1. Introductory Statistics Introductory Statistics

2. The Five Processes in Statistics
(Daniel) Design the Study
(Can) Collect Data
(Discern) Describe the Data
(More) Make Inference
(Truth) Take Action

3. Questions that you may address
Who are safer drivers at BYU-I, male/female, Utahns or non-Utahns?
Who is likely to win the presidential race?
Is a vaccination effective in treating children?
Is this drug rehabilitation program effective?

4. Population and Sample
What would be population of interest?
Who are safer drivers at BYU-I, male/female, Utahns or non-Utahns?
Who is likely to win the presidential race?
Is a vaccination effective in treating children?
Is this drug rehabilitation program effective?
Feasible to get data from everyone?

5. Descriptive Statistics
Two Types of Descriptive Statistics
Graphical
Numerical
(e.g. Average, Median, Percentage, correlation)

6. Inferential Statistics - then Take Action
Inference vs Deduction
Deduction – Most Math
Inference - Statistics
Take Sample of U.S. to find that 49% of sample would vote for Obama. We would infer this percent (with Margin of Error) to the whole population of the U.S.
Large
Small
Small - Sample
Large - Population

7. Quantitative vs Categorical
Quantitative (Numerical)
Ex. Height, Body Temperature, Cost of new medical equipment, Number of Siblings, Typical Questionnaire ratings
Categorical (Non-numerical)
Ex. Gender, Class in College, Race

8. Types of Data Collecting
Census
Taken every Ten Year
Observational Study – Collects Data but does not attempt to manipulate or influence the outcomes
Examples: Household and Governmental Surveys, Exit Polls after elections
Designed Experiment – applies a treatment to individuals and attempts to isolate the effect on the outcome
Examples: Drug, Vaccination Treatment

9. Sampling from the Population
Simple Random Sample – A Probability Sample
Consists of individuals from the population chosen in such a way that every set of n individuals has an equal chance of being selected (names out of a hat) – class example
We do Random Sampling to Minimize Bias
Other Valid Sampling Methods
Stratified Sampling – Divide Population into groups and do a SRS for each group
Cluster Sampling – Divide the Population into groups, take a SRS of the groups and sample everyone in the selected groups
Systematic Sampling - Select every kth individual and then choose the first person with a random start.
Invalid Sampling Methods – Introduce Bias
Voluntary Response Sampling
Convenience Sampling

10. Vocabulary for Experiments
Subjects – Person or object in study
Response Variable – Variable of Interest
Treatment Groups – Variable(s) that describe or explain the changes in the response variable
Principles of Experimental Design
Control – Control any possible lurking variables
Randomize – Randomize Subjects to Specific Treatments
Replicate – Have as many Subjects for each Treatment
Example - Jonas Salk – Vaccination for Polio
Double Blind Study
Placebo Group