eChapters

A sample is said to be small sample if the sample size(n) is less than 30. Various small sample tests are: (i) t-test (ii) F-test

t-test performs the following tests for small sample(s).

1. To test the equality between a single sample mean and the assumed population mean.
2. To test the equality of two independent sample means.
3. To test the equality of two paired sample means.
4. To test the equality between sample correlation coefficient and the assumed population correlation coefficient

Q.1.    What is an experiment ?

    Answer
    
    An experiment is a means of getting an answer to the question that an experimentor (researcher) has in mind. This may be to decide which of the several pain relieving drugs that are available in the market, is the most effective or whether they are equqlly effective.
    
    An experiment may be planned to compare the yield of different varieties of wheat. so that the varity giving the maximum yield may be recommended to the farmers to increase their production.

In this chapter, we will explore multiple comparison tests, with a particular emphasis on three significant tests: least significant difference (LSD), Bonferroni, and the techniques to visualize their outcomes. We aim to facilitate a comprehensive understanding of how to conduct and interpret the results of these comparison tests visualization using the R software.

Treatment Comparison

The “doebioresearch” package is commonly used for testing the significance of treatment groups in various experimental designs such as randomized block design (RBD), completely randomized design (CRD), and least significant difference (LSD). This package excels in conducting multiple comparison tests like LSD, Duncan’s multiple range test (DMRT), and Tukey’s honestly significant difference (HSD). It efficiently calculates all comparisons in the background, resulting in the generation of only group labels, as previously demonstrated in the output of the “doebioresearch” package. Additionally, the “agricolae” package is another useful tool that provides detailed values for multiple comparison tests. These three tests—LSD, DMRT, and HSD—are particularly popular in agricultural experiments and form the focus of our discussion.

I. Least significant difference (LSD) test

• To conduct the least significant difference test for comparing treatment means, the “agricolae” package is essential.

• To install the “agricolae” package, use the following code:

• install.packages (“agricolae”)

• After installation, activate the package with this command:

• library (“agricolae”)

• The data frame required for analysis in this context remains consistent with what is needed for randomized block design (RBD), completely randomized design (CRD), LSD, and variability analysis, among others. As an illustration, consider an example involving variability analysis.

Stratified Sampling (Meaning and Concept)

The word ‘stratify’ comes from the Latin word, meaning ‘to make layers’, thus in stratified sampling the whole population is considered to be classified into some mutually exclusive layers or subgroups or classes also called strata. The strata do not overlap and they constitute the whole population.

Why the population is considered to be divided into different strata (or layers or subgroups or classes)?

Matrix is a rectangular array of numbers arranged in rows and columns. The order of a matrix is written as r x c, where r is the no. of rows and c is the no. of columns. The numbers included in a matrix are called elements of the matrix. The name of the matrix is denoted by an uppercase English alphabet.

Statistics as a wide subject is used in almost all disciplines especially in research studies. Statistics provides scientific tools for representative data collection, appropriate data analysis and summarization of data and inferential procedure for drawing valid conclusion in the face of uncertainty. Each and every researcher should have some knowledge in Statistics and must use statistical tools in his or her research, one should know about the importance of statistical tools and how to use them in their research or survey. The quality assurance of the work must be dealt with: the statistical operations necessary to control and verify the analytical procedures as well as the resulting data making mistakes in analytical work is unavoidable. The emergence of statistical software in the twenty-first century has helped different researchers in the physical and social science to improve in the quality of research. Most renowned researchers in adopting this software in their data analysis have been able to identify the immense contribution to research findings (Adetola, 2013). Any quantitative research cannot be done effectively without Statistical Software. Moreover, it enables research data for easy presentation. It helps professionals to interact with data thereby paving way for creativity and innovation. Some are user friendly interface with drop-down tips for beginners (ATS, Ucla Edu, 2014). Advances in technology have improved all our lives (Akindutire, 2013) and has allowed experts greater freedom to come out with results within a twinkle of an eye than ever before where it takes time to finish analysis. This same technology has offer tremendous opportunity to research and keep research as a more interesting field of study. This study is significant, because it is an attempt to measure the direct impact of different statistical software on research analysis.

9.1. Introduction

The human population of the world will reach nearly 10 billion by 2050, boosting agricultural order-in a situation of humble financial development (FAO, 2017). At present, approximately 37.7% of total land surface is being utilized for crop production. From employment generation to contribution to National Income, agriculture sector is contributing a significant role in economic prosperity of the developed nations and is playing an active part in the economy of the developing countries as well. The growth of agriculture has resulted in a significant increase in the per-capita income of the rural community. Thus, a greater emphasis on agricultural sector is the need of the hour. For countries, like India, the agricultural sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. Development in the agricultural sector will enrich the rural development which will lead toward rural transformation and ultimately the desired structural transformation (Mogili and Deepak, 2018; Shah et al., 2019). The role of Artificial Intelligence (AI) has been obvious in the agricultural sector recently. The sector needs the impetus from AI based technology to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology.

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.

4.1 Introduction
Companies that produce tangible items or offer services must drastically increase their productivity in order to maintain their operations in the face of intensifying global competition. A single quantum jump productivity enhancement technique or multiple concurrent incremental improvement productivity strategies can both be used to achieve this goal. If a business chooses the second choice, integrating various tactics for a significant increase in the total productivity would be quite difficult. The first alternative, a single productivity improvement plan, will make it possible for the business to increase productivity with the same or greater success. The only difficulty with this strategy is locating the probable problem area and then using process improvement techniques to achieve the anticipated dramatic increase in productivity. Business process reengineering refers to any significant adjustment of business processes that results in a dramatic increase in an organization’s productivity. Price, quality, after-sales support, adherence to delivery schedules, technological breakthroughs, etc. are some elements that influence market competition. Therefore, the business should focus its efforts on each of these areas in order to achieve notable improvements in terms of decreased operating costs and higher sales revenue from the sale of its goods and services to clients. Improved pricing for a company’s goods or services can provide it an advantage over competitions. Applying various cost-cutting strategies to lower variable costs and overhead costs will lower the price of the good or service. The other elements, viz. quality, after-sales support, delivery schedule adherence, and technological advancements are all directed in this direction

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

In the annals of history, the term “Renaissance” evokes images of a time marked by profound cultural, artistic, and scientific rebirth. The Renaissance of the 14th to the 17th century was characterized by an extraordinary surge in creativity and intellectual achievement, which laid the groundwork for the modern world. Today, we find ourselves at the cusp of a new Renaissance—a Digital Renaissance—propelled by the rapid and relentless advancements in technology. This new era, like its historical counterpart, is marked by a transformative shift that is redefining every aspect of human life, from how we work and communicate to how we learn and govern.

This chapter delves into the essence of this transformative era, exploring the fundamental technological innovations reshaping our world and their far-reaching impacts on society. We will examine the pivotal technologies driving the Digital Renaissance, including artificial intelligence, blockchain, the Internet of Things (IoT), and other emerging technologies such as virtual and augmented reality and 5G networks. By understanding these technologies and their applications, we can appreciate how they collectively contribute to a digital transformation that is as revolutionary as the artistic and scientific achievements of the past.

The digital age has revolutionized the way we live, work, and interact with the world around us. Digital transformation has become a crucial imperative for businesses of all sizes and industries, enabling them to not only adapt to the changing landscape but also thrive in it. (De Lucas,2021). The rapid advancement of technologies such as 5G, cloud computing, big data, artificial intelligence, IoT, and blockchain has opened up a plethora of opportunities for enterprises to enhance their operations, improve customer experiences, and gain a competitive edge. (Anjali et al, 2023). Businesses can increase their reach, automate jobs, obtain insights from data, personalize services, and streamline procedures by utilizing these technologies. They must reconsider their business models and implement more flexible and agile strategies in response to these exponential changes. (Bansal et al, 2020, Fenech et al, 2019).

Introduction

It refers to action which are taken with an aim to provide value to customers developing a competitive advantage by encashing upon the strength areas. In case of business Organisation with multiple business, where each business is considered as Strategic Business Unit (SBU).

• It address the following issues

 • Satisfying the Customer needs

 • Achieving an edge over its competitors

 • Avoidance of competitive disadvantage

Michal Porter Generic Competitive Strategy aim to develop position in long run to outperform the competitors. The three strategies includes:

• Cost Leadership: Providing quality product at low cost

• Differentiation: Providing unique goods and services

• Focus: Cost and differentiation focus

Introduction

It includes different levels of competitive strategy and how generic competitive strategies can be formulated. The organisation need to understand how to tackle the competitors and what specific decisions to be taken while formulating a competitive strategy. Each type of industry has to plan its own competitive strategy either to grow or to come out in bad situation. Michal Porter described generic competitive strategies to cope with five competitive forces. These strategies help organisation in different situation to develop appropriate competitive strategy that can be implemented effectively.

Competitive strategies are the methods and approaches used by companies to gain a competitive edge in the market and surpass their rivals. These strategies involve making deliberate choices about how to position the company, allocate resources, and respond to market conditions. Here are some key points to consider when introducing competitive strategies.

Introduction to Corporate Level Strategy

Corporate level strategy pertains to the choices and initiatives undertaken by a company's senior management to define the overarching direction and breadth of the organization. It entails making decisions regarding which industries or markets to engage in and how to distribute resources among various business units.

Scope of Decision-Making: Corporate level strategy concentrates on the entirety of the organization rather than individual business units or functional areas. It involves strategic decisions related to diversification, vertical integration, mergers and acquisitions, strategic alliances & resource allocation.

Purpose: The primary goal of corporate level strategy is to create value for shareholders by achieving a sustainable competitive advantage and maximizing the overall performance and profitability of the organization. It involves assessing the external business environment, identifying opportunities and threats, and leveraging internal capabilities to gain a competitive edge.

Diversification: One key aspect of corporate level strategy is deciding whether to pursue a strategy of diversification. Diversification can be either related (expanding into industries or markets related to the company's current business) or unrelated (entering completely new industries). The decision to diversify involves assessing the potential benefits, risks, and synergies associated with entering new markets.

Introduction

The strategic control process is an essential component of strategic management that helps organizations monitor and evaluate their strategic plans and initiatives. It involves a systematic approach to assess the progress performance, and effectiveness of strategic objectives and make necessary adjustments to ensure alignment with organizational goals. Here are some key points on the introduction to the strategic control process. Strategic control refers to the ongoing monitoring, evaluation, and adjustment of an organization's strategic activities to ensure they are in line with the desired outcomes and objectives. It The significance of strategic control lies in several key reasons. helps organizations track their progress, identify deviations from the intended course, and take corrective actions. It also enables managers to assess the effectiveness of strategies, make informed decisions, and enhance organizational performance.

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.

Normal distribution :- A random variable x is said to be normally distributed with population mean u and standard deviation σ if it assumes the following distribution

Introduction

In any experiment, for instance, a varietal trial or a treatment trial, the main purpose of the experimental design would be to detect the differences among the varieties or treatments. This assumes, as an obvious prerequisite that the experimental units, such as plots or animals, be as alike as possible so that differences observed in responses, such as yield in varietal trial, could be attributable to the varietal or treatment differences. But more often than not, this condition is not realized to a satisfactory degree in practice due to heterogeneity present in experimental units. In a field experiment, for instance, a big piece of land might be quite heterogeneous regarding soil fertility, moisture content, salinity, etc; so that experimental plots marked on this land would differ widely among themselves, though plots nearer to each other would be more alike or homogeneous than the plots scattered distant apart; also in the case of animal experiments, animals forming the experimental units might be heterogeneous regarding their ages , weights, genetic composition, etc. With such experimental units in the experiment, the variability produced in the observations will be comparatively high so that only large treatment differences are detectable, since small differences are obsecured by large error variability.

The randomized complete block design (RCBD), is perhaps the most widely used design of all experimental designs, mainly for its simplicity, flexibility and validity. The main objective of the design is either to control, remove or reduce the variability due to heterogeneity in experimental material, which is quite irrelevant for treatment comparisons. In CRD, the error variability as estimated from the residual sum of squares will be considerably high. The RCBD is a device introduced in this design for separating out the variability due to heterogeneity among experimental units from the variability due to all extraneous sources, thus purifying in one sense. The design is especially suitable for field experiments where the number of treatments is not large and the experimental area has a predictable productivity gradient.The primary distinguishing feature of the RCBD is the presence of blocks of equal size, each of which contains all the treatments.