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Population
The collection of all objects or elements of interest in a study

Parameter
A quantitative measure that describes a characteristic of a population

Statistic
A quantitative measure that describes a characteristic of a sample

Sample
Any portion of a population selected for study

Random sample
When each and every element in the population gets an equal chance of being selected in the sample

Sampling Units
Nonoverlapping collections of elements from a population that covers the entire population

Variable
A characteristic observed on sample units that can vary from unit to unit

Quantitative Variable
A variable that can be measured numerically

Qualitative Variable
A variable that can not assume a numerical value, but can be classified into two or more categories

Discrete Variable
A variable that can assume integral values (counting numbers)

Continuous Random Variable
A variable that can assume any numberical value over a certain interval(s)

The Mean
Suppose we have a data set of sample of nobservations, say x _{1}, x _{2},x _{3}, , x _{n}. The sample mean is denoted by x̄ ^{} and defined as x̄=

The Median
The middle number when the measurements of the data set are arranged in order from least to greatest in magnitude (a Ranked data set)

The Mode
The value(s) which occur most in a data set

Quartile
There are 3 quartiles, namely Q_{1}, Q_{2 }and Q_{3} which split the data set into 4 equal parts when the given data set has been ranked

Percentile
The k^{th} percentile of a data set is denoted by P_{k} and defined as the value such that k% of the observations in the data set are less than or equal to that value or equivalently, (100k)% of observations are greater than or equal to that value.

Percentile Rank of Observation
The percentage of values in the data set smaller than this value (≠)

Range
The difference between the smallest value and the largest value in the data set

Sample Variance (s^{2})
Suppose we have a sample of n observations, say x_{1}, x_{2},x_{3}, , x_{n}. The sample variance is denoted by s^{2} and defined as the sum of the squares of the deviations of the observations in the sample from their mean.

Coefficient of Variation
Sometimes we need to compare the variability of 2 data sets that have different units of measurement

Classification
forming nonoverlapping classes or intervals into which all the observations in the given data set are placed

Class Limits
The end points of each class or interval are called the class limits. The lower endpoint of the class is called the lower limit of the class, and the upper endpoint of the class is called the upper limit of the class.

Class mark
The midpoint of each class is the class mark. Therefore class mark = of the class. The class mark for the i ^{th} class is denoted by x _{i} (book uses m _{i} )

Class Frequency
The number of observations falling into a class. The class frequency of the i^{th} class is denoted by f_{i}. The sum of all the class frequencies is equal to the number of observations in the data set.

Relative Frequency
The relative frequency of the i ^{th} class is denoted by P _{i} and defined as P _{i}= , where f _{i} is the frequency of the i ^{th} class and n is the sum of all the frequencies.

Set
A collection of welldefined objects called the elements of the set. We usually use a capital letter and curly brackets {} to denote a set.

Subset
Any part of another set is called a subset. If B is a subset of A, we write as B ⊆ A, that is whatever is in B must be in A.

Empty set
is denoted by ф and contains no elements in it

Experiement
a process by which an observation is obtained

Event
a specific collection of one or more outcome(s) of an experiment

Simple Event
An event that can't be decomposed any further

Compound Event
An event that can be decomposed into two or more simple events

Sample Size
The set of all outcomes of an experiment and is denoted by S.

Probability of an Event
The probability associated with an event A defined over the sample space S is denoted by P(A) and defined as P(A) =

Union of Events
Let A and B be any two events defined over the sample space S. The union of A and B is denoted by A∪B and defined as the set of all outcomes that are either in A or in B or in both A and B

Intersection of Events
 Let A and B be any two events defined over the sample space S. The intersection
 of A and B is denoted by A∩B and defined as the set of all outcomes
 which are in A and B (ie, common in A and B)

Mutually Exclusive (or disjointed) Events
Two events A an dB defined over the sample space S is said to be mutually exclusive if A∩B=ф. A∩B=ф implies that P(A∩B)=0

Independent Events
Two events A an dB defined over the sample space S is said to be independent events if A∩B=P(A)*P(B. In the case of two independent events, the occurrence of one event does not change the probability of the other.

The Complement
The complement of event A is denoted by Abar and defined as those outcomes in the sample space S which are not in A.

