Data Processing is a process of converting data into the information and it can also convert

information into a data. It means Data Processing can convert any data from one format to another.
By means of various sources, customers give their opinions as data. Information system takes that
raw data as input to produce Information as output. Hence, conversion of raw data into useful
information is accomplished through an application of data-processing.
Many usinesses are venturing to take immense advantage of it as data processing is an essential
ingredient in the market research. Data are facts or figures through which one can !ump into the
conclusion. By gathering plentiful data, "ompanies then utili#e that information for swelling their
revenue or for cutting-down the price. $hese data are presented in a systematic fashion so that it is
easy to understand, analy#e and act upon.
5 Step Approach to Data Processing:
1. Editing % $o determine the relevance of data is a crucial step in a data processing. &nce the data
has een accumulated from the different sources, the relevance of the data is een tested-out then.
'll the inappropriate data is taken out and only the relevant information is een kept.
2. Coding % 'll the needed information would e in a random order. $herefore, it needs to e
aligned into a particular system so that it is unprolematic to comprehend it. $his method other than
"oding, is also called as (netting) or (ucketing) which necessitates certain codes.
3. Data Entry % Data is entered into the software that does the eventual cross taulation. 'fter the
decision has een made on a code, edited data is than entered into the software.
4. Validation % *alidation is the second phase of (cleaning) in which thorough +uality-check is een
done. Data is doule-checked so as to ensure that the process has een done infallily.
5. Ta!lation % ,inal step is the production of the end product which is taulated in a systematic
format so that thorough analysis can e done.
Univariate analysis
It is the simplest form of quantitative (statistical) analysis. The analysis is carried out with the description
of a single variable in terms of the applicable unit of analysis.
]
For example, if the variable "age" was the
subject of the analysis, the researcher would look at how many subjects fall into given age attribute
categories.
Univariate analysis contrasts with bivariate analysis the analysis of two variables simultaneously
or multivariate analysis the analysis of multiple variables simultaneously.
[1]
Univariate analysis is
commonly used in the first, descriptive stages of research, before being supplemented by more
advanced, inferential bivariate or multivariate analysis.
[2][3]
Methods[edit]
A basic way of presenting univariate data is to create a frequency distribution of the individual cases,
which involves presenting the number of cases in the sample that fall into each category of values of the
variable.
[1]
This can be done in a table format or with a bar chart or a similar form of graphical
representation.
[1]
A sample distribution table is presented below, showing the frequency distribution for a
variable "age".
Age range Number of cases Percent
under 18 10 5
1829 50 25
2945 40 20
4565 40 20
over 65 60 30
Valid cases: 200
Missing cases: 0
In addition to frequency distribution, univariate analysis commonly involves reporting measures of central
tendency (location).
[1]
This involves describing the way in whichquantitative data tend to cluster around
some value.
[4]
In univariate analysis, the measure of central tendency is an average of a set
of measurements, the word "average" being variously construed as (arithmetic) mean, median, mode or
another measure of location, depending on the context.
[1]
For a categorical variable, such as preferred
brand of cereal, only the mode can serve this purpose. For a variable measured on an interval scale, such
as temperature on the Celsius scale, or on a ratio scale, such as temperature on the Kelvin scale, the
median or mean can also be used.
Another set of measures used in univariate analysis, complementing the study of the central tendency,
involves statistical dispersion.
[1]
These measures look at how the values are distributed around the central
tendency.
[1]
The most common dispersion measures are the range, interquartile range, and the standard
deviation.
[1]
Further descriptors include the variable's skewness and kurtosis.
In the case of time series, which can be ordered along a time scale, univariate analysis can also involve
time series analysis such as autoregression, moving average,autoregressive moving average,
or autoregressive integrated moving average models. These models describe the relation between the
current value of the variable and its various past values.
Bivariate analysis
It is one of the simplest forms of the quantitative (statistical) analysis.
[1]
It involves the analysis of
twovariables (often denoted as X, Y), for the purpose of determining the empirical relationship between
them.
[1]
In order to see if the variables are related to one another, it is common to measure how those two
variables simultaneously change together (see also covariance).
[2]
Bivariate analysis can be helpful in testing simple hypotheses of association and causality checking to
what extent it becomes to know and predict a value for the dependent variable if we know a case's value
of the independent variable(see also correlation).
[2]
Bivariate analysis can be contrasted with univariate analysis in which only one variable is analysed.
[1]
Furthermore easier, the purpose of a univariate analysis is descriptive. Subgroup comparison the
descriptive analysis of two variables can be sometimes seen as a very simple form of bivariate analysis
(or as univariate analysis extended to two variables).
[1]
The major differentiating point between univariate
and bivariate analysis, in addition to the latter's looking at more than one variable, is that the purpose of a
bivariate analysis goes beyond simply descriptive: it is the analysis of the relationship between the two
variables.
[1]
Bivariate analysis is a simple (two variable) special case of multivariate analysis (where
multiple relations between multiple variables are examined simultaneously).
[1]
Types of analysis[edit]
Common forms of bivariate analysis involve creating a percentage table or a scatterplot graph and
computing a simplecorrelation coefficient.
[1]
The types of analysis that are suited to particular pairs of
variables vary in accordance with the level of measurement of the variables of interest (e.g.
nominal/categorical, ordinal, interval/ratio). If the dependent variablethe one whose value is determined
to some extent by the other, independent variable is a categorical variable, such as the preferred brand
of cereal, then probit or logit regression (or multinomial probit or multinomial logit) can be used. If both
variables are ordinal, meaning they are ranked in a sequence as first, second, etc., then a rank
correlation coefficient can be computed. If just the dependent variable is ordinal, ordered probit or ordered
logit can be used. If the dependent variable is continuouseither interval level or ratio level, such as a
temperature scale or an income scalethen simple regression can be used.
If both variables are time series, a particular type of causality known as Granger causality can be tested
for, and vector autoregression can be performed to examine the intertemporal linkages between the
variables.
Multivariate analysis (MVA )
it is based on the statistical principle of multivariate statistics, which involves observation and analysis of
more than one statistical outcome variable at a time. In design and analysis, the technique is used to
perform trade studies across multiple dimensions while taking into account the effects of all variables on
the responses of interest.
Uses for multivariate analysis include:
Design for capability (also known as capability-based design)
Inverse design , where any variable can be treated as an independent variable
Analysis of Alternatives (AoA), the selection of concepts to fulfill a customer need
Analysis of concepts with respect to changing scenarios
Identification of critical design drivers and correlations across hierarchical levels.
Multivariate analysis can be complicated by the desire to include physics-based analysis to calculate the
effects of variables for a hierarchical "system-of-systems." Often, studies that wish to use multivariate
analysis are stalled by the dimensionality of the problem. These concerns are often eased through the
use of surrogate models, highly accurate approximations of the physics-based code. Since surrogate
models take the form of an equation, they can be evaluated very quickly. This becomes an enabler for
large-scale MVA studies: while a Monte Carlo simulation across the design space is difficult with physics-
based codes, it becomes trivial when evaluating surrogate models, which often take the form of response
surface equations.
Factor analysis is a statistical method used to describe variability among observed,
correlated variables in terms of a potentially lower number of unobserved variables called factors. For
example, it is possible that variations in four observed variables mainly reflect the variations in two
unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent
variables. The observed variables are modelled as linear combinations of the potential factors, plus
"error" terms. The information gained about the interdependencies between observed variables can be
used later to reduce the set of variables in a dataset. Computationally this technique is equivalent to low
rank approximation of the matrix of observed variables. Factor analysis originated in psychometrics, and
is used in behavioral sciences,social sciences, marketing, product management, operations research,
and other applied sciences that deal with large quantities of data.
Factor analysis is related to principal component analysis (PCA), but the two are not identical. Latent
variable models, including factor analysis, use regression modelling techniques to test hypotheses
producing error terms, while PCA is a descriptive statistical technique.
[1]
There has been significant
controversy in the field over the equivalence or otherwise of the two techniques (see exploratory factor
analysis versus principal components analysis).
Type of factor analysis[edit]
Exploratory factor analysis (EFA) is used to identify complex interrelationships among items and group
items that are part of unified concepts.
[3]
The researcher makes no "a priori" assumptions about
relationships among factors.
[3]
Confirmatory factor analysis (CFA) is a more complex approach that tests the hypothesis that the items
are associated with specific factors.
[3]
CFA uses structural equation modeling to test a measurement
model whereby loading on the factors allows for evaluation of relationships between observed variables
and unobserved variables.
[3]
Structural equation modeling approaches can accommodate measurement
error, and are less restrictive than least-squares estimation.
[3]
Hypothesized models are tested against
actual data, and the analysis would demonstrate loadings of observed variables on the latent variables
(factors), as well as the correlation between the latent variables.