top of page
  • Nucot

Statistical Computing

INTRODUCTION: Statistics computation / Computational statistics is the interface of computer science and statistics. It is the area of computational science or scientific computing specific to the mathematical science of statistics. In this blog, we will be discussing what is statistical computing, its model, analysis, and the terms of statistical computing and its measures.

What is Statistical Computing?

Statistical computing /computational statistics is scientific computing that is specific to the

Mathematics science of statistics. The goal is to transform the raw data into statistics in traditional statistics.

Statistical Modelling

Statistical modelling is the process where statistical analysis is applied to a

dataset. It is a Mathematical representation of the observed data. There are

two types of statistical modelling they are descriptive statistics and

inferential statistics

Importance of Statistical Modelling in Data Analysis

  • Statistical modelling is an approach that helps the researchers discover something about the phenomenon that is assumed to exist.

  • Modelling is a unifying strategy that brings estimation and hypothesis tests together under the same umbrella. Estimation is the process of generalizing the finding from one study to a targeted population. This test helps in determining how complicated the statistical model should be.

  • A modelling approach constructs a summary model that displays current knowledge. The models are then fitted to the Data.

  • All statistical procedures can be put into a modelling framework.

Statistical Analysis

Several statistical principles functions, and algorithm that can be used to

analyse primary data, build a statistical model and predict the outcomes.

An analysis of any situation can be done in two ways Statistical analysis and non-statistical analysis

1. Statistical analysis: Statistical analysis are also called the quantitative

the analysis is the collection of collecting, exploring, and presenting large

amounts of data to identify the patterns and the trends.

2. Non-statistical analysis: Non-statistical analysis is also called a

qualitative analysis It provides generic information and includes text,

sound, still images and moving images

Statistical Terms

There are various statistical terms that one should be aware of while dealing with

statistics and they are as follows

1. Population: A population is a group from which Data is to be collected.

2. Sample: A sample is a subdivision of a population.

3. Variable: A variable is a feature characteristic member of the population

differing in quality or quantity from another member.

4. Quantitative Variable: A variable that differs in quantity is called as a

quantitative variable.

5. Qualitative Variable: A variable that differs in quality is called a qualitative

variable or attribute.

6. Discrete Variable: A discrete variable is one in which no value can be

assumed between two given values.

7. Continuous Variable: A continuous variable is one in which any value can

be assumed between the two given values.

Types of Statistical Measures

There are four Statistical measures used to describe data are Measures of

Frequency, Measures of Central Tendency, Measures of Speed, Measures of


  • Measures of Frequency: The frequency of the data indicates the number of

Of time a data occurs on any particular data value in the given dataset.

The measure of frequency is number and percentage.

  • Measures of Central Tendency: Central tendency indicates whether the data values gather in the middle of distribution or toward the end.

The measure of central tendency is mean median and mode.

  • Measures of Spread: Spread describes how similar or varied the set of observed values are for a particular variable.

The measures of spread are standard deviation, variance, and quartiles.

The measures of speed are also called measures of dispersion.

  • Measures of Position: Position identifies the exact location of a particular data value in the given data set.

The measures of position are percentiles, quartiles, and standard scores.

Conclusion: Use of Statistics helps in planning a proper statistical inquiry in

any field of study effectively and efficiently and they help in collecting

appropriate quantitative data. Statistical computing helps to use the proper

methods to collect the Data, employ the correct analyses and effectively and effectively

present the results.

22 views0 comments

Recent Posts

See All
IT Infrastructure Solutions


Data Science

Artificial Intelligence, Machine Learning, and Data Science with Python

IT Staffing Company


IT Design Services


IT Infrastructure Consultant


IT Infrastructure Services


bottom of page