eChapters

Section-A : Write True / False

Q.1 A better and more beautiful way to present the data in a more visual form can be done by means of diagram.

Q.2 Description of data through diagrams and graphs is better for layman.

Q.3 Diagram does not register a meaningful impression almost before we start to think about it.

Q.4 Diagrams are never used by the business man and government agencies for advertisement and educating people.

Q.5 Diagrams do not give visual presentation of data.

Q.6 The diagrams put a rememberance for a longer time in mind of human being.

Q.7 Diagrams facilitate comparison and reveal pattern of data.

Q.8 Diagrams show approximate values of items in data.

Q.9 Diagrams are more attractive, appealing and impressive than tables.

Q.10 Diagrams can be more easily compared and interpreted than the tables.

Q.11 Tables contain precise figures while diagrams indicate approximate figures.

Diagrams and graphs are the effective and attractive ways of presenting data. Widely used diagrams for data presentation are pi chart and bar diagrams. Bar diagrams are also of various types; (i) Simple bar diagram (ii) Component or sub-divided bar diagram (iii) Multiple bar diagram (iv) Percentage bar diagram. The widely used graphical presentation are histograms, frequency polygon and frequency curve.

There are numerous libraries to create charts and graphs in R programming language.

Pie-chart- A pie-chart is a representation of values of variable as sectors of a circle with different colours/shadings. The colours/shadings used for different sectors are indexed at the side of the graph. pie() function is used to create pie chrt in R.

Let us create an exel file names as ‘exp_sports’ which contains the expenditure made (in Rs.) under various sports in a sports club. The name of the sports are cricket, hockey, tennis and football. The names of sports are placed under the column head ‘sports’. The corresponding expenditure (in Rs.) are 7200, 3600, 1800, 1800 and they are placed under the column head ‘expenditure’.

The basic syntax for creating a pie-chart using the R is.

Diagrammatic Presentation of Data

The quantitative data when classify are converted into averages and if the number of average is large then it is not possible to draw a conclusion from their large numbers of averages. Moreover quantitative data are dull, confusing, boring for many of us. Therefore, a statistical device to make such confusing data more attractive was developed i.e. if such complex data are presented by geometric f igures, pictures, graphs or curves then it will become more attractive and more impressive, is called as diagrammatic representation of data. These diagrams are nothing but the use of geometrical figures to improve the overall presentation and offer visual assistance for the reader.

Types of Diagrams

Generally, diagrams are made for a special series or a time series and they are divided into five different categories:

1) One dimensional diagrams

2) Two dimensional diagrams

3) Three dimensional diagrams

4) Pictograms

5) Cartograms

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Introduction

Micro, small, and medium enterprises (MSMEs) dominate the Indonesian economy. These enterprises account for around 99 percent of all businesses in Indonesia and employ more than 95 percent of the total workforce across the country. They should be the primary engines of economic growth, although they generate only approximately 56 percent of the overall gross domestic product (GDP) of Indonesia. Around 80 percent of MSMEs are in the trading sector involved in buying and selling existing products/services). Within the MSME group, micro and small enterprises (MSEs), including those in the manufacturing industry, called micro and small industries (MSIs), i.e. business units with up to a maximum of 500 million Indonesian rupiah (IDR), are the ones that receive the most attention from the Indonesian government because they account for almost 99 percent of total MSMEs. So, compared to medium and large industries (MLIs), MSIs are the largest job creators in the country. These enterprises also have high resilience in various previous crises, including the COVID-19 pandemic. Their high resilience has played a role as a cushion for the economy because of their ability to survive periods of pressure and grow back faster and higher after pressure.

9.1 Section-A : Write True / False

Q.1 The probabilistic laws for dif erent values of a random variable are made on the basis of given conditions. T ese conditions are known as "prior considerations".

Q.2 If the probabilistic laws are made for dif erent values of a random variable on the basis of results obtained from an experiment, then such results are known as "a posterior inferences".9.1.1 Discrete Uniform Distribution

Q.3 A random variable X is said to have a discrete uniform distribution over the range [1, n] if its p.m.f is written as

9.1  Write True/False

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

1.1 R and R studio

R has become the world-wide language for statistics, predictive analytics, and data visualization. R is not limited to statistics; it has a wide range of tools for spatial analysis and digital image analysis and can handle any process needed for image analysis flexibly. It offers a wide range of methodologies for digital image processing, from the most basic to the most complex. As an open source project, it’s freely available for a range of platforms, including Windows, Mac OS X, and Linux. An integrated development environment (IDE) for R, R Studio, allows interactive execution of R functions with user interface. RStudio is available in two formats - RStudio Desktop and RStudio Server - both available in free and fee-based (commercial) editions.

1.1.1. Download and install R

The R software setup files can be downloaded from any of the widely distributed Comprehensive R Archive Network (CRAN). To download R, please choose your preferred CRAN mirror, a location close to you. Follow the following steps:

Aerial photography is objective of getting panoramic view of earth surface to analyze the features of earth and their interrelation at particular time period which may be changed in further events. In this concerned, the observation may play a vital role in achieving the goals of photography. Undoubtedly it is widely used in social function and so on, but still awaited for scientific or research use. Drone can measure the geometry of ground surface at lower altitude with more accuracy in the form of visual feature in natural colour. flight height of drone mainly limits the large area coverage due to lower flying and drone can work perfectly at low altitude planning with good quality feature/photography.

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
Environmental science plays a critical role in understanding and addressing the complex interactions between human activities and the natural world. This chapter explores the intersection of environmental science and agriculture, emphasizing the importance of ecological balance for sustainable development. As agriculture continues to be a major driver of environmental change, understanding its impacts on ecosystems, biodiversity, and climate is crucial for developing strategies that promote sustainability.
The chapter begins by examining the environmental impacts of agriculture, including land degradation, deforestation, and pollution. It then explores agroecology and biodiversity, highlighting the importance of maintaining diverse ecosystems to support sustainable agricultural practices. Sustainable resource management is discussed as a key approach to conserving natural resources and minimizing the environmental footprint of agriculture.
Climate-smart agriculture, a modern concept that integrates environmental sustainability with agricultural productivity, is introduced as a solution to mitigate the effects of climate change while ensuring food security. The chapter also covers environmental regulations and policies, which are essential for guiding sustainable agricultural practices and protecting the environment.
As the chapter progresses, it delves into the importance of environmental science in food security, the impact of environmental factors on global warming, and various modern concepts in environmental science, including sustainable food systems, circular economy, and ecosystem-based approaches. The chapter concludes with a discussion on combating food security and global warming challenges through sustainable practices and policies.
Environmental science plays a crucial role in agriculture by studying the interactions between agricultural practices and the natural environment. This chapter explores various aspects of environmental science in agriculture, including the environmental impacts of agriculture, agroecology and biodiversity, sustainable resource management, climate-smart agriculture, and environmental regulations and policies. Agriculture and the environment are inherently interconnected, as agricultural practices have both direct and indirect impacts on the natural environment. Environmental science in agriculture aims to understand these impacts and develop sustainable practices that minimize negative environmental

True/False

Q.1.    The purpose of most medical research is to identify the causes of disease and search out the ways to prevent or cure them.
Q.2.    Now a day, epidemiology has become a basic science for public health
Q.3.    The studies of diagnostics therapeutic and prognostic procedures have successfully incorporated into growing stream of “Clinical Epidemiology”.

Introduction to Evaluation in Strategic Management Process

Strategic management is the process of formulating and implementing strategies to achieve an organization's goals and objectives. It entails making decisions and taking actions that align the organization with both its external environment and internal capabilities. Evaluation is a crucial component of the strategic management process as it helps assess the effectiveness and efficiency of the strategies implemented. It involves the systematic and objective assessment of various aspects of the organization's performance, including its strategy, resources, operations, and outcomes. The primary purpose of evaluation in strategic management is to determine whether the strategies are yielding the desired results and to identify areas for improvement. It offers valuable insights into the organization's strengths, weaknesses, opportunities, and threats, enabling informed decision-making and adjustments to strategies as necessary.

Agriculture has progressed from labour-intensive traditional practices through mechanization and the Green Revolution to precision agriculture and Agriculture 5.0. This chapter traces that historical development and identifies limitations associated with conventional farming, including inefficient resource use, environmental degradation and weak adaptability. It explains how GPS-guided machinery, sensors and precision technologies prepared the foundation for intelligent systems. Artificial neural networks, deep learning and fuzzy logic now process data from IoT devices, UAVs and satellites to support automated irrigation, crop monitoring and soil analysis. The chapter emphasizes that integrating human knowledge with computational power is increasingly essential for global food security, sustainability and climate resilience.

Modern farm planning involves nonlinear, discontinuous and multi-objective problems that conventional optimization methods may not solve effectively. This chapter examines Genetic Algorithms, Particle Swarm Optimization and Differential Evolution for improving agricultural resource allocation. These nature-inspired techniques can simultaneously address crop yield, water use, fertilizer application, environmental effects and financial viability. Their integration with deep-learning forecasts and crop-simulation systems enables more realistic optimization. Multi-objective evolutionary algorithms identify Pareto-optimal alternatives, allowing decision-makers to balance productivity and ecological sustainability. The chapter also emphasizes explainable artificial intelligence, economical implementation and farmer usability as necessary conditions for transferring evolutionary optimization methods from experimental research to practical farm management.

11.1 Introduction

In the preceding chapter 10, we have discussed in detail, the test procedures for which it has been assumed that the sample size n is large, (i.e n >,30). Those tests are also known as approximate tests or z tests. But in case when sample size n is small i.e. n<30, the approximate test fails to deal with such problem and we apply the theory of exact sample, for small sample tests. The exact sample tests however can be applied to large samples also though the converse is not plausible. In all the exact sample tests, the basic assumption is that the parent population is normally distributed.

In this part we will discusse in details the exact sample tests (based on exact sampling distributions namely snedekor’s F, student’s t and chi square (c²) distributions. These distributions will be simply introduced in short because main emphasis has to be given only on the test procedures based on them.

14.1 Section-A : Write True / False

Q.1 If sample size n is small ie n < 30, then the small sample test is applicable.

Q.2 The exact sample test (small sample test) can be applied to large sample test but the converse is not plaussible.

Q.3 If (x²1 , x²2 ) are two independent chi-square variates with (V1, V₂) degree of freedom then F statistic is def ned as

In this chapter, the focus is on understanding the importance of experimental design and being familiar with key terminology. You’ll also learn about the three fundamental principles of experimental design and their roles in conducting experiments. Additionally, you’ll learn how to do randomization of treatments in various experimental designs using R.

Experimental Design

Experimental design is the methodical planning of scientific experiments for reliable results by controlling variables and minimizing biases. It serves as the foundation for effective research. Some key definitions to enhance understanding of this concept:

Key terminologies

• Experiment: Structured investigation for knowledge acquisition or validation.

• Experimental unit: Entity receiving treatment (e.g., land plot, pot).

• Treatment: Specific procedure for assessment and comparison.

• Experimental error: Variation among identically treated units.

Principles of Experimental Design

Experiments are vital tools in agricultural research, aiming to assess treatment effectiveness and quantify their impact. However, extraneous factors can confound results. To mitigate these issues, we depend on three core principles of experimental design: replication, randomization, and local control.

Introduction

A payment gateway is a server that is located between the web server of the merchant and the web server of the acquiring bank. When a merchant receives an order, it is transferred to the payment gateway by the merchant server.

The payments sector in India is set to reach USD 100 trillion in volume and USD 50 trillion in transactional value by the end of 2030. The payment gateways, which are also a part of the payments sector are set to reach USD 1.21 billion in India, growing at a CAGR of 17.6% between 2024-2029. The global payment gateway landscape as of 2024 stands at USD 16.27 billion growing at a CAGR of 16.43%. Payment gateways across the world are credit card-based gateways, with the introduction of card readers in mobile phones. The Fintech and payments sector boom in India cannot be justified without mentioning UPI (Unified Payments Interface), which has encompassed payment solutions for small street vendors to large conglomerates with its user-friendly interface and QR-code-based payments. UPI is one of the major payment gateways in India, in addition to IMPS (Immediate Payment Service), RTGS (Real Time Gross Settlement), and NEFT (National Electronic Funds Transfer) and many more.