% alpha levels How would

One versus two tail test of significance 5% versus 1% alpha levels How would the critical z change? One-tailed 5% 1% α = 0. 05 Significance level =. 05 α = 0. 01 Significance level =. 01 Two-tailed 2. 5%. 5% -1. 64 or +1. 64 -1. 96 or +1. 96 -2. 33 or +2. 33 -2. 58 or +2. 58

One versus two tail test of significance 5% versus 1% alpha levels What if

One versus two tail test of significance 5% versus 1% alpha levels What if our observed z = 2. 0? How would the critical z change? One-tailed α = 0. 05 Remember, reject the null if the observed z is bigger than the critical z Significance level =. 05 -1. 64 or Reject +1. 64 the null α = 0. 01 -2. 33 or Significance Do not +2. 33 level =. 01 Reject the null Two-tailed -1. 96 or +1. 96 Reject the null -2. 58 or Do not +2. 58 Reject the null

One versus two tail test of significance 5% versus 1% alpha levels What if

One versus two tail test of significance 5% versus 1% alpha levels What if our observed z = 1. 75? How would the critical z change? One-tailed α = 0. 05 Remember, reject the null if the observed z is bigger than the critical z Significance level =. 05 -1. 64 or Reject +1. 64 the null α = 0. 01 -2. 33 or Significance Do not +2. 33 level =. 01 Reject the null Two-tailed -1. 96 or +1. 96 Do not Reject the null -2. 58 or Do not +2. 58 Reject the null

One versus two tail test of significance 5% versus 1% alpha levels What if

One versus two tail test of significance 5% versus 1% alpha levels What if our observed z = 2. 45? How would the critical z change? One-tailed α = 0. 05 Remember, reject the null if the observed z is bigger than the critical z Significance level =. 05 α = 0. 01 -1. 64 or Reject +1. 64 the null -2. 33 or Significance Reject +2. 33 level =. 01 the null Two-tailed -1. 96 or +1. 96 Reject the null -2. 58 or Do not +2. 58 Reject the null

Five steps to hypothesis testing Step 1: Identify the research problem (hypothesis) Describe the

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

One or two tailed test? Two tailed because there is no prediction regarding who

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.

What type of analysis is this? Marietta is a manager of a movie theater. She wanted to know whethere 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 Let’s try another one This is an example of a a. between participant design b. within participant design c. mixed participant design Let’s try one Between

What if we were looking to see if our new management program provides different

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

Remember the null says “no difference between groups” (between levels of IV) What if

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 a. None of the employees are happy difference b. The program does not affect employee happiness c. The new programs works better d. The old program works better

False alarm Which of the following is a Type I error: a. We conclude

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 does 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

Which of the following would represent a one-tailed test? a. Please test to see whether men or women are taller Increases 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?

Which of the following represents a significant finding: a. p < 0. 05 b.

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 Careful with e. Both a and d “exceeds” p < 0. 05 and “reject null” both mean “significant finding”

Let’s try one Marietta took a pregnancy test. The null hypothesis would be: a.

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”

Let’s try one Marietta took a pregnancy test and it read that she was

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 False alarm = b. Type II error Type I error c. Type III error d. Correct decision

Let’s try one Kenley decided to reject the null, and then found out the

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 It is right to c. Type III error reject a false null d. Correct decision

Let’s try one Agnes compared the heights of the women’s gymnastics team and the

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

According to the Central Limit Theorem, which is false? a. As n ↑ x

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 d. As n ↑ As n goes up, variability goes down

Let’s try one Albert compared the time required to finish the race for 20

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

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 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

Let’s try one Which would be a Type II error? Albert compared the time

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

Let’s try one Albert compared the time required to finish the race for 20

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”

Let’s try one Albert compared the time required to finish the race for 20

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

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. b. c. d. This is a quasi, between participant design This is a quasi, within participant design quasi, between This is a true, between participant design This is a true, within participant design

Let’s try one Albert compared the time required to finish the race for 20

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 “t for two” b. t-test (two groups being compared) c. one-way ANOVA d. two-way ANOVA

Match each level of significance to each situation. Which situation would be associated with

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 5% 1% α = 0. 05 Hint: Possible values 1. 64 1. 96 2. 33 2. 58 Significance level =. 05 α = 0. 01 Significance level =. 01 Two-tailed 2. 5%. 5% -1. 64 or +1. 64 A -1. 96 or +1. 96 C -2. 58 or +2. 58 -2. 33 or +2. 33 B D

Match each level of significance to each situation. Which situation would be associated with

Match each level of significance to each situation. Which situation would be associated with a critical z of 1. 64? a. A b. B c. C d. D Critical z values One-tailed 5% 1% α = 0. 05 Hint: Possible values 1. 64 1. 96 2. 33 2. 58 Significance level =. 05 α = 0. 01 Significance level =. 01 Two-tailed 2. 5%. 5% -1. 64 or +1. 64 A -1. 96 or +1. 96 C -2. 58 or +2. 58 -2. 33 or +2. 33 B D

Match each level of significance to each situation. Which situation would be associated with

Match each level of significance to each situation. Which situation would be associated with a critical z of 2. 58? a. A b. B c. C d. D Critical z values One-tailed 5% 1% α = 0. 05 Hint: Possible values 1. 64 1. 96 2. 33 2. 58 Significance level =. 05 α = 0. 01 Significance level =. 01 Two-tailed 2. 5%. 5% -1. 64 or +1. 64 A -1. 96 or +1. 96 C -2. 58 or +2. 58 -2. 33 or +2. 33 B D

Relationship between advertising space and sales An advertising firm wanted to know whether the

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 More than two groups b. t-test being compared c. one-way ANOVA d. two-way ANOVA

Afra was interested in whether caffeine affects time to complete a crossword puzzle, and

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

Relationship between movie times and amount of concession purchases. Gabriella is a manager of

Relationship between movie times and amount of concession purchases. Gabriella is a manager of a movie theater. She wanted to know whethere 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 Two separate IVs b. t-test 1. Time of day – two levels c. one-way ANOVA (afternoon vs evening) d. two-way ANOVA 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

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 _____. Quasi- experiment a. quasi-experiment She did not randomly assign b. true experiment groups, she let the workers c. correlational study self-select who will go on vacation Let’s try one

Ian was interested in the effect of incentives for girl scouts on the number

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

Relationship between movie times and amount of concession purchases. Marietta is a manager of

Relationship between movie times and amount of concession purchases. Marietta is a manager of a movie theater. She wanted to know whethere 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 “t for two” b. t-test (two groups being compared) c. one-way ANOVA d. two-way ANOVA Let’s try one

Relationship between movie times and amount of concession purchases. Concession purchase Marietta is a

Relationship between movie times and amount of concession purchases. Concession purchase Marietta is a manager of a movie theater. She wanted to know whethere 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 “t for two” c. a. (two groups being compared) Movie Times d. Concession b. Evening Movie Time Concession purchase Matinee Movie Times Let’s try one

Relationship between daily fish-oil capsules and cholesterol levels in men. Pharmaceutical firm tested whether

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 “t for two” b. t-test (fish-oil vs no fish-oil are the c. one-way ANOVA two groups being compared) d. two-way ANOVA Within (same people measured twice) Let’s try another 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 Starting Salary Elaina was interested in the relationship

Relationship between GPA and starting salary 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 Correlation b. t-test (both variables are quantitative) c. one-way ANOVA d. two-way ANOVA GPA Relationship between GPA and Starting salary Let’s try one

Relationship between driving strategy and gas mileage (miles per gallon). An automotive firm tested

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 ANOVA c. one-way ANOVA Four groups being compared d. two-way ANOVA

Afra was interested in which characteristics of displays around the cash register will affect

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 Type of display – two levels Let’s 1. try one (big vs little) 2. Location – two levels (eye vs waist level)