eChapters

Introduction

The split-plot design is specifically suited for a two-factor experiment that has more treatments than can be accommodated by a RCBD. In a split-plot design,one of the factors is assigned to the main plot. The assigned factor is called the main-plot factor. The main plot is divied into subplots to which the second factor ,called the subplot factor, is assigned. Thus, each mainplot becomes a block for the subplot treatments (i.e the levels of the subplot factor). The split-plot design is profitably employed under the following situations.

1. An experimenter may have already possessed some knowledge about the main effect of a certain factor so that his interest lies in estimating the interaction effect of this factor with some other more important factor in whose main effect also he is equally interested. In either case he wants to estimate the latter two effects with greater precision for which he is prepared to sacrifice the precision in estimating the first mainfactor.
2. The second common type of situation when the split-plot design is automatically suggestive is the difficulties in the execution of other design. For instance, if ploughing is one of the factors of interest,then one cannot have different depths of ploughing in different plots scattered randomly apart. Similarly, if different levels of irrigation are chosen,then random distribution of these levels in an ordinary experiment may involve difficulties in the practical execution of such plans. This difficulty may,however, be offset partly by having strips of plots (in one unit of block,say), all treated alike ;receiving the same level of irrigation with different depths of ploughing; or different levels of irrigation with same depth of ploughing. Different levels of irrigation or different depths of ploughing can,however,be randomly distributed over such strips.
3. The third situation in which the experimenter may adopt a split-plot design is this. A particular factor (factors) under investigation exhibits large treatment differences which can be easily detected even in bigger plots,while a second factor (factors) requires comparatively smaller plots to detect smaller treatment differences with higher precision. Then the first factor will be tested in whole plots and the second factor whose treatment differences are small will be tested in small plots or subplots.

Q.1 If the sampling units of the population are of heterogeneous nature then stratified random sampling scheme is suitable for sample survey to be conducted.

Q.2 In stratatified random sampling scheme, the strata are homogeneous within themselves but heterogeneous between themselves.

Q.1 According to prof. R. A. Fisher “ANOVA” is not the separation of variance assignable to one group of causes from the variance assignable to another group of causes.

Q.2 The basic purpose of ANOVA technique is to test the homogeneity (equality) of several means.

Completely Randomized Design (CRD)

The simplest design using the two essential principles of replication and randomization is the CRD. Suppose that we have t treatments (or t levels of a factor) under comparison and ith treatment is to be replicated r times, for i = 1,2, … t . A particular case of this is equal replication for different treatments, where r1=r2= =r6=r, so that n=rt.

Vector data consists of points, lines and polygons with x, y coordinates known as vertices representing the geographic location i.e. latitude and longitude. Vector data depicts the shape of the spatial feature. Points consist of single x, y locations. Examples of points are tower location, sampling location, etc. Lines represent 1 – dimensional features like roads, streams, etc., and are composed of many or at least two connected points or vertices. Polygons denote 2 – dimensional features like boundaries with three or more vertices that are connected and closed. The vector data are represented in the following figure (fig. 5.1).

A treatment is the combination of N & P

If levels of N & P are 5 & 10 respectively, so number of treatments are 5x10=50.

Introduction

The phrase “sustainable” has been used so much in recent years-and frequently so imprecisely—that for many people, it has become meaningless or confused, more of a rhetorical chant than a source of useful advice. Lester Brown, the founder of the World Watch Institute, defined a sustainable society as one that can meet its requirements without compromising the opportunities for future generations. This definition is the one that we come across most frequently. Brown wrote this description in the early 1980s. The Brundtland Report, a 1987 United Nations report, addressed this issue and defined sustainable development as development that satisfies present demands without jeopardising the ability of future generations to satiate their own. Despite their obvious limitations speaking simply of human wants, for example, without reference to the natural world-these are significant moral exhortations. They do serve as a helpful reminder that it is our duty to provide our kids and grandkids with an equally abundant planet as we did, but they offer no guidance on how to truly create a sustainable society. When air, water, and minerals interact with living things to fill a specific region, it is referred to as an ecosystem (Tansley, 1935). This word was first used in 1935. Before then, the main goals of land management had been to provide a single item, like food or lumber, or to conserve a single, iconic species.

Changing Dynamics in HM

HM has transformed significantly; a lot because of changes in consumer behaviour, technological advancement, economic developments, regulatory changes, and growing consciousness of sustainability (Samaddar & Mondal, 2024). These changing dynamics have pushed customer’s expectations toward better and more personalized service (Law et al., 2024). Initially, the focus was on basic services and manual processes, but since then, the HM industry has embraced smart technologies like mobile apps, AI, IoT, and big data analytics. These developments have revolutionized the way customers interact; from merely checking in with their mobile phones, and controlling their room settings, to house-keeping and service delivery, thanks to AI-powered customization and automation. Using cloud services for managing properties and improving cyber security helps keep operations smooth and keeps data safe. Efforts to use energy and water more wisely show the industry's move towards being more sustainable. As the HM continues to evolve, industry stakeholders eye towards more personalized, efficient, and sustainable guest experiences. This would reflect the ongoing commitment to excellence and adaptation efforts to strive and evolve in the dynamic global landscape.

Accurate crop-yield forecasting supports food-security planning, resource allocation and farm decision-making. Yield is influenced by interacting factors such as weather, soil, irrigation, fertilizer and pesticide use, making reliable prediction challenging. This chapter presents an algorithmic framework using historical agricultural and environmental data. It covers cleaning, missing-value treatment, categorical encoding, normalization and feature extraction. Decision Tree, Gradient Boosting, AdaBoost and XGBoost regression models are evaluated individually and combined through soft voting. Performance is measured using standard prediction-error metrics. The case study demonstrates that ensemble modelling can improve accuracy and robustness, helping farmers and planners optimize inputs, anticipate production and strengthen agricultural productivity.

Analysis of Variance
    
Completely Randomised Design (C.R.D.)
    
Randomised Block Design (R. B. D.)
    
Latin Square Design

Q.1.    Under what situation, the exact sample tests are required to be used and who discovered it first time ?

Basic thought of Sampling

The data is very important in every field for decision making whether it is medical sciences, engineering sciences, agricultural sciences and management sciences etc. for this we need to plan the correct data. If we don’t have correct data then based on that we cannot plan good decisions and it will provide misleading results. So, to achieve or plan the right data it is necessary to have the right technique which is to be used while collecting data and an appropriate method of data collection is preferred. From this, the two important questions arise (i) what are those methods required to obtain paramount data or sample and (ii) how best to use the data or sample to estimate the characteristic of the whole population i.e., what are those methods by which we can study our population parameters to draw a proper statistical inference.

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.

In statistics, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. Each observation measures one or more properties (such as weight, location, colour) of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications.

The sampling process comprises several stages

• Defining the population of concern

• Specifying a sampling frame, a set of items or events possible to measure

• Specifying a sampling method for selecting items or events from the frame

• Determining the sample size

• Implementing the sampling plan

• Sampling and data collecting

11.1. Introduction

Fuzzy Cognitive Mapping (FCM) is a powerful soft computing technique that integrates elements of fuzzy logic and cognitive mapping to model complex systems. It allows for the representation of causal relationships between variables in an interpretable and flexible manner, making it particularly valuable for analyzing and simulating real-world dynamic systems. FCM has its roots in cognitive science, systems thinking, and artificial intelligence, enabling researchers and practitioners to model uncertainties and interdependencies effectively (Tyrovolas et al., 2023). The approach is particularly advantageous when dealing with complex, ill-structured problems where traditional mathematical modeling techniques fall short. By capturing expert knowledge and combining it with computational techniques, FCM helps in making informed predictions and policy decisions.

It is widely applied across multiple domains, including environmental management for sustainability assessments (Özesmi and Özesmi, 2004), decision support systems in business and governance, healthcare for medical diagnostics and treatment planning (Apostolopoulos et al., 2024), and socio economic planning for policy analysis and forecasting (Gray et al., 2015). Furthermore, its adaptability has led to its increasing integration with artificial intelligence, machine learning, and big data analytics, making it a promising approach for tackling contemporary challenges.

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.

True/False

Q.1.    The most fundamental assumption for “Parametric inference” is that the random sample drawn is from “Normal” population.
Q.2.    The most fundamental assumption of “Non-parametric” test is that the random sample drawn is from “Continuous distribution”
Q.3.    The standard parametric methods are applicable for “Estimating the parameter” as well as for “Testing the hypothesis” about the parameters.
Q.4.    Non-parametric methods, are applicable only for testing the hypothesis about  distribution but not applicable for estimation purpose of parameter.

True/False

Q.1.    Two independent random variables (X,Y) are always uncorrelated but reverse may not be true

Introduction to strategy implementation involves translating strategic plans into actions and achieving desired outcomes. While many organizations focus on the technical aspects of implementation, it is crucial to consider the behavioral dimensions as well. Behavioral dimensions encompass the attitudes, behaviors, and actions of individuals within an organization that impact the success of strategy implementation. Here are some key points to consider regarding the behavioral dimensions of strategy implementation:

Organizational culture: The culture of an organization plays a significant role in strategy implementation. It comprises shared values, norms, beliefs, and practices that influence employee behavior. An accommodating and flexible culture fosters employee engagement, commitment, and alignment with strategic goals.

Leadership and Communication: Effective leadership is essential for successful strategy implementation. Leaders must articulate the strategy clearly, provide guidance, and motivate employees towards the desired outcomes. Communication channels ought to be open, transparent, and promote a culture of collaboration and feedback.

Employee Involvement and Empowerment: Involving employees in the strategy implementation process increases their commitment and ownership. Empowering employees through delegation of authority and decision-making cultivates a sense of responsibility and accountability, leading to enhanced performance and innovation.

Raster sub-setting is done to select the desired area or bands of the raster image on which the user wants to perform analysis. Raster sub-setting is done in two ways:

Spectral subset: Spectral sub-setting is done to extract few desired bands from the multiband raster image which are required for analysis.

Spatial subset: Spatial sub-setting is done to minimize the area or extent of raster image to the desired area or extent for further analysis.

Raster sub-setting in R is done by functions available in the ‘raster’ package. The following packages are required in this chapter: