eChapters

True/False

Q.1.    In many clinical research studies, the regression analysis is used to develop models for prediction of an outcome from one or more explanatory variables.
Q.2.    Now a days, the linear, logistic and Cox (proportional hazards) regression models are used frequently in medical research.

Introduction

The successful combination of technology and finance, commonly referred to as Financial Technology (henceforth, FinTech), can be understood as “technology enabled financial innovation” that may result in new business applications, models, products and/or processes, that create manifold material impact on f inancial markets and provision of financial services (Schueffel, 2016).

FinTech presents a promising prospective of bettering the situation of financial inclusion in developing countries like India by leveraging on technology in an innovative manner. Inclusive growth can be achieved through FinTech by means of broad-based prosperity, reduction in transaction costs and better access to financial services to the underserved (Sahay et al., 2020). In this context, Fintech companies are institutions applying technological innovations to increase efficiency and create enhanced access to the finance industry. There has been tremendous growth of such companies in recent years and it has also captured the interest of investors alike.

Introduction

Indian economy is basically an agrarian economy. In spite of all challenges and difficulties faced over the years, the sector still serves as the lifeblood of the nation, by standing as the major source of livelihood for over half of the population by guaranteeing food security for its teeming millions. A significant contribution to the GDP of the nation is still ensured by the agriculture sector. The development of a nation highly centres on its primary agriculture sector and Indian economy have a much diverse dependence on the agriculture sector in terms of its growth and development. The nation straightaway necessitates a tremendous shift entirely to sustainable agricultural practices in its development trajectory.

Despite its undeniable significance and praised role, Indian agriculture currently faces multifaceted challenges, which significantly restrict the production and productivity level of the sector as a whole and the farmers in particular. Its high time that the Indian agriculture sector, which stands as the backbone of the nation needs to go through a looming transformation. The industry 4.0 revolution is a vibrant platform for the sector to make a complete transformation. The traditional face of the sector will transform towards a modern makeover by taking up technological advancement and digitalisation. The wider transformative opportunities will ensure a return of the Indian agriculture sector to the forefront in the nations growth and development.

Introduction

Strategy in the global context requires a thorough understanding of the external environment, including economic, political, cultural, and technological factors that could impact an organization's operations. Organizations must also understand their internal strengths, weaknesses, opportunities, and threats to develop effective strategies that align with their goals. A global strategy involves developing a standardized approach to business activities that can be applied across different countries and cultures, while also adapting to local Market Conditions. Companies that operate globally must consider the differences in legal and regulatory environments, cultural norms, and consumer preferences when developing their strategies. Global strategy requires collaboration across different regions and functions within an organization to achieve a shared vision and common goals. The use of technology and data analytics is crucial in developing and executing global strategies, as it can help organizations identify new market opportunities, monitor changes in the external environment, and make informed decisions. Companies achieving success in formulating and executing global strategies must demonstrate agility and rapid adaptability to external environmental shifts. Additionally, they should maintain a steadfast commitment to continuous improvement and innovation to maintain a competitive edge.

Introduction

External environmental analysis is a critical process that organizations undertake to assess and understand the various factors and forces that exist outside their immediate operational boundaries.

It involves examining the external environment in which a business operates to identify opportunities, threats, and potential influences that may impact its performance and strategic decisions.

External environmental analysis is the systematic evaluation of factors outside an organization's control that can significantly impact its operations, strategies, and overall success.

Purpose The aim of conducting an external environmental analysis is to acquire a deeper understanding of the opportunities and threats that exist within the external environment. By comprehending these elements, organizations can make informed choices and craft efficacious strategies to adjust and prosper in an ever-evolving business environment.

Factors to Analyze: Highlight the main factors that organizations typically analyze during external environmental analysis. These encompass the Political, Economic, Social, Technological, Environmental, and Legal (PESTEL) factors.. Additionally, industry-specific factors such as competition, customer behavior, and market trends should also be considered.

Tools and Techniques: Briefly mention some common tools and techniques used in external environmental analysis. These methodologies may involve SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, the application of Porter's Five Forces framework, scenario planning, trend analysis, and market research.

Introduction to Internal Environment Analysis

Internal environment analysis is a critical component of strategic management that focuses on evaluating an organization's internal factors and resources. It involves assessing the internal strengths and weaknesses to gain insights into the organization's capabilities and competitive advantage. Here are some key points to highlight when discussing the introduction to internal environment analysis:

Internal environment analysis refers to the process of examining and evaluating an organization's internal factors, including its resources, capabilities, and core competencies. It helps identify strengths and weaknesses that can influence the organization's performance and competitiveness.

Purpose: The primary purpose of internal environment analysis is to understand the organization's internal dynamics and uncover key areas where it has a competitive advantage or faces challenges. It enables organizations to identify their core competencies and allocate resources effectively to achieve strategic goals.

Introduction to Corporate Governance

Corporate governance encompasses the framework of rules, practices, and processes that guide the direction, control, and management of a company. It involves the interactions among various stakeholders, including shareholders, management, the board of directors, employees, customers, suppliers, and the community. Successful corporate governance is vital for a company's long term success and sustainability. It helps establish a framework for responsible decision-making, accountability, and transparency, which enhances investor confidence and protects the interests of stakeholders.

In this chapter some preliminary terminologies have been provided which are very much useful and frequently used in dealing with the theory of sample surveys and its application.

Q.1 Cluster sampling restricts the spread of sample over the population which results generally in increased sampling variance of estimators under considerataion.

Q.2 In adopting cluster sampling, few selected clusters are enumerated partially.

Q.1 A sampling procedure in which the units are selected with probability proportional to their some size measure, is known as sampling procedure with probability proportional to size (P.P.S.) Notations: Let there be a population of N units say Yi , (i = 1,2, ......... N), YN with their selection probabilities Pi (i=1, 2........... , N). Here Pi ? Pj V i ? J. = 1, 2, .............. N. Let yi , (i = 1, 2, ...................n) be the sample drawn from the population with probability of selection pi (i = 1, 2 -, n).

The satellite data is available in raster format. Raster data consists of two – dimensional array of pixels or grids which stores the values representing features. All the pixels are of same size and shape with their particular coordinate location. A raster data format is shown below.

Q.1 If two type ot factors, say (A & B) have to be studied in the same experiment where A requires larger plot size and B requires smaller plot size and structure of the experiment is that of plots within blocks and blocks within replication then such layout is known as split plot design.

Q.1 Let there be two factors A and B with p and q levels respectively which are applied to the larger strip by dividing the experimental material into p rows and q columns. The factors A and B are applied at random to these rows and columns respectively. Such a layout is known as strip plot design.

The sensors present over remote sensing satellites are designed to collect energy or radiance from the varied surface features having wide range of radiance i.e. from features like water bodies having very low radiance to features like sand or snow having very high radiance. But, most of the time, the study area may not have all these features; therefore, radiance values may remain confined to a narrow range. This reduces the contrast of the image, thereby decreases its visual quality. In this context, image contrast enhancement techniques are used for effective visualization and display of remote sensing image on computer screens or hard copy so that maximum amount of information can be derived through its visual interpretation.

Contrast enhancement of a digital image is a technique to modify the original grey levels of input image to the entire available range of the grey levels. This increases the contrast between various features in the image which improves the visual quality of original image. Contrast enhancement can be done by both linear and non-linear methods. Some of the commonly used contrast enhancement techniques with their implementation in R software are discussed in this chapter.

We need following packages for performing this exercise in R. raster package is required for importing of raster image and for performing contrast enhancement. RStoolbox package is also required for contrast enhancement as well as for visualization of raster data with ggplot2 package. rasterVis package is mainly used in this exercise for plotting histogram of the raster data.

Statistics

It is a branch of mathematics, which comprises several areas like Algebra, Trigonometry, Calculus, Geometry, Statistics and Probability etc.

Biostatistics is a mathematical science which analyses numerical facts or biometric data of biological events following certain rules like design of experiments, analysis of data, presentation of data with tables, figures hypothesis testing, making a probability prediction and conclusion etc.

or it is an art of analysis and presentation of data following certain rules and international norms accepted by international journals for pharmacology experiments, animal farm data, human and animal medicines data, epidemiology data, non-parametric data etc.

What is statistics?

The number of introductory or elementary texts on the subject of statistics indicates how important the subject has become for everyone in the biological sciences. However, the fact that there are many texts might also suggest that we have yet to discover a fool proof method of presenting what is required. The problem confronted in biological statistics is as follows:

When there is a need to make a set of numerical observations in biology, it has been observed that the values are scattered. Then questions arise to know whether these values differ because of factors e.g. treatments or these are part of a ‘background’ i.e., natural variation. You need to evaluate what the numbers actually mean, and to represent them in a way that readily communicates their meaning to others.

Introduction

In recent years, there has been an exponential increase in the volume of datasets generated across various application domains. However, a prevalent issue within these datasets is class imbalance, wherein the distribution of samples is biased towards the major class, resulting in an extremely skewed dataset. Number of samples in the major class far exceeds those in the minor class, sometimes with an extreme ratio like 1/1000 or even higher. While traditional machine learning models trained on such imbalanced datasets often yield high accuracy for the overall dataset and the major class samples, their ability to correctly predict class labels for minor class samples is signicantly compromised. In many real-world scenarios, the minor class samples hold greater signicance and importance compared to the major class samples. Therefore, researchers are increasingly focusing on addressing the challenges posed by class imbalanced datasets and exploring solutions to improve the performance of machine learning models in handling such imbalances

Important Definitions

• Sampling method or sampling technique is the process of studying the population by gathering information (on basis of samples) and analyzing that data.

• Population: The whole group we are interested in census.

• Census: A collection of data from the whole population

• Sample: A collection of data from part of the population

• Parameter Vs Statistics: Parameter is statistical measure pertaining to population whereas statistics is measure pertaining to sample.

• Estimate-Value of population parameter obtained from sample.

• Livestock census carried every 5 years (2019-20th livestock census)

What is Least Square Method?

The Least Squares Method is used to derive a generalized linear equation between two variables, one of which is independent and the other dependent on the former. The value of the independent variable is represented as the x-coordinate and that of the dependent variable is represented as the y-coordinate in a two-dimensional cartesian coordinate system. Initially, known values are marked on a plot. The plot obtained at this point is called a scatter plot. Then, we try to represent all the marked points as a straight line or a linear equation. The equation of such a line is obtained with the help of the least squares method. This is done to get the value of the dependent variable for an independent variable for which the value was initially unknown. This helps us to fill in the missing points in a data table or forecast the data. The method is discussed in detail as follows. The least-squares method was officially discovered and published by Adrien-Marie Legendre (1805), though it is usually also co-credited to Carl Friedrich Gauss (1809), who contributed significant theoretical advances to the method.

Least Square Method Definition

The least-squares method can be defined as a statistical method that is used to find the equation of the line of best fit related to the given data. This method is called so as it aims at reducing the sum of squares of deviations (residual part) as much as possible. The line obtained from such a method is called a regression line.

Introduction

Artificial intelligence is an effective technology that makes it possible for computers and other devices to simulate human intellectual abilities and ability to solve problems. Comprehensive study on artificial intelligence may be traced back to Alan Turing’s development of the renowned Turing Test in the 1950s, which examined whether machines are capable of thinking. (Turing, 1950), and John McCarthy came up with the phrase “artificial intelligence” in 1955 when he organised the 1956 Dartmouth Summer AI Research Project. They suggest that “making a machine behave in ways that would be called smart if a human were doing so” is an issue with AI (McCarthy et al., 1955). Compared to humans, artificial intelligence can analyse data, both organised and unstructured, substantially faster and more accurately.

9.1  Write True/False

Q.1    In the subject matter of order statistic, the useful beta distribution is written as :