Notes 1.3 (Part 1)

An Overview of Statistics

What you will learn• 1. How to design a statistical study

• 2. How to collect data by taking a census, using a sampling, using a simulation or performing an experiment.

• 3. How to create a sample using random, simple random, stratified, cluster and systematic sampling techniques and how to identify a biased sample.

Experimental Design

• Guidelines to designing a statistical study

• (6 steps system)

• 1. Identify variable of interest and population of study.

• 2. Develop a detailed plan for collecting data. (If using a sample, make sure it represents the population).

• 3. Collect data.

• 4. Describe data using descriptive statistics.

• 5. Interpret data and make a decision using inferential statistics.

• 6. Identify any possible errors.

Four types of data collection

• 1. Census: count or measure of an entire population. Costly and difficult to perform.

• 2. Sampling: count or measure part of a population statistics used to predict a population parameter. More practical than taking a census.

• 3. Simulation: Mathematical or physical model to reproduce the conditions of impractical or even dangerous situations or processes. Usually involves using computers. Saves time, money and lives.

• 4. Experiment: A treatment is applied to part of a population and responses are observed. Another part receives no treatment (or a placebo). After responses from both groups are collected they are compared and analyzed to see if the procedure worked. Usually used in medicine or marketing.

Notes 1.3 Part 2

Sampling techniques

• A sample can be done in many different ways. We will discuss six different methods.

• 1. Random sample: one in which every member of population has an equal chance of being selected.

• 2. Simple random sample: One in which every member of a population is assigned a number and then numbers are randomly selected.

• 3. Stratified sample: When its important for a sample to have members from each segment of the population. The population is divided into two or more different subsets called stratas, that share similar characteristics. These characteristics can be items such as age, gender, income level, political preferences and many others. People from each stratas are then chosen randomly. This ensures each segment of the population is represented.

• 4. Cluster sample: When a population falls into naturally occurring subgroups, having similar characteristics (live in same area or zip code, same class, same school, etc).

• 5. Systematic sample: a person is chosen in a systematic manner, such as every 3rd person, 5th person, or 100th member is selected.

• 6. Convenience sample: this type of sample leads to biased studies. Consists of only available people. (Not recommended)

Assignment #9

• Pg 20 1 - 4, 9 – 20

• Pg 14 1 - 8