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·Quantitative or numerical data arise when the
observations are frequencies or measurements.

·The data are said to be discrete if the measurements are integers (e.g. number ofemployees of a company, number of incorrect
answers on a test, number of participants in a program…)

·The data are said to be continuous if the measurements can take on any value, usually
within some range (e.g. weight).Age
and income are continuous quantitative variables. For continuous variables,
arithmetic operations such as differences and averages make sense.

Analysis can take almost any form:

ÞCreate groups or categories and generate
frequency tables.

·Some quantitative variables can be treated only
as ranks; they have a natural order, butthese values are not strictly measured.Examples are:1) age group (taking the values child, teen,
adult, senior), and 2) Likert Scale data (responses such as strongly agree,
agree, neutral, disagree, strongly disagree).For these variables, the distinction between adjacent points on the
scale is not necessarily the same, and the ratio of values is not meaningful.

·This data describes the quality of something in
a non-numerical format.

·Counts can be applied to qualitative data, but
you cannot order or measure this type of variable. Examples are gender, marital
status, geographical region of an organization, job title….

·Qualitative data is usually treated asCategorical variable.

With categorical data, the
observations can be sorted according into non-overlapping categories or by
characteristics. For example, shirts can be sorted according to color; the
characteristic 'color' can have non-overlapping categories: white, black, red,
etc.People can be sorted by gender with
categories male and female. Categories should be chosen carefully since a bad
choice can prejudice the outcome. Every value of a data set should belong to
one and only one category.