# Worksheet Redux Introduction to Statistics for the Social

- Slides: 52

Worksheet – Redux

Introduction to Statistics for the Social Sciences SBS 200 - Lecture Section 001, Spring 2019 Room 150 Harvill Building 9: 00 - 9: 50 Mondays, Wednesdays & Fridays. February 6 http: //www. youtube. com/watch? v=o. SQJP 40 Pc. GI

e v a h u o y f i d e r Even e t s i g e r t e y n a c t o u n o y r e k c i l c r e u t a yo p i c i t r a p still The Gre e She n ets

Schedule of readings Before next exam (February 8) Study Guide posted Please read chapters 1 - 5 in Open. Stax textbook Please read Appendix D, E & F online On syllabus this is referred to as online readings 1, 2 & 3 Please read Chapters 1, 5, 6 and 13 in Plous Chapter 1: Selective Perception Chapter 5: Plasticity Chapter 6: Effects of Question Wording and Framing Chapter 13: Anchoring and Adjustment

Everyone will want to be enrolled in one of the lab sessions e u n i t n o c s b k a e e L w s thi

Exam 1 Review Seating Chart – Arrive a little early University ID number – 8 -digit (on UAccess) Class ID – 4 -digit (on all of your assignments) Distributed from left side Extras go on bottom of pile Finish by 9: 45 Need Ø ID Card Ø Two pencils with good erasers Ø Two simple calculators (four function + square root) Ø Tissues

Summary of 7 facts to memorize w vie

z= -1 68% If we go up one standard deviation 1 + z score = +1. 0 and raw score = 105 z= If we go down one standard deviation z score = -1. 0 and raw score = 95 85 90 95 100 105 110 115 z= -2 95% z= 85 90 95 100 105 110 115 z= -3 99. 7% 85 90 95 100 105 110 115 Mean = 100 Standard deviation = 5 If we go up two standard deviations 2 z score = +2. 0 and raw score = 110 + If we go down two standard deviations z score = -2. 0 and raw score = 90 If we go up three standard deviations z score = +3. 0 and raw score = 115 +3 z = If we go down three standard deviations z score = -3. 0 and raw score = 85 z score: A score that indicates how many standard deviations an observation is above or below the mean of the distribution z score = raw score - mean standard deviation w R e i v e

Worksheet – Redux

Writing Assignment – Pop Quiz Distance from the mean 2 inches 4 inches X Taller Shorter X Taller X Pre sto mea n is 2” n (t alle taller th an m an the ost) Mik e mea is 4” s n (s hort e hor ter r than th mos e t) Dia llo Equal to mean mea is exa 0 inches n (h ctly alf t sam Half are Shorter alle e r ha heigh lf sh t orte as r)

Writing Assignment – Pop Quiz Parameter Sigma – standard deviation - population mu – a mean – an average - population x-bar – a mean – an average - sample statistic s – standard deviation - sample statistic The number of “standard deviations” the score is from the mean population Sigma squared and s squared - variance Sigma is parameter (population) s is statistic (sample) Deviation scores (x-µ) for population (parameter) (x-x) is statistic (sample) Sum of squares On left is statistic on right is parameter Standard deviation s is statistic sigma is parameter Degrees of freedom sample

If it is complete and correct, hand it in now Do not hand it in until it: • Is correct, complete and stapled • If it is not complete and correct, consider this an “extension” and hand it in next time

Exam 1 Review

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

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. b. c. d. Nominal level of measurement Ordinal level of measurement Interval level of measurement Ratio level of measurement

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. b. c. d. Nominal level of measurement Ordinal level of measurement Interval level of measurement Ratio level of measurement

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

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

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 This is a true, between participant design This is a true, within participant design

Let’s try one Albert found that the standard deviation for the weight of the jockeys is 10 pounds, what is the variance? a. b. c. d. 10 pounds 100 pounds 1, 000 pounds 10, 000 pounds

Let’s try one Albert wanted to know if female jockeys have gotten better over the last 60 years, so he plotted the winning percentages for each year and looked for a trend over time. This is called a. b. c. d. Cross sectional design Time sectional design Cross series design Time series 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. This is called a. b. c. d. Cross sectional design Time sectional design Cross series design Time series 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. This is a _____ a. Between participant design b. Within participant design

Let’s try one Albert wants to know the probability of a particular horse winning the race. Albert measured the weights of the jockeys and found the following distribution. What percent of the jockeys will fall between 95 and 105 pounds (within one standard deviation of the mean) a. b. c. d. 68% 95% 99. 7% Unknown

Let’s try one Albert wants to actually calculate the standard deviation for the weight of the jockeys. So he found the deviation scores and added them up. What did he get? Σ(x - x) equals a. b. c. d. 68% 95% 99. 7% 0

Let’s try one Albert wants to know whether weight makes a difference. He created a scatterplot looking at weight and winning percentage and found that smaller jockeys won more often. Describe this relationship a. b. c. d. Strong positive Strong negative Weak positive Weak negative

Let’s try one Albert wants to know whether weight makes a difference. He plotted their weight in a frequency curve and found this: a. b. c. d. Positively skewed Negatively skewed Weakly skewed Strongly skewed

Let’s try one Albert wants to know whether weight makes a difference. He plotted their weight in a frequency curve and found this: a. b. c. d. The mean was bigger than the mode The mean was smaller than the mode The mean equaled the mode Not enough information

Let’s try one Albert wants to know whether weight makes a difference. He measured 5 jockeys and found this: 90, 95, 100, 105 and 110 a. b. c. d. The mean 100 The median is 100 The mode is 100 Both A and B are true

Let’s try one Albert wants to know whether weight makes a difference. He plotted the weight of each jockey using a Pareto Chart. How did he arrange the data? a. alphabetically from left to right b. in descending frequency from left to right with most b. frequently occurring category first c. in increasing cumulative frequency from left to right d. in any order because categorical data can appear in any order

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 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. Judy wants to evaluate the performance of individual sales team members. So she finds how much better (or worse) each sales person performed relative to the mean of their group. This difference between the individual’s performance and the mean for her group (x – μ). This value is call a _______. a. mean score b. deviation score c. standard deviation score d. variance score

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. Judy wants to evaluate the performance of individual sales team members. So she finds how much better (or worse) each sales person performed relative to the mean of their group. She then added up all of these scores Σ(x – μ). This value is ___. a. the mean score b. the deviation score c. equal to zero d. variance score

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. Judy wants to evaluate the performance of individual sales team members. So she found the deviation scores. But then squared each deviation score and added up the squared deviation scores. Σ (x – μ)2. This value is call a _______. a. mean score b. deviation score c. standard deviation score d. sum of squares score

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. Judy wants to know (on average) how much all of the team member differ from the mean. So she found the deviation scores. But then squared each deviation score and added up the squared deviation scores and divided that whole thing by the number of scores (n). Σ (x – μ)2. N Notice she did not yet take the square root. This value is call a _______. a. mean score b. deviation score c. standard deviation score d. variance

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 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

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. between participant design b. within participant design c. mixed participant design

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. quasi experimental design quasi b. true experimental design c. mixed participant design

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 quasi b. true experiment c. correlational study 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 true c. correlational study Let’s try one

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