Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2018 Room 150 Harvill Building 10:00 - 10:50 Mondays, Wednesdays & Fridays. Welcome 10/10/18 http://www.youtube.com/watch?v=oSQJP40PcGI http://www.youtube.com/watch?v=oSQJP40PcGI http://www.youtube.com/watch?v=oSQJP40PcGI

Alyson Lecturer’s desk Chris Flo Jun Trey Projection Booth Screen Row A 15 14 Row A 13 12 11 10 9 8 7 6 5 4 3 2 1 Row A Chris Row B 23 22 21 20 Row B 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row B Row C 25 24 23 22 21 Row C 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row C Row D 29 28 27 26 25 24 23 Row D 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row D Row E 31 30 29 28 27 26 25 24 23 Row E 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row E Flo Row F 35 34 33 32 31 30 29 28 27 26 Row F 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row F Row G 35 34 33 32 31 30 29 28 27 26 Row G 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row G Row H 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 Row H 12 11 10 9 8 7 6 5 4 3 2 1 Row H 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 1 Row J Row J 13 12 11 10 9 8 7 6 5 4 3 2 41 40 39 38 37 36 35 34 33 32 31 30 29 Row K 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row K Row L 33 32 31 30 29 28 27 26 25 Row L 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row L Row M 21 20 19 Row M 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row M Row N 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Row P 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Jun Trey table 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Projection Booth Left handed desk Harvill 150 renumbered

.. The Green Sheets

Study Guide posted Schedule of readings Before next exam (October 12th) Please read chapters 1 - 8 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

Just study for the Exam on Friday No homework Just study for the Exam on Friday

Lab sessions Labs Prep for Exam 2

Hand out z tables

z table Formula Normal distribution Raw scores z-scores probabilities Have z Find raw score Z Scores Have z Find area z table Formula Have area Find z Area & Probability Have raw score Find z Raw Scores

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 . Let’s try one

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

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. 27.74 b. 29.26 c. 29.34 d. 32.26 Correct Answer Let’s try one

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

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. .3413 b. .6826 c. .9544 d. .9970 Correct Answer Let’s try one

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

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. .74 b. 42.6 c. 57.4 d. 57.7 Correct Answer Let’s try one

77th percentile Go to table nearest z = .74 .2700 x = mean + z σ = 50 + (.74)(10) = 57.4 .7700 .27 .5000 50 ? 57.4

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. .44 b. 40.5 c. 45.6 d. 54.4 Correct Answer Let’s try one

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

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. .0668 b. 1.5 c. .9332 d. .4332 Correct Answer Notice only one is bigger than .50 Let’s try one

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

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 -3.5. 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

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”

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? correct 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

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 correct

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 correct

a. Discrete b. Continuous 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) _____ correct a. Discrete b. Continuous

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? correct 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

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? correct a. This is t-test b. This is an ANOVA c. This is a correlational design d. This is a chi square .

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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 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

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

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

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

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

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

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

Thank you! See you next time!!