1. Do Now  47 TCNJ students were asked to complete a survey on campus clubs and activities. 87% of the students surveyed participate in campus clubs and activities. If you went to TCNJ, you would probably find a club or activity that you would like to participate in.  Identify the population.  Identify the sample.  Is this study descriptive, inferential, or both?

2. Objectives:  Identify types of data  Identify the measurement level for each variable  Identify the four basic sampling techniques Standards:  IC.A.1  Understand statistics as a process for making inferences about population parameters based on a random sample from that population.

3. What is Statistics? Variables and Data Qualitative  Variables/data that can be placed into distinct categories, according to some characteristic or attribute.  Examples:  Color  Gender  Religious preference  Geographic location  Others?? Quantitative  Numerical; can be ordered or ranked  Examples:  Age  Height  Weight body temperature  Others??

4. Qualitative or Quantitative??  A) Classify the colors of automobiles on a used car lot.  B) Classify the number of complaint letter received by the United States Postal Service in a given day.  C) Classify the number of seats in a movie theater.  D) Classify the numbers on the shirts of a girl’s soccer team.

5. Types of Quantitative Data Discrete  Assume values that can be counted.  Examples  Suppose we flip a coin and count the number of heads. The number of heads could be any integer value between 0 and infinity. However, it could not be any number between 0 and infinity (can’t have 2.5 heads). Continuous  Can assume an infinite number of values between any two specific values.  Are obtained by measuring.  Often include fractions and decimals.  Examples:  Suppose the fire department mandates that all fire fighters must weigh between 150 and 250 pounds. The weight of a fireman can take on any value from 150 to 250.

6. Measurement Scales VARIABLES CAN BE CLASSIFIED BY HOW THEY ARE CATEGORIZED, COUNTED, OR MEASURED. THESE CLASSIFICATIONS USE MEASUREMENT SCALES.

7. 1. Nominal Level  Examples:  Classifying residents by zip code  Classifying teachers into content areas  Classifying by political party  Classifying by marital status  Classifies data into mutually exclusive (nonoverlapping), exhausting categories in which no order or ranking can be imposed on the data.

8. 2. Ordinal Level  Examples:  Classifying gymnasts into first, second, and third place  Classifying people into small, medium, or large build  Classifying grades A, B, C, D, F  NOTE: precise measurement of differences in ordinal level of measurement does not exist  Classifies data into categories that can be ranked; however, precise differences between ranks do not exist.

9. 3. Interval Level  Ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero.  Examples:  Classifying IQ – There is a 1 point difference between IQ 109 and IQ 110.  Classifying temperature – There is a 3 degree difference between 75 and 78 degrees.  NOTE: Zero has no true meaning.  IQ tests do not measure people who have no intelligence  0 degrees F does not mean there is no heat at all.

10. 4. Ratio Level  Possesses all the characteristics of interval measurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population.  Examples:  Measuring height, weight, area, number of phone calls received.  If one person can lift 200 lbs and another can lift 100 lbs, then the ratio between them is 2 to 1 – the first person can lift twice as much as the second person.

11. Activity: Place the following terms under the appropriate level of measurement.  Political Affiliation  SAT Score  Salary  Age  Ranking of Tennis Player  Nationality  Judging (1 st, 2 nd, 3 rd place)  Temperature  Zip Code  Religious Affiliation  Gender  IQ  Time  Height  Rating Scale (poor, good, excellent)  Eye color  Major Field (mathematics, biology)  Grade (A, B, C, D, F)  Weight

12. SKILLS CHECK ABOUT TRANSPORTATION SAFETY

13. Data Collection WHAT WAYS CAN YOU THINK OF TO COLLECT DATA??

14. Data Collection  Surveys  Telephone surveys  Mailed questionnaire  Personal interview  Other ways of collecting data  Surveying past records  Direct observation

15. Sampling Techniques TO OBTAIN SAMPLES THAT ARE UNBIASED, STATISTICIANS USE FOUR BASIC METHODS OF SAMPLING: RANDOM, SYSTEMATIC, STRATIFIED, AND CLUSTER SAMPLING.

16. Sampling Techniques Random  Samples are selected by using chance or random numbers  Examples:  Numbering each subject in the population. Place number cards into a bowl, mix, and choose as many as needed. Systematic  Numbering each subject of the population and then selecting every nth subject  Example:  Suppose there are 200 subjects in the population and a sample of 50 is needed. Since 200/50=40, you could take every 40 th subject in the population to select for your sample.

17. Sampling Techniques Stratified  Dividing the population into groups (called strata) according to some characteristic that is important to the study, then sampling from each group.  Example:  President from a two-year college wants to learn how students feel about some issue. Furthermore, the president wishes to see if the opinions of the first-year students differ from the opinions of the second-year students. The president will select students from each group to use in the sample. Cluster  The population is divided into groups called clusters by some means such as geographic area or schools in a large school district, etc. Researcher randomly selects some of these clusters and uses all the members of the selected clusters as the subjects of the sample.  Example:  I want to survey apartment dwellers in a city. If there are 10 apartment buildings in the city, I can select at random 2 of the 10 and interview all of the residents of those 2 buildings.

18. Sampling Techniques  Other techniques:  Convenience sample  Researcher uses subjects that are convenient  Not the most representative technique – researcher has to check how representative the sample would be before conducting the survey/gathering the data.

19. Exit Ticket