
Data Range
The difference between the maximum and minimum member of a data set.

Class Width
The difference between the lower (or upper) limits of two adjacent classes.

Class boundary
The midpoint of the interval between the upper limit of a class and the lower limit of the next class

Class midpoint
The midpoint of the interval between the lower and upper limits of a class.

Class frequency
The number of scores that fall in a class.

Relative frequency
The number of scores that fall in a class

Cumulative frequency
The sum of frequencies for a specific class and all classes below it.

Frequency histogram
A bar diagram where each bar corresponds to one of the classes, and its height is equal to class' frequency (to some scale).

Ogive
a Graph of cumulative frequency distribution

Pie chart
A circular diagram depicting the distribution of qualitative data.

Center
A representative or average value that indicates where the middle of the data set is located.

Variation
A measure of the amount that the data values vary.

Distribution
The nature or shape of the spread of the data over the range of values (such as bellshapred, uniform, or skewed).

Outliers
Sample values that lie very far away from the vast majority of the other sample values.

Time
Changing characteristics of the data over time

CVDOT
Computer
Virus
Destroy
Or
Terminate
Center, Variation, Distribution, Outliers, Time

Class width=
 (maximum data value)  (Minimum data value)
 Number of classes

The number of classes should be between
5 & 20

Relative frequency=
 class frequency
 sum of all frequencies

Percentage frequency =
 Class frequency
 sum of all frequency x 100%

Mean
The average value of all members of a data set



Median
The middle value of a data set that is arranged in ascending or descending order.

Mode
The value most frequently occurring in a data set.

Midrange
The halfsum of the minimum and maximum value of a data set.

Weighted Mean
The mean of a data set whose members have a different significance (weight) w:

Skewness
The measure of a distribution’s asymmetry. (A leftskewed distribution has a peak shifted to the right, and vice versa.)

Standard deviation
A measure of variation taking into account each member of a data set.

Population standard deviation

Sample standard deviation

Coefficient of variation (CV)
A relative measure of variation.

z score (standardized value)
 A measure of relative standing, showing how many standard deviations the given value is below or
 above the mean.

Unusually small or unusually large values
Any values that are more than two standard deviations below or above the mean.

Quartiles
Characteristic values dividing a data set (arranged in ascending/descending order) in quarters.

Interquartile range (IQR)
The difference between the third and the first quartile.

Outlier
An extremely small or extremely large value in a data set.

Sigma
demotes the sum of a set of data values

x
is the variable usually used to represent the individual data values.

n
represents the number of data values in a sample

N
represents the number of data values in a populations.

Probability experiment
An action involving uncertainty and consisting of a number of trials for which an outcome (measurement, response etc.) is obtained.

Sample space
The set of all possible outcomes of an experiment.

Event
A collection of outcomes.

Simple event
An event that includes just one outcome.

Compound event
An event that includes two or more outcomes.

Probability Rule 1 (Relative Frequency Approximation)
If the experiment was performed m times and event A occurred f times, then the probability of event A is

Probability Rule 2 (Classical Approach)
 If the number of simple events (sample space size) is n and the number of ways the event A can occur is s, then
 the probability of event A is

Subjective probability
A probability that is based on intuitive feeling rather than on logical reasoning.

Law of Large Numbers
The larger is the number of trials, the closer is the probability by Rule 1 to the actual probability.

Range of probability
The probability of an event may be a number in the interval from 0 to 1 inclusive.
The probability of an impossible event is 0.
The probability of an event that is certain to occur is 1.

Complementary events
Two events such that one or the other must occur, but not both at the same time.

Formal Addition Rule
The probability that either event A occurs, or event B occurs, or both occur at the same time is
P(A or B) = P(A) + P(B) – P(A and B)
 where P(A and B) is the probability that both A and
 B occur at the same time.

Mutually exclusive (disjoint) events
Two events that cannot occur at the same time.

Addition Rule for mutually exclusive events
P(A or B) = P(A) + P(B)

Rule of Complementary Events
 If two events A and are complementary, then

Conditional probability (probability of B given A)
 The probability of event B occurring under condition
 that event A has occurred.

Independent events
Two event (A and B) such that the probability of B does not depend on whether A occurred or not.

Formal Multiplication Rule
 The probability of events A and B occurring consequently is
 P(A and B) = P(A)×P(B½A)

Multiplication Rule for independent events
P(A and B) = P(A)×P(B)

Rule of At Least One
P (at least 1) = 1 – P(0)

Fundamental Counting Rule
 If a procedure consists of two events, such that one can occur in m ways and the other in n ways,
 the whole procedure can occur in m·n ways.
The same principal is applicable to a procedure consisting of more than two events.

Factorial function
The product = n! is called n factorial.
This definition applies to n> 1. 0! = 1.

Permutation Rule (when all items are different)
The number of ways r items can be selected from a set of n items (r < n) and arranged in all possible orders is
 Another form of this formula:

Factorial Rule
n different items can be arranged in n! ways.

Combination Rule
 The number of ways r items can be selected
 from a set of n items (r < n) is

