1. + StatisticsChapter 1 Sections 1-4 Mrs. Weir

2. + Ch 1: Introduction to Statistics What is Statistics? What words come to mind when you hear the word statistics? Where have you seen statistics?

3. + Statistics: The science of collecting, organizing, analyzing, and interpreting data in order to make decisions. Words that might come to mind: data, percents, means, averages, surveys, polls, graphs, and charts. Places you might have seen them: advertisements, sports, articles, newspapers, and research journals.

4. + Branches of Statistics Descriptive Statistics: the branch of statistics that involves organizing, summarizing, and displaying data. Keyword: DESCRIBE

5. + Examples of Descriptive Statistics Looking at the percent of each color M&M. Collecting data on how many pets are in households in your neighborhood.

6. + Inferential Statistics The branch of statistics that involves using a sample to draw conclusions or generalizations about a population.

7. + Examples of Inferential Statistics Taking a sample of M&Ms then making an inference that there’s a higher percent of blue M&Ms. Doing a study to see if there is a connection between eating breakfast and doing well in school.

8. + Data Consist of information coming from observations, counts, measurements, or responses. The singular for data is datum.

9. + Examples of Data Observing students’ hair color. Measuring students’ height. Surveying students on their favorite TV show.

10. + Population The collection of ALL outcomes, responses, measurements, or counts of interest. Ex: All seniors at SHS.

11. + Sample A subset of a population Ex: Students in this class would be a sample of all senior in SHS.

12. + Parameter A numerical description of a population characteristic. Ex: Weight of all seniors at SHS.

13. + Statistics A numerical description of a sample characteristic. Ex: Weight of students in this class would be a statistic if the population was all seniors at SHS.

14. + Data Lists America League Baseball Teams: Boston Red Sox, Chicago White Sox, Cleveland Indians, Detroit Tigers Report Card Grades: A+, A, A-, B+, B, B- Average Temperature in Shrewsbury in each month: 31, 34, 43, 54, 66, 74, 79, 78, 69, 59, 47, 36 Prices of Concert Tickets: $50, $40, $25, $75, $69, $48, $99

15. + Questions About Data Lists Looking at the lists of data what do they have in common? How are they different? What meaningful mathematical operations can be performed on each?

16. + Nominal Level of Measurement: Data at this level are categorized using names, labels, or qualities. So it’s qualitative only. No mathematical computations can be made. Ex: Eye color, Team’s names, Hair color.

17. + Ordinal Level of Measurement: Data that represents categories that have an associated order (often ranking.) Standard mathematical operations are not defined for ordinal data. Examples: Giving a rating to your service at a fast food restaurant as very good, good, fair, bad, or very bad. Number of stars a movie gets.

18. + Interval Level of Measurement: If the data can be ordered and the arithmetic difference is meaningful. At the interval level, a zero entry simply represents a position on a scale. The entry is not a meaningful zero. Ex: Water temperature, years on a timeline

19. + Ratio Level of Measurement: Similar to interval data, except that it has a meaningful zero point and the ratio of two data points is meaningful. One data value can be expressed as a multiple of another. If one person weighed 100 lbs and another person weighs 200 lbs the 200 lb person weighs twice as much, a 1:2 ratio. Ex: Students’ height or weight.

20. + Categorical (Qualitative Data) Consist of attributes or labels. Ex: Eye color, baseball teams

21. + Could categorical data consist of numbers?

22. + YES! Phone numbers Jersey numbers Social security numbers The above examples are all numbers that are labels.

23. + Quantitative Data Consist of numerical measurements or counts. Ex: Heights of Students, Weights of Chairs, Time waiting at red lights

24. + Discrete Data: Data in which the observations are restricted to a set of values that possess gaps. Ex: Data values that are prices: $2.34, $2.48, $2.99, $3.56, and $3.26. Data values that are whole numbers, number of pets: 1, 3, 2, 1, 0, 1, 2, 1, 1, 2, 2

25. + Continuous Data: Data that can take on any value within some interval. Ex: The data values of the length of a sofa. Any value between 0-15 feet. Weight of a dog 0-250 pounds.

26. + Now fill in notes on Chapter 1 Section 2

27. + Look at articles. Look at what types of data you can find in the articles. What level is the data? (Nominal, Ordinal, Interval, Ratio) Is it Quantitative or Qualitative? If Quantitative, is it Discrete or Continuous?