1. CHAPTER 1: INTRODUCTION TO STATISTICS SECTION 1.1: AN OVERVIEW OF STATISTICS

2. Statistics – The science of collecting, organizing, analyzing and interpreting data in order to make decisions Data – information coming from observations, counts, measurements, or responses Where have you seen statistics being used before?

3. DATA SETS: POPULATIONS VS. SAMPLES A population is the collection of all outcomes, responses, measurements, or counts that are of interest A sample is a subset of a population

4. In a recent survey, 1708 adults in the US were asked if they think global warming is a problem that requires immediate government action. 939 of the adults said yes. Identify the population and the sample.

5. The US Department of Energy conducts weekly surveys of approximately 800 gasoline stations to determine the average price per gallon of regular gasoline. On Feb. 12, 2007, the average price was $2.24 per gallon. Identify the population and the sample.

6. Parameter – a numerical description of a POPULATION characteristic Statistic – a numerical description of a SAMPLE characteristic **P’s stay together, and S’s stay together **Population = parameter **Sample = statistic

7. DISTINGUISH BETWEEN A PARAMETER AND STATISTIC 1. A recent survey of a sample of MBAs reported that the average salary for an MBA is more than $82,000. 2. Starting salaries for the 667 MBA graduates from the University of Chicago Graduate School of Business increased 8.5% from the previous year. 3. In a random check of a sample of retail stores, the Food and Drug Administration found that 34% of the stores were not storing fish at the proper temperature. 4. In 2006, major league baseball teams spent a total of $2,326,706,685 on players’ salaries.

8. BRANCHES OF STATISTICS Descriptive Statistics – the branch of statistics that involves the organization, summarization, and display of data. Inferential Statistics – the branch of statistics that involves using a sample to draw conclusions about a population.

9. SECTION 1.1 ASSIGNMENT Pg. 8 - 11 #1 - #36 ALL

10. SECTION 1.2 DATA CLASSIFICATION

11. Data can be just about ANYTHING pertinent to the question at hand: Data about Students at BJSHS:

13. TYPES OF DATA Qualitative Data – consists of attributes, labels, or nonnumerical entries (movie ratings, favorite color, teams, etc…) Quantitative Data – consists of numerical measurements or counts (amounts, times, etc…) NOTE: NUMBERS DO NOT MEAN QUANTITATIVE

14. LEVELS OF MEASUREMENT 1.Nominal – qualitative only 2.Ordinal – qualitative or quantitative 3.Interval – quantitative only 4.Ratio – quantitative only

16. LEVELS OF MEASUREMENT 1. Nominal – categorized by names, labels or qualities Yes/No Questions Jersey Numbers Names Hair Color

17. 2. Ordinal – able to be ranked or ordered, difference mean nothing particular S/M/L/XL shirts 1 st, 2 nd, 3 rd,… Movie Ratings

18. 3. Interval – when 0 does NOT mean “nothing”; can’t find ratios Temperature Years (NOT TIME BETWEEN THINGS) 4. Ratio – when 0 means “none” or “nothing”; true count, ratio between two data points can be formed Population # of pages in a book Length Price/Money

19. SECTION 1.2 ASSIGNMENT Case Study on Page 17 (SUBMIT) [Groups of 3 or less] INDIVIDUAL: Pg. 15 – 16 #1 - #24 ALL (Level of Measurement means: nominal, ordinal, interval or ratio)

20. EXPERIMENTAL DESIGN SECTION 1.3

21. DESIGNING A STATISTICAL STUDY 1.Identify the variables 2.Develop a plan for collecting data 3.Collect the data 4.Describe the data (using DESCRIPTIVE statistics) 5.Interpret the data (using INFERENTIAL statistics) 6.Identify any possible errors.

22. DATA COLLECTION 1.Do an Observational Study 2.Perform an Experiment 3.Use a Simulation 4.Use a Survey

23. DATA COLLECTION 1.Observational Study - Researcher observes and measure characteristics of interest, but does NOT change existing conditions.

24. DATA COLLECTION 2. Perform an Experiment - a TREATMENT is applied to part of a population and responses are observed - Control Group – part of population where NO treatment is applied - Subjects are given a PLACEBO – harmless, unmedicated treatment that is made to look like the real treatment - Effects of treatment can be compared to control group - Subjects of a study also knows as EXPERIMENTAL UNITS

25. DATA COLLECTION INSIGHT IN AN OBSERVATION STUDY, A RESEARCHER DOES NOT INFLUENCE THE RESPONSES, IN AN EXPERIMENT, A RESEARCHER DELIBERATELY APPLIES A TREATMENT BEFORE OBSERVING THE RESPONSES.

26. DATA COLLECTION 3. Use a Simulation -Use of a mathematical or physical model to reproduce the conditions of a situation or process -Allows you to study situations that are impractical, or dangerous -Saves companies time and money

27. DATA COLLECTION 4. Use a Survey -An investigation of one or more characteristics of a population -Customer Service Surveys -QUESTIONS MUST BE WORDED SO THEY DO NOT LEAD TO BIASED RESULTS

28. Which method of data collection would you use to collect data for each study? 1.A study of the effect of exercise on relieving depression? 2.A study of the success of graduates of a large university finding a job within on e year of graduation.

29. EXPERIMENTAL DESIGN 3 KEY ELEMENTS OF A WELL- DESIGNED EXPERIMENT 1.CONTROL 2.RANDOMIZATION 3.REPLICATION

30. EXPERIMENTAL DESIGN: CONTROL Confounding variable – occurs when an experimenter cannot tell the difference between the effects of different factors on a variable Example: -Coffee Shop owner redecorates to attract more costumers -At the same time, a shopping mall nearby has a grand opening -VARIABLES ARE CONFOUNDED

31. EXPERIMENTAL DESIGN: CONTROL PLACEBO EFFECT – when a subject reacts favorably to a placebo when in fact, he or she has been given no medicated treatment at all To avoid this, we use BLINDING

32. EXPERIMENTAL DESIGN: CONTROL BLINDING – WHEN THE SUBJECT DOES NOT KNOW WHETHER HE OR SHE IS RECEIVING A TREATMENT OR A PLACEBO DOUBLE BLINDING – NEITHER THE SUBJECT NOR THE THE EXPERIMENTER KNOWS IF THE SUBJECT IS RECEIVING A TREATMENT OR PLACEBO (PREFERRED)

33. EXPERIMENTAL DESIGN: RANDOMIZATION Randomization – process of randomly assigning subjects to different treatment groups 1.Completely Randomized Design 2.Randomized Block Design 3.Matched Pairs Design

34. EXPERIMENTAL DESIGN: RANDOMIZATION 2. Randomized Block Design - Divide subjects with similar characteristics into blocks, and randomly assign subjects to treatments within each block All Subjects 30 – 39 year olds ControlTreatment 40 – 49 year olds ControlTreatment

35. EXPERIMENTAL DESIGN: RANDOMIZATION 3. Matched-Pairs Design -Subjects are paired according to a similarity -Subjects may be paired based on age, residency, etc. -One receives one treatment, and the other receives another treatment

36. EXPERIMENTAL DESIGN: REPLICATION Replication – repetition of an experiment using a large group of subjects -More subjects, more value added to the result of your experiment -We’re always looking for a large sample size

37. SAMPLING TECHNIQUES 1.Census – count or measure of ENTIRE population 2.Sampling – count or measure of PART of a population -Random Sample -Simple Random Sample -Stratified Sample -Cluster Sample -Systematic Sample -Sampling Error – difference between the results of a sample and those of the population

38. SAMPLING TECHNIQUES Sampling Error – difference between the results of a sample and those of the population Biased Sample – one that is NOT representative of the population from which it is drawn. Example: A sample of 18 – 22 year old college students would NOT be representative of the entire 18 – 22 year old population in the country.

39. SAMPLING TECHNIQUES Random Sample – every member of the population has an equal chance of being selected Simple Random Sample – every possible sample of the same size has the same chance of being selected USE OF RANDOM NUMBER GENERATORS!

40. SAMPLING TECHNIQUES WHEN IT IS IMPORTANT FOR THE SAMPLE TO HAVE MEMBER FROM EACH SEGMENT OF THE POPULATION Stratified Sample – members of population are divided into two or more subsets (strata), then sample is randomly selected from each strata **Ensures that each segment of the population is represented

41. SAMPLING TECHNIQUES WHEN THE POPULATION FALLS INTO NATURALLY OCCURRING SUBGROUPS CLUSTER SAMPLE – Divide the population into groups (clusters), and select ALL of the members in one or more (but NOT ALL) of the clusters. **Must be important that all clusters have similar characteristics

43. SAMPLING TECHNIQUES: INSIGHT For STRATIFIED SAMPLING, each of the strata contains members with a certain characteristic. For CLUSTERS, each consist of geographic groupings, and should consist of members with ALL characteristics. -Stratified – Some of members of each group are used -Cluster – All of members of one or more groups are used

44. SAMPLING TECHNIQUES -Systematic Sample – a sample in which each member of the population is assigned a number, those members are then ordered and then sample members are selected at regular intervals starting with the starting number. #########

45. SAMPLING TECHNIQUES Convenience Sample – sample consists only of available members of population (not recommended)

46. ASSIGNMENT Pg. 25 #1 - #14, #17 - #26 (identify sampling technique) Pg. 27 #29- #30

47. HOMEWORK SELECTED ANSWERS Section 1.1 5. False 6. True 7. True 8. False 9. False 10. True 11. Pop 12. Sam 13. Sam 14. Pop 15. Sam 16. Pop 21. Pop: all adults in US Sam: 1000 surveyed 22. Pop: all infants in Italy Sam: 33043 infants in study 23.Pop: all households in US Sam: 1906 households surveyed 24. Pop: all computer users Sam: 496 students surveyed 29. Statistic 30.Statistic 31.Parameter 32. Parameter 33. Statistic 34. Parameter 35. Statistic 36. Parameter Section 1.2 1.N and O 2.O, I and R 3.False 4.False 5.False 6.False 7.Qualitative 8.Quantitative 9. Quantitative 10. Qualitative 11. Qualitative 12. Quantitative 13. Qualitative, O 14. Qualitative, N 15. Qualitative 16. Quantitative, R 17. Qualitative, O 18. Quantitative, R 19. O 20. R 21. N 22. R 23. I, N, R, O 24. I,N,I,R Section 1.3 5.True 6.False 7.False 8.False 9.Fasle 10.True 11.P an E 12.Survey 13.Simulation 14.Census 17. SRS 18. Stratified 19. Convenience 20. Cluster 21. SRS 22. Systematic 23. Stratified 24. Convenience 25. Systematic 26. SRS 29. Census 30. Survey