What are the Applications of Statistical Techniques?

Introduction 

 

According to Dodge (2008), Statistics: 

[…] is made up of a set of techniques for obtaining knowledge from incomplete data, from a rigorous scientific system for managing data collection, their organization, analysis, and interpretation, when it is possible to present them in numeric form.

 

From ancient times, the field of statistics has experienced manifold transformations, the time when it was limited to accumulate information related to crime, military strength, population, wealth to the present time to meet each sphere of natural and human activity.

 

The word “statistics has multiple meanings for different individuals”, such as,

 

• For Students, statistics means grades and final exams scores.

• For the banking community, statistics implies a customer will repay the loan.

• For food and drug administrators, statistics follow the unsatisfactory influences on the particular population who consumed new drugs, and many more.

 

Therefore, statistics encloses a particular practice or compilation of methodologies (procedures, assumptions and expectations) that collect and analyze numerical form of data in order to extract interpretation and decision-making.

 

(Related blog: Top statistical terms for machine learning)

 

In simplified expression, statistics encompasses collecting, organizing, analyzing data to derive insights from sample to whole population. The field of statistics demands a proper design of study, well-structured selection of data as sample, and implementation of appropriate statistical tests. While addressing all sorts of data, statistics also involves planning of data accumulation in particular of designed surveys and experiments.

 

The purpose of this exercise is to provide you with extensive discussion over diverse applications of statistical techniques. 

 

(Must read: Importance of statistics for data science)

 

 

Importance of statistical techniques

 

Let’s briefly concentrate on what statistical techniques can benefit;

 

• In identifying association amidst different variables in data: Statistical techniques recognize the essential correlations between number of variables inside data and defined relationships among those variables that helps in understanding the problem to be addressed.

• In analyzing and interpreting data: A holistic approach towards understanding data leads to analyzing and interpreting it in a logical manner along with deriving insights out of complex information.

• In translating inferences: Statistical data analysis and interpretation make users understand how to decipher predictions for uncovered data that improves accuracy of inferences and outcomes.

• In employing suitable ML models: Different machine learning techniques/ methods can be best suited for distinct problems, a strong understanding of statistical background is desirable for the same.

• In transforming data observations into accurate information: From examining data observations to generating solutions, data experts attempt to convey precise and accurate information. Through statistical techniques, they can transform a number of observations into transparent and significant insights.

 

(Most related: Types of statistical analysis)

 

The below video explains the advanced statistical techniques and their applications covering; 

 

• Analysis of Variance (ANOVA)

• Linear Regression Analysis

• Principal Component Analysis (PCA)

• Factor Analysis 

 

 

Various applications of statistical techniques

 

1. Marketing

 

Being an integral part of latest marketing research, statistical techniques provide many interesting insights for marketers and assist in effective marketing-based decision making. These decisions consist of the process of construction, evaluation, and selection of impressive marketing strategies. 

 

For instance, to get information for marketing based decision making, statistical analysis can help in finding what to be sold first and then in evolving suitable strategies. A comprehensive data analysis over a number of matrices including purchasing power, man power, consumer nature, transportation cost, etc can be accomplished to narrow down the marketing competition and to establish new markets.

 

Statistical techniques can be applied to marketing in different ways;

 

• To understand marketing trends.

• To compute and inspect potential marketing programs.

• To explore targeted markets and audiences accurately.

• To employ effective marketing communication channels and tricks to approach it.

• To meet customer satisfaction and maintain their trust, brand loyalty and support

• To determine the correlation between marketers’ organizations and their customers.

 

 

2. Finance 

 

Statistical methods are extensively used in developing financial theory and its applications with respect to the areas of corporate finance, securities, portfolio management and analysis, financial bodies and institutions and markets.

 

The increasing demand of implementation of statistical data and advanced statistical techniques has innumerable applications in, accounting and auditing, banking, and investment, as described below;

 

• For example, in accountancy and auditing, statistical data can be analyzed over income, expenditure, investment, profits, savings, etc to evaluate the accounting records that further assist in monitoring purchasing power of money, to forecast future profits, recognition of trends, measure financial performance.

• In investment, statistically made decisions are precise and qualitative judgments that benefit investors in their investment decision in selecting securities that sound safe and have the best sources of good return. 

• Moreover, the statistical evaluation of annual investment-returns for the fixed assets leads to determine various perspectives such as the standard industrial classification of statistics in terms of types of asset, new or used assets, the value of sold assets, etc.

 

(Also read: Statistical data analysis techniques)

 

 

3. Production

 

Statistical techniques have a very significant impact in the field of production, for example, to answer what to produce, how to produce, when to produce, and many more are highly dependent on statistical data analysis. 

 

In general, manufacturing units build a large variety of products using raw materials or congregate products from distinct elements, these products (goods) are supplied to different local and international markets. The powerful statistical techniques support specialists to expedite production, statistical quality control charts and inspection methods in the manufacturing industry.

 

Statistical techniques are helpful in production as follows;

 

• To formulate designing, constructing, improving and evaluating the dependability of numerous products such as appliances, plastic materials, spare-parts of vending machines, etc.

• To facilitate the quality control method of manufacturing units at each stage of production such that product quality could be improved and met predefined standards.

• To indicate the feasibility of economic factors regarding the sales, stocks, purchase of goods and services, wages, salary distribution, etc.

 

 

4. Business Management

 

An all-inclusive statistical survey concludes productive business activities can improve the efficiency of making more persuasive decisions in terms of future activities; various statistical techniques, discussed below, help in business management;

 

• Time series analysis can be applied to decipher the behaviour of prices, production of goods, consumption of products, capital distributions, etc.

• Multiple regression analysis to measure customer perspective, demand analysis to identify the association between demand of a commodity and its supply.

• Analysis of variance and analysis of covariance are methodological in nature to determine the reliability of all experimental design and benefits. 

• Bayesian analysis to reshape different financial decisions in the context of forecasting future security prices, portfolio selection, anticipating security financial volatility, and credit analysis.

• Factor analysis to review consumer’s living behavior, nature, interest, activities and personality checks.

• Trend analysis and correlation to make economic decisions and forecasts, and regression analysis to make predictions in financial markets.

• Serial correlation analysis of residuals can be applied to almost all aspects of the financial area like security analysis, market research and studies, financial institutions, mergers and acquisitions, capital market theory and many more.

• Statistical modelling is beneficial in online auctions.

• Data mining is used in prediction markets.

• Statistical analytical procedures are used for risk management. 

 

5. Information Technology

 

For particular areas of interest, statistics makes viable predictions of special outcomes, derived from comprehensive data analysis with a nearly tiny sample of generalized area, these inferences aid in achieving reliable information. 

 

Statistical tools are very useful in the area of information technology, they are very effective in obtaining and employing information. 

 

For example, statistics supports in upgrading relevant data to regional authorities, such that

 

• Total revenue, amount transferred by functions in a particular span of time.

• Total expenditure detailing functions and economics types.

• Financial assets and liabilities

• Social security fund receipts and financial account statements

• Detailed description of social benefits

• Social security fund operations (Source)

 

Conclusion 

 

As Josh Wills put it, “a data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” 

 

Statistical methods involve preparing, designing, compiling data, deciphering and bringing out significant experimental results and reporting research findings. “The statistical amalgamation of methods and techniques provides an essence to the trivial numbers, thereby giving direction and meaning to countless data, providing an appropriate use of statistical tests for obtaining precise results and inferences.

 

Facilitating thorough understanding of statistical methods/ techniques meet the incredible wave of essential applications. Statistical methods used in these areas involve finance, banking, management, education, business planning and strategies, information technology etc.