Chapter 1 Sections 1 & 2

1. Chapter 1Sections 1 & 2 Aug. 21, 2012

2. What is statistics? • Conducting studies to collect, organize, summarize, analyze and draw conclusions from data. • Why study statistics? • Pg. 3 Read numbers 1-3, briefly summarize with your peer partner why study stats

3. Vocabulary….. • Variables: a characteristic or attribute that can assume different values • Random variables: values are determined by chance • Data: the values the variables assume • Data set: collection of data values • Data value (datum): each value in the data set

4. Two main areas: • Descriptive statistics: describes a situation • Inferential statistics: makes inferences from samples to populations • Population: all subjects that are being tested • Sample: group of subjects selected from a population Example: Our class is a population. The brown haired students are a sample of the population.

5. Consider 17, 21, 44, and 76. Are those data? • A Consumer Reports article on energy bars gave the brand name, flavor, price, number of calories, and grams of protein and fat. • Who? • What? • When? • Where? • How? • Why?

6. Try it….. • Pg. 5-6 Applying the concepts 1-1 • Read the paragraph on attendance and grades and answer the questions using complete sentences.

7. Bellringer • Read “The Worst Day for Weight Loss” on the top of p. 11. Then answer the questions below. • What are the variables under study? • What are the data in the study? • Are descriptive, inferential, or both types of statistics used? • What is the population under study? • Was a sample collected? If so, from where? • What is the relationship between the variables?

8. Section 2 Variables and Types of data Sort the data into at least 3 groups based on the type of data. Be able to explain to the class why you grouped the data that way.

9. Types of Data • Qualitative (categorical) variables: can be placed in distinct categories (ex. gender, geographic location, race) • Quantitative variables: numerical; can be ordered or ranked (ex. Age, heights, weights) • Discrete variables: countable variables (can be assigned values 0,1,2,3….) (ex. Students in a room, number of children) • Continuous variables: infinite number of values (ex. Temperature, time)

10. Levels of measurement • These four rankings from lowest to highest are Nominal, Ordinal, Interval, and Ratio; Or it’s easy to remember as NOIR. • Nominal: Data is in name only. There is no ranking system. ex. Names, Ethnicity, Eye Color, Political Parties, Zip Code • Ordinal: There is a ranking but no numbers are used and the boundaries are unclear. ex. Small, Large; Poor, Fair, Good • Interval: The values are numerical and therefore can be ranked. There is no absolute zero as a starting point. ex. IQ, Temperature, SAT scores, Time (clock) • Ratio: The values are numerical and there is a starting point of zero. ex. Speed, Money, Time (duration), Age, Weight

11. A plane technician wants to find out what the average weight of carry-on luggage is for a passenger on a plane. He decides to weigh the luggage of each passenger in line at a counter. The weights are 25, 15,4, 14,13,and 8 pounds each. • What would be the population? all the carry-on luggage for planes • What would be the Sample? the luggage of the people in line. • What would be the variable? The weight of the luggage • What would be the data? 25, 15, 4, 14, 13, and 8 in pounds • What kind of statistic would this be? Inferential • What kind of variable are we measuring? Quantitative -continuous

12. A report on the Boston Marathon listed each runner’s gender, country, age, and time. • Who? • What? • When? • Where? • How? • Why? • Variables? • Qualitative? Quantitative?

13. Applying the Concepts Pg. 9 Read the information about transportation industry and answer the questions using complete sentences.

14. Section 3

15. Four basic sampling techniques • Random sampling: using chance methods or random numbers • Ex. Random number generator • Systematic sampling: numbering each subject and then selecting every kth person • Ex. 200 people in the room, asking every 5th person • Stratified sampling: Dividing population into groups according to some characteristic that is important to the study. • Ex. Asking 5 freshman, sophomores, juniors, and seniors to rate the cafeteria food as good, average, or poor.

16. Sampling cont. • Cluster sampling: Divide the population into groups called clusters and randomly selecting some of the clusters and using all members of that cluster • Ex. • X XXXX

17. Summarized table • Pg. 13 • Summarized table of sampling methods

18. Applying the concepts • Pg. 13 • Read the paragraph. Brainstorm with your group the answers to the questions. Be prepared to have a discussion with the class.

19. Class Activity! • Come up with as many examples of the different types of sampling as you can. Work with your peer partner. Make sure to pay close attention and describe what your population is and who your subjects are. Be ready to discuss them with the class.

20. Bellringer • Study the vocabulary terms from sections 1-3 in chapter 1. • Time for a quiz! How did you study Notes 10 students Book 4 students Cards 4 students

21. Section 1.4 Observational and Experimental Studies

22. Observational Studies • Researcher simply observes what is happening or what has happened in the past and draws conclusions based on these observations. • Ex. A research study comparing the risk of developing lung cancer, between smokers and non-smokers. • There is no experiment • Findings can be flawed due to health background, diet, and exercise.

23. Experimental Studies • One of the variables is manipulated to try and determine how the manipulation influences other variables • Independent variable (explanatory variable)- the one being manipulated (changed) • Dependent variable (outcome variable)- variable studied to see if it has changed due to the change in the independent variable. • Ex. Placing different bill amounts on the ground and concluding which one is more likely to be picked up.

24. Problems with experimental studies • Hawthorne effect: subject who know they are participating in an experiment change behavior in ways that affect the study • Confounding of variables: influences the dependent variable but is not separated from the independent variable • Ex. Exercise program testing the program results but not controlling what the participates diet consists of

25. Time to think….. Read starting at the last paragraph of p.14-16. • What are advantages and disadvantages of each type of study? • What is the Hawthorne Effect? Why do you think this happens? • What is a confounding variable? How is this a problem in experimental studies? • Why might two studies on the same subject yield conflicting results? Do #1-7 on p. 16

26. Section 1.5 Uses and Misuses of Statistics

27. Read “Suspect Samples” p. 17 • Complete the table for suspect samples

28. Read Ambiguous Averages and Changing the Subject p. 17-18

29. Read • Detached Statistics • Implied Connections

30. Read • Misleading graphs and Faulty Survey Questions

31. Collect some data….. • What conclusions can we assume from the data collected from the class? • Is are assumption a use or misuse of statistics?