1. Introduction to Statistics
Chapter 1
Introduction to Statistics

2. An Overview of Statistics
Section 1.1
An Overview of Statistics
What you should learn:
The definition of statistics
How to distinguish between a population and a sample
The difference between a parameter and a statistic
How to distinguish between descriptive statistics and inferential statistics

3. Vocabulary
Data -
Information coming from observations, counts, measurements, or responses.
Statistics -
The science of collecting, organizing, analyzing, and interpreting data in order to make decisions.

4. Types of Data Sets Population -
Collection of all outcomes, responses, measurements, or counts that are of interest.
Sample-
A subset of a population
Population
Sample

5. Vocabulary Parameter -
A numerical description of a POPULATION characteristic
Statistic -
A numerical description of a SAMPLE characteristic

6. Branches of Statistics
Descriptive -
The branch of statistics that involves the organization, summarization, and display of data
Inferential-
The branch of statistics that involves using a sample to draw conclusions about the population.

7. Identify the sample used in the next survey
Identify the sample used in the next survey. What is the sample’s population?

8. Assignment:
pg. 6: 1-6, 7-23 odd

9. Section 1.2 What you should learn: Data Classification
How to distinguish between qualitative data and quantitative data
How to classify data with respect to the four levels of measurement: nominal, ordinal, interval, and ratio

10. Types of Data Qualitative - Quantitative-
Data that consists of attributes, labels, or nonnumerical entries.
Quantitative-
Data that consists of numerical measurements or counts.

11. Levels of Measurement Interval - Ratio-
Data that can be ordered & you can calculate meaningful differences between data entries. A zero entry represents a position on a scale…not a zero.
Ratio-
Same as interval level of measurement, but zero represents a zero and a ratio of two data values can be calculated so that the each piece of data can be represented as a multiplier of another.

12. Levels of Measurement Nominal - Ordinal-
Data categorized using names, labels or qualities. No mathematical calculations can be made at this level.
Ordinal-
Data that can be arranged in order, but differences between data entries are not meaningful.

13. Types & Levels
Qualitative
Quantitative
Interval
Nominal
Ordinal
Ratio

14. Motion Picture Association of America Ratings Descriptions
Examples of the 4 Levels
Example of Data Set
Meaningful Calculations
Nominal Level
Major PGA Tournaments
The Masters
The US Open
The British Open
The PGA Championship
Put in a category
Ordinal Level
Motion Picture Association of America Ratings Descriptions
G General Audience
PG Parental Guidance Suggested
PG Parents Strongly Cautioned
R Restricted
NC and under not Permitted
Put in a category and put in order

15. Examples of the 4 Levels Interval Level Ratio Level
Example of Data Set
Meaningful Calculations
Interval Level
Average Monthly Temp. (F) for
Sacramento , CA
Jan Jul
Feb Aug
Mar Sep
Apr Oct
May Nov
Jun Dec
Put in a category, put in order, and find a difference in values. A zero entry represents a position on a scale…not a zero.
Ratio Level
Average Monthly Precipitation (in.) for
Jan Jul
Feb Aug
Mar Sep
Apr Oct
May Nov
Jun Dec
Put in a category, put in order, find difference in values, and find ratios in values. Zero represents a zero.

16. Assignment:
pg. 12: 1-20

17. Section 1.3 What you should learn: Experimental Design
How to design a statistical study
How to collect data by performing an experiment, using simulation, taking a census, or using a sampling
How to create a sample using random sampling, stratified sampling, cluster sampling, systematic sampling and convenience sampling

18. Methods of Data Collection
Experiment -
A treatment is applied to part of a population and responses are observed.
Simulation-
The use of a mathematical or physical model to reproduce the conditions of a situation or a process.

19. Methods of Data Collection
Census -
A count or measure of an entire population.
Sampling-
A count or measure of part of a population. The statistics are then used to predict various population parameters.

20. Advantages & Disadvantages
A census can be costly and difficult.
A simulation often saves time and money.
A simulation allows you to study situations that are impractical or dangerous.
A sample can be biased.

21. Sampling Techniques Random Sample -
Each member of a population has an equal chance of being selected. Each member of the population is assigned a number.

22. Random Sampling with the Calculator
MATH 5
randInt(lower bound,upper bount)

23. Preferred over Random Sample
Sampling Techniques
Preferred over Random Sample
Statified Sample -
A population is divided into at least 2 different subsets called STRATA, that share a similar characteristic. A sample is then randomly selected from each.
Example:
Middle Income Homes
Low Income Homes
High Income Homes

24. Less reliable, but often more practical.
Sampling Techniques
Cluster Sample -
Divide a population into groups, called CLUSTERS, then select all of the members in one or more, but not all, of the clusters.
South
Example:
West
North
East

25. Sampling Techniques Very easy to use. Systematic Sample - Example-
A population is ordered in some way and then members of the populations are selected at regular intervals.
Example-

26. Most likely to produce biased results.
Sampling Techniques
Convenience Sample -
The use of any members of a population that are readily available.

27. Assignment:
pg. 20: 1-8, 9-17 odd