1. An Overview of Statistics Section 1.1

2. Ch1 Larson/Farber 2 Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions. What Is Statistics?

3. Ch1 Larson/Farber 3 Important Terms Population The collection of all responses, measurements, or counts that are of interest. Sample A portion or subset of the population.

4. Ch1 Larson/Farber 4 Important Terms Parameter A number that describes a population characteristic. Statistic A number that describes a sample characteristic. Example: Average gross income of all people in the United States in 2002. Example: Average gross income of people from three states in 2002.

5. Ch1 Larson/Farber 5 Inferential Statistics Two Branches of Statistics Descriptive Statistics Involves organizing, summarizing, and displaying data. Involves using sample data to draw conclusions about a population.

6. Data Classification Section 1.2

7. Ch1 Larson/Farber Types of Data Qualitative Data -consist of attributes, labels or qualities Quantitative Data -consist of measurements, counts or quantities 7

8. Ch1 Larson/Farber 8 Levels of Measurement 1. Nominal 2. Ordinal 3. Interval 4. Ratio A data set can be classified according to the highest level of measurement that applies. The four levels of measurement, listed from lowest to highest are:

9. Ch1 Larson/Farber 9 Levels of Measurement Categories, names, labels, or qualities. Cannot perform mathematical operations on this data. Data can be arranged in order. You can say one data entry is greater than another. Show rank or position 1. Nominal: Ex: type of car you drive, your major, zip code 2. Ordinal: Ex: movie ratings, condition of patient in hospital

10. Ch1 Larson/Farber 10 Data can be ordered and differences between 2 entries can be calculated. There is no inherent zero (a zero that means none). Levels of Measurement There is an inherent zero. Data can be ordered, differences can be found, and a ratio can be formed so you can say one data value is a multiple of another. 3. Interval : 4. Ratio: Ex: Height, weight, age Ex: Temperature, year of birth

11. Experimental Design Section 1.3

12. Ch1 Larson/Farber 12 Data Collection Experiment: Apply a treatment to a part of the group. Simulation: Use a mathematical model (often with a computer) to reproduce condition. Observational Study: Observe and record what the group is doing.

13. Ch1 Larson/Farber 13 Data Collection A count or measure of part of the population. Census: A count or measure of the entire population. Sampling: Survey: Asking people questions by interview, mail or telephone

14. Ch1 Larson/Farber 14 (Simple) Random Sample: Each member of the population has an equal chance of being selected. Assign a number to each member of the population. Random numbers can be generated by a random number table, computer program or a calculator. Data from members of the population that correspond to these numbers become members of the sample. Sampling Techniques

15. Ch1 Larson/Farber 15 Sampling Techniques Stratified Sample: when it is important to have members from each segment of the population Ex. Income, race, education Divide the population into groups (strata) and select a random sample from each group.

16. Ch1 Larson/Farber 16 Sampling Techniques Cluster Sample: when the population falls into naturally occurring subgroups Ex. Zip codes, class Divide the population into clusters and randomly select one or more clusters. The sample consists of all members from selected cluster(s).

17. Ch1 Larson/Farber 17 Sampling Techniques Systematic Sample: each member is assigned a number and every kth member is chosen Choose a starting value at random. Then choose sample members at regular intervals. If k = 5, every 5 th member of the population is selected. Random.org

18. Ch1 Larson/Farber 18 Sampling Techniques Convenience Sample : Choose readily available members of the population for your sample. This often leads to biased results so it is not used very often.

19. Ch1 Larson/Farber 19 Bias Biased sample: the sample does not represent the entire population Ex: a sample of 18-22 year old college students does not represent the population of 18-22 year olds

20. Ch1 Larson/Farber 20 Bias Biased question: using a survey question that encourages people to answer a certain way Ex: Do people have the right to possess loaded guns in their homes to protect themselves and their families? Or: Do people have the right to possess loaded guns in their homes?