Introduction to Statistics Flashcards
Statistics
is a branch of
mathematics that deals with
the collection, organization,
presentation, analysis, and
interpretation of data.
Two Types of Statistics
Descriptive and Inferential Statistics
Descriptive Statistics
Deals with the collection & presentation of data and the summarizing values that describes the group’s characteristics.
Most common summarizing values
Measures of central tendency and variation
Inferential Statistics
Deals with predictions & inferences based on the analysis & interpretation of the results of information gathered by the statistician Surrender
Common Statistical Tools of Inferential Statistics
T-test, z-test, analysis of variance (anova), chi-square, and pearson r
Two Types of Data Gathering
Census and Survey
Census
Methods of gathering data or population wherein 100 percent of the total population is being asked.
Survey
Method of gathering data or population wherein only a representative sample of total population is being asked.
Population
It is the totally of all the objects of a certain under consideration.
Population
it is a complete set of individual, objects or measurements having some common observable characteristics.
Sample
Part of a population that has the same characteristics of the given population.
Parameters
The value or measure obtained from the population.
Estimates
The value or measure obtained from the sample.
Variables
An observable characteristic or attribute associated with the population or sample being studied which makes one different from the other. It can vary in quantity or in quality.
Types of Variables
Qualitative and Quantitative Variables
Qualitative variables
Are variables that can be placed into distinct categories, according to some characteristic or attribute.
Examples of Qualitative Variables
Gender, religious preference, geographic location.
Quantitative variables
Are numerical and can be ordered or ranked
Examples of Quantitative Variables
Age, height, weight, body temperature
Measurement scales
Nominal
Ordinal
Interval
Ratio
Nominal
Classifies data into mutually exclusive categories in which no order or ranking can be imposed on the data.
Examples of Nominal
Subject taught by college instructors, sex, political party, religion, marital status
Ordinal
Classifies data into categories that can be ranked; however, precise differences between the ranks do not exist.
Example of Ordinal
Grade(A.B.C.D), judging(1” place, 2nd place, etc.), rating scale (poor, good, excellent). ranking of tennis players
Interval
Ranks data and precise differences between units of measure do exist; however, there is no meaningful zero.
Examples of Interval
SAT score, IQ, temperature
Ratio
Possesses all the characteristics of interval measurement, and there exists a true zero(a point where none of the quality being measured exists). In addition, true ratio exist when the same variable is measured on two different members of the population.
Ratio examples
Height, weight, time, salary, age
