UNIT 1 CHAPTER 1 SUMMARY

SECTION 1.1

STATISTICS is the study of how to

  • Collect,
  • Organize,
  • Analyze, and
  • Interpret data.

“DATA consists of information from observations, counts, measurements, or responses.”

POPULATION data is from every individual or item of interest.

SAMPLE data is from only a part of the individuals or items of interest.

Examples:

1)All American Polling contacts 2,000 United States teenagers between the ages of 13 and 17. There were asked about prescription drug for mental disorders.

Population: All US teenagers

Sample: 2000 US teenagers

2)A soda taste-tester randomly selects 100 bottles of cola that were filled on April 30 to assess the taste quality of the mixing machine.

Population: All cola filled on April 30

Sample: The 100 bottles of cola filled on April 30

3)A large college observed that an increasing number of full-time students are enrolling in online classes. The administration randomly selects 200 full-time students and asks the question: “Are you taking online classes?”

Population: All full-time students at the college

Sample: 200 full-time students at the college

4)The U. S. Bureau of Census publishes a Current Population Report. This report is based on a survey of 50,000 households. The report objective is to learn the demographics of all households within the United States.

Population: All United States households

Sample: 50,000 U. S. households surveyed

5)Top-Notch Credit Agency did a study on credit cards. They observed 150 credit cards and found they the average late fee is $31.43.

Population: The collection of all credit cards

Sample: The collection of 150 credit cards observed

6)In a survey of 2500 family-practice doctors, it was found that approximately 75% considered leaving the practice of medicine because they were unhappy about the healthcare system in the United States.

Population: All family-practice doctors in the United States

Sample: The 2500 family-practice doctors surveyed

BRANCHES OF STATISTICS:

DESCRIPTIVE statistics involves methods to organize, picture, and summarize information from samples or populations.

 INFERENTIAL statistics involves methods of using information from a sample to draw conclusions regarding the population.

SECTION 1.2

Variable data:

  • QUANTITATIVE variable has a value or numerical measurement for which operations such as addition or averaging make sense.
  • QUALITATIVE variable describes an item by placing the item into a category or group, such as male or female.

Levels of measurement:

  • NOMINAL level applies to data that consist of names, labels or categories.
  • ORDINAL level applies to data that can be arranged in order. It can be qualitative or quantitative but math calculations are meaningless.
  • INTERVAL level applies to data values that can be arranged in order and math calculations are meaningful but have not inherent zero starting point.
  • RATIO level applies to data that can be arranged in order, math calculations are meaningful. The data has a true zero starting point.

Levels of Measurement Examples

Nominal: Data is put the data in categories. Nominal is qualitative only. Data are categorized using labels, names or qualities. No math calculations can be made at this level.

Examples:

Hair color (red, brown, blonde)

Type of music (Pop, Rock, Jazz, Hip Hop, Classical)

The 3 Political parties in the 110th Congress (Republican, Democrat, Independent)

Gender (Male or Female)

Ordinal: Data is put into order by data categories. It can be qualitative or quantitative. Data can be arranged in order or ranked (worst to best, smallest to largest) but differences between the data are not meaningful.

Examples:

Class surveys (Poor, Fair, Good, Excellent)

Movie Ratings (G, PG, PG-13, R, NC-17, X)

The top 5 basketball teams in the ACC tournament

Pain levels ( scale of 0 to 10)

Interval: Data can be put in order and can be used to perform numerical calculations. Data values can range above and below zero.

Example:

Temperature ( remember Fahrenheit and Celsius temperature can be negative which is below zero)

Average monthly temperature in Washington, DC

Highest grade level completed found on an employment applications

Year of college graduation

Ratio: Data can be put in order the data and can be used to perform numerical calculations. There is a zero value, a point from where the data starts.

Example:

Cost of new car age, height, weight.

Height

Weight

The length (in inches) of a sample of Rainbow trout caught in Oregon waters.

The average ticket prices for 10 country music concerts in 2007.

On employment application, the number of years on last job

SECTION 1.3

SAMPLINE TECHNIQUES

SIMPLE RANDOM SAMPLE of size n from a population is a subset such that

1)every sample if size n has an equal chance of being selected and

2)every member of the population has an equal chance of being included in the sample.

STRATIFIED SAMPLE: The population is divided into distinct subgroups called strata.

SYSTEMATIC SAMPLE: Elements of the population are arranged in a natural order and then a sample is taken using a systematic method, such as every 10th item.

CLUSTER SAMPLE: Divide the demographic areas into sections and then sample every member of the chosen cluster.

CONVENIENCE SAMPLE: The sample is obtained using a convenience or readily available group.

SECTION 1.3

Experiment Design of a Statistical Study

  1. Identify the variable and population
  2. Develop a detailed plan
  3. Collect the data
  4. Describe the data using descriptive techniques
  5. Interpret the dat
  6. Identify possible study flaws

Methods of Data Collection

  • Random Sample
  • Observational Study
  • Experiment
  • Simulation
  • Survey