eChapters

3.1 Introduction

One of the most important objectives of statistical analysis is to summaries the information contained in the data which may be considered either as a population or as a sample (or sub-population). If the set of data consists of all conceivably possible (or hypothetically possible) observations of a certain phenomenon or a variable, we call it as population. If a set of data contains only a part of these observations, that is selected values of a variable, we call it as sample. Frequency distribution and graphical representation summarize to some extent.  They reduce a mass of data to a more understandable form but not to a single value. The aim of this lesson is to determine descriptive measures or statistical constants which will describe the nature of the data.

Method of Maximum Likelihood Estimation (M.L.E.)
    
Method of Moments
    
Method of Minimum X²
    
Interval Estimation

In day-to-day life, one comes across many statements which create doubt about certain happenings and one may want to determine the chance of occurrence of a particular happening, e.g., if one desires to know the chance of rain based on some metrological observations.  In the case of coin tossing, one wants to know what will be the chance of winning if a person always stakes for head in a fixed number of tossing. There can be innumerable such queries which one would like to make and meanwhile evaluate mathematically the chance of the occurrence of such possibilities.  This invoked the need of probability theory. The theory of probability came in existence when problems of games were referred to mathematicians like Karl Pearson, Laplace and others. Complicated applications involving advanced theory of probability and axionmatic approach of probability are not dealt within this book.

Simple Random Sampling (SRS)

Simple random sampling is probability sampling and refers to a technique of sampling where each and every unit of the population have an equal and independent opportunity of being selected and included in the sample during the sampling. In the SRS the selection of the sample depends on the chance factor and is also known as the method of probability sampling.

There are two ways in which SRS can be performed:

• SRSWR: Simple Random Sampling With Replacement
• SRSWOR: Simple Random Sampling Without Replacement

3.1. INTRODUCTION

In this chapter, we will study the measures of central tendency which is a numerical method to explain the data in brief. We can see examples of summarizing a large set of data in day to day life like average marks obtained by students of a class in a test, average rainfall in an area, average production in a factory, average income of persons living in a locality or working in a firm etc.

One of the most important objectives of statistical analysis is to get one single value that describes the characteristic of the entire mass of data. Such a value is called the central value or an ‘average’ or the expected value of the variable. This single value is the point of location around which individual values cluster and, therefore, called the measure of location. Since this single value has a tendency to be somewhere at the center and within the range of all values it is also known as the measure of central tendency. The purpose of computing an average value for a set of observations is to obtain a single value which is representative of all the items.

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.

7.1. People’s Participation in Observation, Analysis and Planning for Rural Development

Why should people participate?

There is no controversy as to whether there should be people’s participation in the development programmes in the development programmes designed to meet the basic needs of the people. The post-mortem of any unsuccessful programmes has shown that they have invariably failed when they failed to provide an adequate place and importance to people’s participation in the planning and analysis.

There is a body of philosophy and a set of assumptions behind people’s participation in rural development. These are;

i) It should be people’s programme with government’s participation;

ii) Community needs to build up their own capacity, agents have got a catalytic role;

iii) Self- imposed changes have got permanence as compared to those imposed from outsides

iv) Holistic approaches are far better than the fragmented approaches;

v) People need certain help to solve unique problems;

1.1. INTRODUCTION

In this chapter, we will know how to classify raw data. The purpose of classifying raw data is to bring order in them so that they can be statistically analysed easily. Classification of objects or things saves our valuable time and effort. It is done in a logical manner and similar things are arranged into a group or class.

11.1 Section-A : Write True / False

Q.1 Some assumptions or guesses made about the unknown population parameters, is know as "Statistical hypothesis".

Q.2 For the normal distribution N(μ, σ²), the statement (1) μ = 25.0 and (ii) σ² = 37.0, are known as statistical hypotheses.

Q.3 Let p = 0.5 is the probability of getting a head in single throw of a coin. Ten, this statement is not a statistical hypothesis.

Q.4 The statistical hypothesis under consideration (under test), is known as "Null hypothesis".

Q.5 The null hypothesis is denoted by "Ho".

12.1 Section-A : Write True / False

Q.1 Almost all biological data e.g. crop yield, plant height, seed weight, seed size, protein content in pulse, oil content in oil seed etc. are assumed to be normaly distributed.

Q.2 Let X be a normal variable with N(μ, σ²). The random variable Z def ned as Z = (x - μ)/σ, is known as standard normal variable.

Q.3 The standard normal variable Z is denoted as Z ~ N (0, 1).

Q.4 The p.d.f. of random variable Z is given as

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

True/False

Q.1.    A hypothesis that sets the fundamental objective of scientific study is called the “Research hypothesis”.
Q.2.     A statistical assumption or statement about the unknown parameters of a population is known as Statistical hypothesis.

True/False

Q.1.    According to Prof. RA Fisher “ANOVA” is the separation of variance assignable to one group of causes from the variance assignable to other group of causes.
Q.2.    The basic purpose of ANOVA technique is to test the equality (homogeneity) of several population means from the same 
experiment.

Introduction

Financial inclusion has become a global agenda because it is now well recoganised that a nation can prosper only if it ensures inclusive growth. Financial access and use of formal financial system, the means of financial inclusion, have positive association with inclusive growth (Levine and Demirguc-Kunt, 2008). The financial systems which are more inclusive have provided broader access to appropriate financial services to ensure that the benefit can be availed by poor and disadvantaged sections of the society (Akter, 2016). Since, financial inclusion is the process of ensuring availability of f inancial services and well-timed and sufficient credit as required by vulnerable groups such as low income segment and weaker segment of the society at an affordable cost (Rangarajan Committee on financial inclusion, 2005). Thus, the study of financial inclusion on the basis of socio economic factors is the key to investigate individual characteristics associated with financial inclusion (Allen, Kunt, Klapper, Peria, 2012). Understanding financial usage on the basis of socio economic factors enables framing appropriate financial inclusion strategies to achieve financial inclusion in real sense. Many international agencies like International Monetary Fund, Asian Development Bank, World Bank release data on financial inclusion around the world. Global findex report is published by World Bank after the interval of every three years to present the picture of financial inclusion on various socio economic parameters. Around the world, 74% adults, 72% female, 70% income poorest population, 63% out of labour force population and 66% rural population report to have account at formal financial system (World Bank Global findex report, 2021).

Unsupervised classification is the technique of classification of raster data in which no prior information is provided by the user. Raster data is classified based on their spectral characteristics such that the pixels having similar spectral properties in the multispectral feature space are grouped together in one class. Unsupervised classification is mainly performed by clustering algorithms. The purpose of these algorithms is to minimize within cluster variability and maximize between clusters variability. The user must interpret and label the classes after performing unsupervised classification. This method of classification requires minimum data which makes it easy and quick, but user has toput efforts in identification of the classes. This method could be useful when user doesn’t have any prior knowledge about the study area. This method removes subjectivity and enables us to get the unique classes which might be unrecognized by the user. K-means is the most commonly used method for performing unsupervised classification. In this chapter, we have discussed K- means, CLARA and unsupervised random forest method for classification of raster data in R.

Artificial intelligence and machine learning provide the technological foundation for intelligent agricultural applications. This chapter introduces supervised, unsupervised and reinforcement learning, model-development pipelines, feature engineering and evaluation metrics. It explains how algorithms can be selected and implemented for agricultural datasets that may be noisy, incomplete, variable or weakly labelled. Advanced topics include deep-learning architectures, transfer learning and model optimization. Practical considerations involving preprocessing, interpretability and deployment constraints are also discussed. Agricultural case studies illustrate AI applications in crop-yield prediction, plant-disease detection and environmental modelling. The chapter provides the technical foundation needed to design, evaluate and responsibly implement AI-driven agricultural solutions.

Design of Experiment was first elaborated by R. A. Fisher (1935).

• Design of Experiment: Proper allotment of treatments to the available experimental units.

• Treatment: It is the factor whose effect is to be studied. Example: Application of drugs, feeds, conditions, methods, etc.

• Experimental units: The subjects or individuals upon which an experiment is conducted.

Example: Laboratory animals like mice, rat, monkeys, primates; livestock like cattle, buffalo, sheep, goat, poultry; plants, a plot of land, etc.

Necessity of designing an experiment

1. To get maximum information from the available resources.

2. To know the effect of treatment on experimental units and to make comparison between treatments.

3. To know whether the difference between the effect of two treatments is significant or not.

4. A well designed experiment has a well defined method of statistical analysis of data.

5. To minimize error in the whole experimental procedure.

6. An ill designed experiment is not likely to give proper estimates of parameters and comparisons between treatments with desired precision even if the method of data analysis is very good.

7. The design of experiment is essential to get all the important treatment effects to be compared independently.

The Internet of Things supports continuous monitoring of soil, crops, weather and other field conditions through distributed sensors and connected devices. This chapter proposes an edge-cloud architecture designed to address scalability, heterogeneous data, communication latency and energy requirements. Edge nodes conduct local processing and anomaly detection, while cloud platforms provide storage and run advanced machine-learning models. Applications include crop-status evaluation, irrigation scheduling, pest and disease detection and yield forecasting. Explainable artificial intelligence improves transparency by helping users understand model recommendations. The integrated architecture can reduce response time compared with conventional centralized systems and provides a foundation for scalable, efficient and responsive next-generation smart-farming operations.

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.

Introduction

Chemical exposure in the workplace is a prevalent concern across various industries, including manufacturing, agriculture, healthcare, and construction. These exposures can range from acute to chronic and involve many substances, from heavy metals and solvents to pesticides and industrial chemicals. While the physical health effects of chemical exposure are well-documented, the psychological impacts are less understood but equally significant. Psychological stress is a response to perceived threats or challenges characterized by physical, emotional, and cognitive symptoms. Theories such as the Transactional Model of Stress and Coping by Lazarus and Folkman emphasize the role of individual perception and coping mechanisms in stress experiences.

The modern industrial landscape is characterized by ubiquitous chemicals, ranging from benign substances to potentially hazardous compounds. As industries strive for higher productivity and efficiency, the workforce is frequently exposed to various chemicals. This exposure is not without consequences; while the physical health effects of chemical exposure have been extensively studied, the psychological impacts are less understood. Emerging research indicates that chemical exposure can significantly contribute to psychological stress, affecting worker health, productivity, and overall well being.