eChapters

Section-A : Write True / False

Q.1 The moment generating function (m.g.f) of a random variable X (about origin having the probability function f (x) is defned as

In modem days, many informations receive through electronic media with instant connecting by satellite based information on social, economical, environmental etc. the informations are coded with feasible electronic encrypt and delivered to people. Similarly remote sensing, GIS (Geographical Information System) and GPS (Global Positioning System) also serve world for better understanding the ground reality along with reducing cost of operation, transforming data through electronic webs easily. In earlier phase, ground related information was not easily obtained because all kind of work had been done manually using analog maps of earth; even map draw was done by drafting from totposheet or hand maps, no interface of analog and digital world.

GPS stands for Global Positioning System, basically Navstar GPS is a radio based space aviation system developed by United state government which was operated by United State Air Force. It provides geographic location and time related information to GPS receivers under global navigation satellite from anywhere of earth where unobstructed line of sight to 4 or more GPS satellite co-ordinate (Anonymous, 2018). Even GPS not required any user for transmitting data, with working independently on any telephonic or internet receivers; through these technologies we can enhance usefulness of the GPS location based information. The GPS serves as critical positioning capacity to various fields like army operation, civil aviation, and commercial scope to wider scope in around world.

Introduction 
Chapter 16, “Global Warming and Food Security” delves into the intricate relationship between global warming and food security. The chapter opens by defining the multifaceted concept of food security, which includes availability, access, utilization, and stability. It emphasizes the critical importance of these components in ensuring a stable and reliable food supply. The chapter then explores various challenges to food security, highlighting the impact of rapid population growth, climate change, land and water scarcity, poverty, inequality, and conflicts. It underscores how these challenges are compounded by global warming, which exacerbates environmental stressors such as rising temperatures, altered weather patterns, and extreme weather events. The implications for agriculture are profound, affecting crop yields, livestock health, and overall food production. The section on the causes and consequences of global warming outlines key phenomena such as risi ng temperatures, changing weather patterns, melting glaciers, and ocean acidification. It illustrates how these factors disrupt agricultural systems and food distribution networks, leading to increased food insecurity. To address these issues, the chapter discusses various strategies for enhancing food security in a changing climate. These include adopting climate-smart agricultural practices, diversifying crop and livestock systems, developing climateresilient crop varieties, and implementing efficient water management strategies. It also highlights the importance of policy and institutional interventions, supportive measures for smallholder farmers, and research and development initiatives. The chapter concludes with a discussion on the need for collaboration and partnerships among various stakeholders, including governments, international organizations, and the private sector, to effectively address the dual challenges of global warming and food security. Global warming, a consequence of human-induced climate change, has emerged as one of the most pressing challenges of our time. As the Earth’s average temperature continues to rise, it brings about far-reaching impacts that extend to various sectors, including food and agriculture. The intricate relationship between global warming and agriculture is of paramount importance, as agricultural systems are both vulnerable to climate change and significant contributors to greenhouse gas emissions. Understanding the impacts of global warming on

17.1. Introduction

Grounded Theory (GT) is a systematic methodology in the social sciences that involves the construction of theories through methodical gathering and analysis of data. Developed by Barney Glaser and Anselm Strauss in 1967, GT is widely used in qualitative research to generate theories that are grounded in empirical data. Unlike traditional research methods that test hypotheses, GT allows theories are to emerge from the data itself, making it a valuable tool for exploring complex social phenomena.

This chapter provides an in-depth exploration of Grounded Theory, including its origins, key principles, methodologies, applications, and criticisms. By the end of this article, readers will have a thorough understanding of GT and its significance in qualitative research.

The H.C.F and L.C.M are very important and many questions are asked in almost all competitive examinations to judge the students ability and also capability to solve the problem. This concept is useful in the chapters of time and distance, time and work, pipes and cisterns, etc. The tricks used to find L.C.M and H.C.F of two or more numbers help in finding out quick solutions and thus reduce time during exams. L.C.M concept is important to solve problems related to racetracks, traffic lights, etc. H.C.F. concept is useful in calculating the largest size of tile/room in particular area, largest tape to measure the land, etc.

Introduction Data analytics is a systematic analysis of data or statistics. It is also the process of collecting, cleaning, transforming, interpreting, modelling and visualization the data. Such transformed data has to be further investigated to retrieve helpful information that may help conclude and support decision making. This study presents revising selected two relative valuation models (PE Model, PB Model) by replacing the most non-significant variables used in the model with the critical variable that is the potential cause for improving the model’s performance. The objective of relative valuation is to value assets based on how similar assets are priced in the market. This paper measures the valuation accuracy using Root Mean Squared Errors and refine the relative valuation models using the machine learning algorithm for predicting the value of a stock. The following steps helped to revise and refine the PE Model, PB model (Relative valuation models) to predict the value of Sensex stocks.

1.1 Introduction
The concept of invention is the fundamental requirement for design thinking. Since the birth of mankind, people started inventing many concepts, which are mainly observations of facts and innovation of ideas for producing products that are useful to the society. The design thinking requires unconventional thinking to design processes for maximal operational efficiency of manufacturing the products, which give maximum satisfaction to customers with more value for money. Hence, the team that is involved in the design thinking projects may gain the required skills by knowing several inventions of the past. Hence, a review of inventions is presented in this chapter.
1.2 Tracing Inventions
The content at https://www.explainthatstuff.com/timeline.html gives a complete account of inventions starting from 4–5 billion years ago up to 2022. The article on solar cell energy gives an account of the sun started producing energy. About 1.2 million years ago, humans invented fire, which has been further innovated for biofuels, candles, IC engines, aviation engines, etc. In the 7th century B.C., ants were killed by burning in the heat produced by concentrated sun rays using magnifying lens. Greeks and Romans utilised burning mirrors to light candles during religious ceremonies in the third century B.C. About 3.5 million years ago, stone, wood, antlers, and bones were used by humans to make first kind of tools. These were further improved to make tools for machines. About 4000 BCE back, iron was used to make decorative ornaments. About 3500 BCE, people invented the concept of wheel, which brought quantum revolution in transportation.

Introduction
Horticulture, the science and art of cultivating fruits, vegetables, and ornamental plants, is a vital component of agriculture that contributes significantly to food security, economic growth, and environmental sustainability. This chapter provides a comprehensive overview of horticulture, exploring its various branches, including fruit and vegetable production, floriculture, landscape design, and greenhouse management. Horticulture is unique in its integration of scientific principles, artistic design, and business acumen, making it a dynamic and multifaceted field.
The chapter begins by introducing the fundamental concepts of horticulture and its importance in modern agriculture. It delves into the specifics of fruit and vegetable production, emphasizing the techniques and practices that enhance yield and quality. Floriculture and landscape design are also discussed, highlighting the aesthetic and environmental benefits of ornamental plants. The chapter further explores post-harvest handling and storage, crucial aspects that ensure the longevity and marketability of horticultural products.
In addition to traditional practices, the chapter addresses modern horticultural techniques, such as greenhouse management, which plays a crucial role in extending growing seasons and improving crop quality. Sustainable horticultural practices are also emphasized as key strategies for addressing global challenges like climate change and food security.
Horticulture is a branch of agriculture that focuses on the cultivation, production, and management of plants, particularly fruits, vegetables, ornamental plants, and flowers. This chapter explores various aspects of horticulture, including an introduction to horticulture, fruit and vegetable production, greenhouse management, floriculture and landscape design, and post-harvest handling and storage. Horticulture plays a vital role in agriculture, encompassing the science and art of growing plants for food, aesthetics, and environmental enhancement. This chapter delves into the exciting world of horticulture, exploring the cultivation and management of fruits, vegetables, ornamental plants, and flowers. Horticulture combines scientific knowledge, practical skills, and artistic creativity to maximize plant growth, yield, and aesthetic appeal. By studying horticulture, we can gain insights into the principles and practices that contribute to successful fruit and vegetable production, greenhouse management, floriculture and landscape

True/False

Q.1.    Qualitative or categorical data are frequently collected in medical investigation.
Q.2.    Qualitative variables may be, e.g. sex, blood type, treatment, groups, classification of exposure or disease and patients survived or not.
Q.3.    Qualitative variables include the grouped quantitative variables such as age groups of mothers, pre term babies, birth weight groups, 
hypertensive or normotensive etc.

We can categorize statistical theory into two general parts.

1. Firstly, there is descriptive statistics which uses the appropriate tools, typically tables, diagrams and/or numerical measures, to describe a data set and provide a summary of its distribution.

2. In addition, there is inferential statistics which is concerned with drawing conclusions about a population using information obtained from a representative sample selected from it.

(a) One aspect of statistical inference is the estimation of a population parameter by the appropriate sample statistic (e.g. the population mean by the sample mean).

(b) The second aspect of inferential statistics is hypothesis testing. In this case, we examine a hypothesis, framed in terms of the parameters in one or more populations.

Estimation is concerned with description whereas hypothesis testing is ultimately concerned with decision.

Basic concepts of hypothesis testing

Hypothesis testing is a process that is concerned with making conclusions about the population using the information obtained from its sample.

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.

The information of image is perfectly accepted by eyes when the image will be in convenient form. Always pictures accurately detail about information related to their position, sizes and inter-related with each other is objects. Actually images portray positional information which can be recognized as objects. Human eyes are exact example for differentiating that information from images to innate visual and mental ability.

The most important use of R programming lies in importing of data files which serves as input for the analysis work. After the analysis is over by using R codes, operators and functions, the output result must also be exported in form of files. The files are imported in form of csv files (comma separated variable), text files or excel files.

Before going to the actual content of the chapter we have to have an idea about csv files. CSV stands for Comma Separated value. It is used to store tabular data, such as a spreadsheet or database. CSV is delimited text file in which the values are separated by commas. CSV files occupy much less space than excel files and also gets imported in a program at a much faster rate than Excel files. CSV Files also does not manipulate data and stores as it is.

Let us consider that an excel file named as ‘plant’ which stores three variables, named as ph, pd and yield is created inside the folder named as ‘My Data’ which is placed in the desktop. Thus the path of the file is “C:\\Desktop\\My Data\\plant”

To import excel file the package will be readxl and the command will be library(readxl)

Complete block designs become less efficient as the number of treatments increases,primarily because block size increases proportionally with the number of treatments,and the homogeneity of experimental plots within a large block is difficult to maintain.That is , the experimental error of a complete block design is generally expected to increase with the number of treatments.

An alternative set of designs for single –factor experiments having a large number of treatments is the incomplete block design.As the name implies, each block in an incomplete block design does not contain all treatments and a reasonably small block size can be maintained even if the number of treatments is large.With smaller blocks,the homogeneity of experimental units in the same block is easier to maintain and a higher degree of precision can generally be expected.

An index number is a statistical measure designed to show changes in a variable or group of related variables with respect to time, geographical location or other characteristics like: income, profession, etc. A collection of index numbers for different years, months, locations, etc. is called an index series. Since index number are usually used to measure the level of business and economic activities over a period of time, so they are rightly called barometers of economic activities or simple called economic barometers. The various problems faced in the construction of an index number are to known the specific purpose of index number, selection of items, data for index numbers, choice of base periods, choice of an average, selection of weights and choice of a suitable formula.

Methods of Constructing Index Numbers

A number of formulae have been developed for constructing index numbers, which may be grouped into the following categories:

• Unweighted index numbers

• Weighted index numbers

• Unweighted index numbers: In this case, weights are not expressly assigned. 

Introduction

Digitalization is transforming the digital economy, offering opportunities for entrepreneurs and businesses, and transforming production and trade. The rapid advancement of technology and information systems has led to significant changes in both individuals and companies, particularly in the realm of digitalization in commerce (BarNir et al., 2003). Digitalization uses digital technologies to revolutionize business models, simplify transactions, and enhance value-producing opportunities in processes like customer service, shipping, buying, and selling (Zhang et al., 2013). All over the world, businesses are transitioning from manual to e-commerce for benefits such as efficiency, productivity, security, competitiveness, cost reduction, marketing, promotion, and information distribution (Daniel & Wilson, 2002; Gefen, 2000). E-commerce, a key feature, is reshaping global production and trade (Koh et al., 2017; Scalco, 2017). Moreover, the e-commerce industry has seen a substantial transition over the last few decades, due to rapid technical improvements and shifting customer behaviors.

6.1. INTRODUCTION

All ecologist and biology researchers have to find out: (i) how reliable are the observed results? and (ii) what is the probability of difference between observed and expected results? The question of reliability is solved by estimating the confidence interval and the second question is addressed by the application of statistical tests for significance. Both these methods help us to draw conclusions on our observations, hence, may be grouped together as ‘inferential statistics’.

Referring back to the chapter on sampling, we may recall that it is usually not possible to calculate the ‘population parameter’ directly. Instead, we have to rely on estimates. Two types of estimates can be used, a point estimate and an interval estimate. A point estimate is a single numerical value of a sample statistic that is used to estimate the corresponding population parameter. For example, the sample mean, x , is used as an estimate of the population mean, μ Point estimates are not used widely because the value of some statistic, say mean, varies from sample to sample and we can’ t conclude with any degree of certainty that a single point estimate equals the population parameter. Therefore, an interval estimate is typically favoured. An interval estimate is a range of values within which the population parameter is likely to lie.

8.1. INTRODUCTION

The word ‘non-parametric’ means “not for any specific parameter”. For example, in the student’s t test we use the parameter ‘mean’. In the non-.parametric tests we check the randomness and trend of the pattern of distribution of a variable directly, without using a population parameter for comparing.

In contrast to the parametric tests, where many assumptions are made regarding the distribution of the parameter in the population (i.e. Gaussian features), here we do not make stringent assumptions about the population from which samples are drawn. These techniques are, therefore, also known as ‘distribution free’ tests.

We shall be discussing about 10 such non-parametric tests.

7.1. INTRODUCTION

The word ‘significance’ in statistics does not describe the strength of association. Statistical significance is simply related to the probability of the result being commonly or rarely encountered (If the probability of chance occurrence is low the result is likely to be determined by the set of circumstances under study and not by chance). If conclusions are to be drawn that a difference exists between two groups of subjects, then we have to ensure that they are equal in all respects except the one under test.

‘P’ values are used to assess the degree of dissimilarity between two (or more) sets of measurements. The ‘P’ value is the probability of obtaining a result as extreme as or more extreme than the one observed. The ‘P’ value is actually a measure of surprise and the smaller the value, the more surprising is the result. When ‘P’ value is between 0.05 and 0.01, the result is usually called statistically significant; when it is less than 0.01, it is usually called highly significant and if, values are lower than 0.005, then it is called very highly significant. Traditionally, the approach had been to indicate the level of significance by rounded off ‘P’ values e.g., P < 0.01. Now it has become a fashion to calculate the exact probabilities and thus arrive at a particular decision.