Data Analysis Services :
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In almost any type of industry, data is an immensely valuable tool that can provide
you with an upper hand in developing, presenting and marketing your new products
or services to your target audience. However, the process of data analysis cannot
be carried out by anyone and requires the expertise of trained research analysts
who can gather, analyse and extrapolate valuable information from your data.
This is where SIDSOFT comes into the picture, as not every company can afford to
carry out data analysis in-house. Today, SIDSOFT offers comprehensive data analysis
services for small, medium and large corporations across a wide range of industries.
Our thorough research data analysis services will enable you to better understand
the key points within your industry and make informed decisions based on the hard
facts gathered by our research professionals.
Data Analysis Services Offered
At SIDSOFT , we offer a wide range of data analysis services especially designed
to gve you the full spectrum of information for any given component of your business.
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Sample Size Defination
The sample size of a statistical sample is the number of observations that constitute
it. The sample size is typically denoted by n and it is always a positive integer.
No exact sample size can be mentioned here and it can vary in different research
settings. However, all else being equal, large sized sample leads to increased precision
in estimates of various properties of the population.
Different Types of Sample :
There are 5 different types of sample
- Simple Random Sample : Obtaining a genuine random sample is difficult. We
usually use Random Number Tables, and use the following procedure;
1. Number the population from 0 to n
2. Pick a random place I the number table
3. Work in a random direction
4. Organise numbers into the required number of digits (e.g. if the size of the
population is 80, use 2 digits)
5. Reject any numbers not applicable (in our example, numbers between 80 and 99)
6. Continue until the required number of samples has been collected
[ If the sample is "without replacement", discard any repetitions of any number]
Advantages:
1. The sample will be free from Bias (i.e. it's random!)
Disadvantages: 1.Difficult to obtain 2. Due to its very randomness, "freak"
results can sometimes be obtained that are not representative of the population.
In addition, these freak results may be difficult to spot. Increasing the sample
size is the best way to eradicate this problem.
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- Systematic Sample :
With this method, items are chosen from the population according to a fixed rule,
e.g. every 10th house along a street. This method should yield a more representative
sample than the random sample (especially if the sample size is small). It seeks
to eliminate sources of bias, e.g. an inspector checking sweets on a conveyor belt
might unconsciously favour red sweets. However, a systematic method can also introduce
bias, e.g. the period chosen might coincide with the period of faulty machine, thus
yielding an unrepresentative number of faulty sweets.
Advantages:
1. Can eliminate other sources of bias.
Disadvantages:
1. Can introduce bias where the pattern used for the samples coincides with a pattern
in the population.
- Stratified Sampling :
The population is broken down into categories, and a random sample is taken of each
category. The proportions of the sample sizes are the same as the proportion of
each category to the whole.
Advantages:
1. Yields more accurate results than simple random sampling.
2. Can show different tendencies within each category (e.g. men and women).
Disadvantages:
1. Nothing major, hence it's used a lot
- Quota Sampling :
As with stratified samples, the population is broken down into different categories.
However, the size of the sample of each category does not reflect the population
as a whole. This can be used where an unrepresentative sample is desirable (e.g.
you might want to interview more children than adults for a survey on computer games),
or where it would be too difficult to undertake a stratified sample.
Advantages:
1. Simpler to undertake than a stratified sample.
2. Sometimes a deliberately biased sample is desirable.
Disadvantages:
1. Not a genuine random sample.
2. Likely to yield a biased result.
- Cluster Sampling :
Used when populations can be broken down into many different categories, or clusters
(e.g. church parishes). Rather than taking a sample from each cluster, a random
selection of clusters is chosen to represent the whole. Within each cluster, a random
sample is taken.
Advantages:
1. Less expensive and time consuming than a fully random sample.
2. Can show "regional" variations.
Disadvantages:
1. Not a genuine random sample.
2. Likely to yield a biased result (especially if only a few clusters are sampled).
Data collection
Data is collected from a variety of sources. The requirements may be communicated
by analysts to custodians of the data, such as information technology personnel
within an organization. The data may also be collected from sensors in the environment,
such as traffic cameras, satellites, recording devices, etc. It may also be obtained
through interviews, downloads from online sources, or reading documentation.
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Data validation
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data validation is the process of ensuring that a program operates on clean, correct
and useful data. It uses routines, often called "validation rules" "validation constraints"
or "check routines", that check for correctness, meaningfulness, and security of
data that are input to the system. The rules may be implemented through the automated
facilities of a data dictionary, or by the inclusion of explicit application program
validation logic.
Data validation is intended to provide certain well-defined guarantees for fitness,
accuracy, and consistency for any of various kinds of user input into an application
or automated system. Data validation rules can be defined and designed using any
of various methodologies, and be deployed in any of various contexts.
Data validation guarantees to your application that every data value is correct
and accurate. You can design data validation into your application with several
differing approaches: user interface code, application code, or database constraints.
There are several types of data validation.
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Data Cleaning
Once processed and organized, the data may be incomplete, contain duplicates, or
contain errors. The need for data cleaning will arise from problems in the way that
data is entered and stored. Data cleaning is the process of preventing and correcting
these errors. Common tasks include record matching, deduplication, and column segmentation.
Such data problems can also be identified through a variety of analytical techniques.
For example, with financial information, the totals for particular variables may
be compared against separately published numbers believed to be reliable. Unusual
amounts above or below pre-determined thresholds may also be reviewed. There are
several types of data cleaning that depend on the type of data. Quantitative data
methods for outlier detection can be used to get rid of likely incorrectly entered
data. Textual data spellcheckers can be used to lessen the amount of mistyped words,
but it is harder to tell if the words themselves are correct.
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Data Formulation
A statistical analysis of time series regression models for longitudinal data with
and without lagged dependent variables under a variety of assumptions about the
initial conditions of the processes being analyzed. The analysis demonstrates how
the asymptotic properties of estimators of longitudinal models are critically dependent
on the manner in which samples become large: by expanding the number of observations
per person, holding the number of people fixed, or by expanding the number of persons,
holding the number of observations per person fixed. The paper demonstrates which
parameters can and cannot be identified from data produced by different sampling
plans.
Data Audit
A Data Audit is a services engagement backed by our applications that — in a very
short timeframe — delivers report findings identifying specific data challenges
that may be hindering your operational efficiency and ability to achieve successful
business outcomes based on the health of your data.
Data Audit is the first step to delivering Business-Ready Data for organizations
focused on data quality, data migrations or data governance. Data Audits span nearly
any industry and line of business and include 17 years of prebuilt knowledge, content
and reporting to quickly discern discrepancies, anomalies and errors in business
data. The Data Audit includes more than 150 data quality checks from all relevant
sub-modules. The resulting analysis reporting includes information such as data
relevancy.
Setting data to distribution
The Distribution Fitting Process Once you have selected the candidate distributions
which can supposedly provide a good fit (see the article above), you are ready to
actually fit these distributions to your data. The process of fitting distributions
involves the use of certain statistical methods allowing to estimate the distribution
parameters based on the sample data. This is where distribution fitting software
can be very useful: it implements the parameter estimation methods for most commonly
used distributions, so you can save your time and focus on the data analysis itself.
If you are fitting several different distributions, which is usually the case, you
need to estimate the parameters of each distribution separately.
The input of distribution fitting software usually includes:
- Your data in one of the accepted formats
- Distributions you want to fit
- Distribution fitting options
The distribution fitting results include the following elements:
- Graphs of your input data
- Parameters of the fitted distributions
- Graphs of the fitted distributions
- Additional graphs and tables helping you select the best fitting distribution
Specific Applications
Even though probability distributions can be applied in any industry dealing with
random data, there are additional applications arising in specific industries (actuarial
science, finance, reliability engineering, hydrology etc.), enabling business analysts,
engineers and scientists to make informed decisions under uncertainty.
Data Analysis
Analysis of data is a process of inspecting, cleaning, transforming, and modeling
data with the goal of discovering useful information, suggesting conclusions, and
supporting decision-making. Data analysis has multiple facets and approaches, encompassing
diverse techniques under a variety of names, in different business, science, and
social science domains.
Main data analysis
In the main analysis phase analyses aimed at answering the research question are
performed as well as any other relevant analysis needed to write the first draft
of the research report.[33]
- Exploratory and confirmatory approaches
In the main analysis phase either an exploratory or confirmatory approach can be
adopted. Usually the approach is decided before data is collected. In an exploratory
analysis no clear hypothesis is stated before analysing the data, and the data is
searched for models that describe the data well. In a confirmatory analysis clear
hypotheses about the data are tested. Exploratory data analysis should be interpreted
carefully. The confirmatory analysis therefore will not be more informative than
the original exploratory analysis.
- Stability of results
It is important to obtain some indication about how generalizable the results. While
this is hard to check, one can look at the stability of the results. There are two
main ways of doing this:
(1) Cross-validation: By splitting the data in multiple parts we can check
if an analysis (like a fitted model) based on one part of the data generalizes to
another part of the data as well.
(2) Sensitivity analysis: A procedure to study the behavior of a system or
model when global parameters are (systematically) varied. One way to do this is
with bootstrapping.
- Statistical methods
Many statistical methods have been used for statistical analyses. A very brief list
of four of the more popular methods is:
(1) General linear model: A widely used model on which various methods are
based (e.g. t test, ANOVA, ANCOVA, MANOVA). Usable for assessing the effect of several
predictors on one or more continuous dependent variables.
(2) Generalized linear model: An extension of the general linear model for
discrete dependent variables.
(3) Structural equation modelling: Usable for assessing latent structures
from measured manifest variables.
(4) Item response theory: Models for (mostly) assessing one latent variable
from several binary measured variables (e.g. an exam).
Data Inference
When making sampling distribution inferences about the parameter of the data, θ,
it is appropriate to ignore the process that causes missing data if the missing
data are ‘missing at random’ and the observed data are ‘observed at random’, but
these inferences are generally conditional on the observed pattern of missing data.
When making direct-likelihood or Bayesian inferences about θ, it is appropriate
to ignore the process that causes missing data if the missing data are missing at
random and the parameter of the missing data process is ‘distinct’ from θ. These
conditions are the weakest general conditions under which ignoring the process that
causes missing data always leads to correct inferences.
Data Simulation
Simulation is a tool for managing change. Simulation provides more than an answer:
it shows you how the answer was derived; it enables you to trace from cause to effect;
and it allows you to generate explanations for decisions. Simulation is a component
of a business rules engine. You can view simulation as a solution to both off-line
design and on-line operational management problems.
1)Forecasting data and methods
The appropriate forecasting methods depend largely on what data are available. If
there are no data available, or if the data available are not relevant to the forecasts,
then qualitative forecasting methods must be used. These methods are not purely
guesswork- there are well-developed structured approaches to obtaining good forecasts
without using historical data.
- Quantitative forecasting: can be applied when two conditions are satisfied:
- Numerical information about the past is available.
- It is reasonable to assume that some aspects of the past patterns will continue
into the future.
- Cross-sectional forecasting : With cross-sectional data, we are wanting to
predict the value of something we have not observed, using the information on the
cases that we have observed. Cross-sectional models are used when the variable to
be forecast exhibits a relationship with one or more other predictor variables.
The purpose of the cross-sectional model is to describe the form of the relationship
and use it to forecast values of the forecast variable that have not been observed.
Under this model, any change in predictors will affect the output of the system
in a predictable way, assuming that the relationship does not change. Models in
this class include regression models, additive models, and some kinds of neural
networks
- Time series forecasting : Time series data are useful when you are forecasting
something that is changing over time (e.g., stock prices, sales figures, profits,
etc.). Examples of time series data include:
1. Daily IBM stock prices.
2. Monthly rainfall.
3. Quarterly sales results for Amazon.
4. Annual Google profits.
Anything that is observed sequentially over time is a time series.
2)Finalization Of Results
Finalization varies significantly between languages and between implementations
of a language, depending on memory management method, and can generally be partially
controlled per-object or per-class by a user-specified finalizer or destructor,
Finalization is primarily used for cleanup, to release memory or other resources:
to deallocate memory allocated via manual memory management; to clear references
if reference counting is used (decrement reference counts); to release resources,
particularly in the Resource Acquisition Is Initialization (RAII) idiom; or to unregister
an object.
Benefits of Data Analysis
The data analysis services that we offer can help any type of business across a
range of industries leverage a large number of tangible benefits. The following
are the top three benefits that your company can leverage by partnering with us:
- We will inspect, clean and transform your data to create models that will highlight
the important information within your business and provide you with insights that
can give you a competitive edge over other companies in your industry.
- With our advanced data analysis services, you can make key decisions while receiving
important conclusions that might otherwise have been hidden within massive or disorganized
data sets.
- By using cutting edge statistical tools and working with only the best trained statisticians
and data management experts in the research field, we can ensure that your analysis
needs are met for every major component of your business, whether you need your
financial data to be analysed and presented or are interested in key decision points
based on competitor data.
To ensure that you receive only the best data analysis services, partner with a
company who is trusted by executives and research firms around the globe - SIDSOFT.
Contact us today to start data analysis services and leverage the benefits.