1. Introduction to Statistics
Chapter 1
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Introduction to Statistics
Elementary Statistics
Larson Farber

2. What is Statistics?
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Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions.
Most students have enrolled in statistics because it is required. Most have no idea of what statistics is about.
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3. Important Terms Population Sample
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Important Terms
Population
The collection of all responses, measurements,
or counts that are of interest.
Sample
A portion or subset of the population. x x x x x x
These terms are used throughout the course. The arrows at the end, emphasize the connection between population parameter and sample statistic. x
Ch1 Larson/Farber

4. Important Terms Parameter: Statistic:
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Important Terms
Parameter:
A number that describes a population characteristic.
Average age of all people
in the United States
Statistic:
A number that describes a sample characteristic.
These terms are used throughout the course. The arrows at the end, emphasize the connection between population parameter and sample statistic.
Average age of people from a sample of three states.
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5. Branches of Statistics
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Branches of Statistics
Descriptive Statistics Involves organizing, summarizing, and displaying data.
Inferential Statistics Involves using sample data to draw conclusions about a population.
Descriptive statistics involve graphs creating numerical summaries. Techniques of descriptive statistics will be covered in the next chapter. Inferential statistics will be introduced in later chapters.
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6. 12/1/2018
Random Samples
Simple Random Sample: Each member of the population has an equal chance of being selected. x x x x
Discuss the use of a random number table or the random selection of numbers using a technology tool.
Discuss advantages and disadvantages of a systematic sample.
Assign a number to each member of the population.
Random numbers can be generated by a random number table, software program or a calculator.
Members of the population that correspond to these numbers become
members of the sample.

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Random Samples
Systematic Sample: Choose a starting value at random. Then choose every kth member of the population.
x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
Convenience Sample: Choose readily available members of the population for your sample.
Discuss advantages and disadvantages of a systematic sample and of a convenience sample.
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8. Random Samples Stratified Sample: Cluster Sample:
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Random Samples
Stratified Sample:
Divide the population into groups (strata) and select a random sample from each group.
Cluster Sample:
Divide the population into individual units or groups and randomly select one or more units.
A stratified sample is used to ensure that various segments of the population are represented in the sample. If a random selection of statistics students were selected and you wanted to be sure that students majoring in psychology and in business and in were included with those from all other groups, you could separate the population into three strata-psychology. Majors, business majors and others and randomly select members from each.
A cluster is a naturally occurring group. If there are 3 sections of statistics offered then a each section might represent a cluster. Usually all clusters have similar characteristic.
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9. Levels of Measurement 1. Nominal: 2. Ordinal:
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1. Nominal:
Categories, names, labels, or qualities. Cannot perform mathematical operations on this data.
Ex: type of car you drive, your major
2. Ordinal:
Data can be arranged in order. You can say one data entry is greater than another.
1st
place
Ex: TV ratings, condition of patient in hospital.
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Levels of Measurement
Data can be ordered and differences between 2 entries can be calculated. There is no inherent zero (a zero that means “none”.)
3. Interval:
Ex: Temperature, year of birth
4. Ratio:
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.
Ex. Height, weight, age
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11. Data Collection Experiment: Simulation: Census: Sampling:
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Experiment:
Apply a treatment to a part of the group.
Simulation:
Use a mathematical model (often with a computer) to reproduce condition.
Census:
A count or measure of the entire population
Give examples of each. With an experiment there is often a control group that receives a placebo. Discuss the placebo effect.
Sampling:
A count or measure of part of the population.
Ch1 Larson/Farber