1. Overview of Statistics

2. Statistics
the science of collecting, analyzing, and drawing conclusions from data
The study of methods for collecting, organizing, and analyzing data.

3. Descriptive statistics
the methods of organizing & summarizing data
Procedures used to organize and present data in a convenient and communicable form.

4. Inferential statistics
involves making generalizations from a sample to a population
Procedures employed to arrive at broader conclusions or inferences about populations on the basis of samples,

5. Population
The entire collection of individuals or objects about which information is desired
The complete set of actual or potential elements about which inferences are made.

6. Parameter – Fixed value about a population
Any value calculated from population
Typically unknown because getting all of the data can be difficult
A numeric measure that describes a population; parameters are usually not computed, but are inferred from sample statistics.
US Census calculations would parameters

7. Sample
A subset of the population, selected for study in some prescribed manner

8. Statistic - Value calculated from a sample
A numeric measure computed from sample data, used to describe the sample and to estimate the corresponding population parameters.
For example, A random telephone poll

9. Variable
any characteristic whose value may change from one individual to another
Examples: height, weight, IQ, hair color and pulse rate are some of the many variables that can be measured for people.

10. Data
observations on single variable or simultaneously on two or more variables
Values of variables that have been observed.

11. Relative Frequency Frequency expressed as a portion of the whole
Example: of 2000 families sampled, 30% of the families have exactly 3 children
Therefore 600 families have 3 children
30% is a relative frequency

12. Types of variables

13. Categorical variables
(or Qualitative variables)
Identifies basic, differentiating characteristics of the population
Usually words, not numbers
Data that are descriptive but not numeric. Ex: your gender, your birthplace, the color or make of an automobile.

14. Numerical variables (Quantitative variables)
Observations are numerical
Makes sense to average these values
two types - discrete & continuous
They take numeric values.

15. Discrete (Numerical) listable set of values usually counts of items
Discrete data take counting numbers (0, 1, 2, 3, ……) Values that usually represent things that can be counted. Ex. The number of fleas on a dog, the number of times a professor is late in a semester.

16. Continuous (Numerical)
data can take on any values in the domain of the variable
usually measurements of something
Continuous data can take a range of numeric values, not just counting numbers. Ex. The height of a child, the weight of a bag of beans, the amount of time a professor is late.

17. Classification by the Number of Variables
Univariate - data that describes a single variable of the population
Bivariate - data that describes two variables of the population
Multivariate - data that describes more than two variables (beyond the scope of this course

18. Identify the following:
gender
age
hair color
smoker
systolic blood pressure
number of girls in class
categorical
numerical