Statistics Chapter 1

1. Statistics Chapter 1 Lessons 1.1 – 1.3 Instructor: Mrs. Carroll

2. 1.1 Intro to Statistics • Statistics: the science of collecting, organizing, analyzing and interpreting data in order to make decisions • Data - information coming from observations, counts, measurements or responses. The singular form is datum.

3. Data Sets • Population: the collection of all outcomes, responses, measurements or counts that are of interest • Sample: a subset of a population • **it is usually impractical to obtain all of the data. In most studies, information must be obtained from a sample

4. Population or Sample • Find the population and sample in each example: • 1) 50 Mill Creek HS seniors were surveyed to find out what song they wanted for their senior song. • 2) 2000 Republicans were polled to predict the outcome of the Republican presidential primary.

5. Results of Data Collection • Parameter: a numerical description of a population characteristic • Statistic: a numerical description of a sample characteristic • Memory Tip: Think p for p, s for s.

6. Parameter or Statistic? • 1) Of the 70 American Women surveyed, 35 said they prefer The Voice to American Idol. According to this survey, 50% of American Women prefer The Voice to American Idol. • 2) The average professor salary at Orange Coast Community College is $45,000.

7. Branches of Statistics • Descriptive Statistics: a branch of statistics that involves the organization, summarization and displaying of data. • Inferential Statistics: branch of statistics that involves using a sample to draw conclusions about a population. A basic tool in the study of inferential statistics is probability.

8. Descriptive or Inferential?

9. Descriptive or Inferential?

10. Quantitative/Qualitative Data Types

11. Quantitative/Qualitative Data Types

12. Quantitative/Qualitative Data Types • Watch out for data that looks quantitative but really isn’t! - These have no real numeric value and no meaningful order. • Examples: • Social security numbers • Phone numbers • ID #s

13. Quantitative/Qualitative Data Types • What type of data is it? • Grade point average • Jersey Numbers of Lacrosse Players • Hair Color • Number of Siblings • Student ID Number • SAT Score • Weight in pounds • Movie Rating (G, PG, PG-13, R) • Temperature in Degrees F • Finishing place in Track event (1st, 2nd, 3rd, etc.)

14. Classify Data by Level

15. Classify Data by Level - Ratio Ratio Level - __________________ data. Categorize, put in order, find meaningful differences, AND find ratios. Also must have a true zero. Like average rainfall. Zero rainfall means NO rain fell. 20 inches is twice as much rain as 10 inches. Key Questions: 1)Does zero mean “none”? • Does “twice as much” or “twice as many” make sense? Yes to both means ratio level. Examples: rainfall, money Can you think of data that is ratio level?

16. Classify Data by Level - Interval Interval Level - __________________ data. Categorize, put in order, AND find meaningful differences between values. Like average temperature. The key here is that you must be able to subtract the values to show a real difference – like, “Today was ten degrees hotter than yesterday.” Examples: temperature, dates

17. Classify Data by Level - Ordinal Ordinal Level - __________________ OR _______________ data. Categorize AND put in order, like good, better, and best product ratings. How else do you categorize with an order?

18. Classify Data by Level - Nominal Nominal Level - __________________ data. Categorizes things – like genres of music. How else do you categorize things or people?

19. Classify – Data Level • Nominal, Ordinal, Interval or Ratio • 1) Is it qualitative or quantitative? Nominal or Ordinal Ordinal, Interval, or Ratio

20. Classify – Data Level • Qualitative: Nominal or Ordinal • Can you meaningfully order it? No Yes Nominal Ordinal

21. Classify – Data Level • Quantitative Data : Ordinal, Interval or Ratio • 1) Does twice as much have meaning? No Yes Ordinal or IntervalRatio • Meaningful numeric differences? No Yes OrdinalInterval

22. Data Collection Methods

23. Data Collection Methods

24. Data Collection Methods

25. Sampling • Sampling is a count or measure of a part of the population. Sampling produces statistics. • The difference between a statistic and a parameter is that a statistic is obtained from a part of the population (sample), while a parameter is obtained by measuring the entire population.

26. Sampling TechniquesSimple Random • Every member of the population has an equal chance of being selected

27. Sampling TechniquesStratified Random • Population is separated into groups (strata) – like age groups, gender, classes, etc. • A random sample is then selected from each strata.

28. Sampling TechniquesCluster • Population is separated into groups – like age groups, gender, classes, etc. • One or more groups is selected and ALL of the members of those groups are selected.

29. Sampling TechniquesSystematic • A system (or rule) is used to select the members of the sample. • EX: every 100th customer, the first person listed on each page of the phone book, etc.

30. Sampling TechniquesConvenience • Sample is selected based on easy access for the researcher. • EX: You survey your friends or your family, or the people sitting next to you at lunch.

32. Identify Sampling Techniques (cont.) • 3. You select the first 5 people who approach you at the mall. • 4. You assign each person a number and select every 25th person. • 5. You survey the drama club and the track team to obtain information about student opinions on lunch choices.