eChapters

Abstract
Combined advancement in computer sciences and hardware with uniformly significant developments in molecular biology and chemistry are providing toxicology with a powerful new tool box. This tool box of computational models promises to increase the efficiency and the effectiveness by which the hazards and risks of environmental chemicals are determined. Computational toxicology focuses on applying these tools across many scales, including vastly increasing the numbers of chemicals and the types of biological interactions that can be evaluated. Apart from that, knowledge of toxicity pathways gathered within the tool box will be directly applicable to the study of the biological responses across a range of dose levels, including those more likely to be representative of exposures to the human population.  Although inappropriate pharmacokinetic properties are a major cause of abrasion in the safety issues are recognized as today’s single largest cause of drug candidate failure. It is expected that the right balance of in vivo, in vitro and computational toxicology predictions applied as early as possible in the discovery process will help to reduce the number of safety issues.

Abstract
This chapter describes how modern quantum mechanical methods can be applied in a systematic manner to understand pharmacophore and mechanistic aspects of drugs. This chapter also explains why accurate computation of drug-receptor interactions is important for in silico drug designing and testing.

WHY in silico DESIGNING?
Drug designing is the inventive art of finding a new lead compound based on knowledge of its biological target and then its slow conversion to a compound of therapeutic benefit to patient termed as “drug”. This whole process comprises of long sequels of synthesizing compounds followed by their testing and activity evaluation procedures; often a seemingly endless task until desired potency level is achieved.

Introduction and Importance of active site

Proteins are polymers of amino acids coupled by peptide bonds. The constituency of a protein that interacts with a ligand is habitually referred to as the “active site.” Ligands can be proteins, DNA or smaller molecules, such as pharmaceutical compounds [1]. The active site generally lies on the surface of the protein. In some cases, the active site is buried within the protein. Residues with reactive groups (ASP, GLU, SER, CYS, HIS, LYS, ARG) tend to be profuse in protein active sites [2]. The SER-HIS-ASP (sometimes SER-HIS-GLU) named as “catalytic triad” which is a motif, generally found in enzyme active site. Enzymes chip in a significant role in scheming the flow of metabolites within a cell, they catalyze virtually all of the reactions that make and modify the molecules mandatory for biological pathways [3]. The identification of catalytic residues is a key step in recognizing the function of enzymes. The site on the surface of an enzyme molecule that binds and acts on the substrate molecule are called as active site.

Since the dawn of the Industrial Revolution, mankind has been introducing numerous hazardous compounds into the environment at an exponential rate.  Environmental pollutants have become a major global concern. The modern growth of industrialization, urbanization, modern agricultural development, energy generation, have resulted in indiscriminate exploitation of natural resources for fulfilling the human desires and need, which have contributed in disturbing the ecological balance on which the quality of our environment depends.
 
Technological revolution has brought new changes in products and processes in industry. The waste generated from the development of products and processes are of concern to the environmental list. A variety of pollutants such as heavy metals, xenobiotics, polycyclic aromatic hydrocarbons (PAHs), chlorinated and nitro-aromatic compounds are found to be highly toxic, mutagenic and carcinogenic for living organisms (Zhang and Bennett. 2005; Samanta et al., 2002).

Introduction
Machine learning is a branch of artificial intelligence which is concerned with the development of algorithms and techniques that allow computers to learn. These techniques are tolerant of imprecision, uncertainty, partial truth, and approximation. There have emerged many methods based on machine learning techniques, such as Artificial Neural Networks (NNs), Support Vector Machines (SVMs), Decision Tree (DT), Bayesian Networks (BNs), Hidden Markov Models (HMMs), etc. These techniques attempt to study, model, and analyze very complex phenomena  for which more conventional methods have not yielded low cost, analytic, and complete solutions.

Bioinformatics is the application of information technology to the area of molecular biology. The increase in the number and complexity of biological databases has raised the need for powerful data analysis tools and techniques. To fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories (Inza et al., 2010). Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in approximation.

A gene microarray is a (mxn) matrix, containing n-experimental conditions, also called attributes for m-genes. Gene clustering refers to partitioning a microarray of genes based on the commonality of attributes. Genes belonging to a cluster often do not coexpress or coregulate. This calls for biclustering that segregates the (mxn) matrix into small windows. In other words biclustering refers to simultaneous clustering of rows and columns of the microarray. This chapter reviews some of the well known biclustering algorithms, and proposes an alternative solution to the problem using evolutionary multi-objective optimization technique.

Introduction

Clustering is process to automatically partition a set of p-dimensional data points (p³1) into classes, where the data in each class have similarity with respect to a given distance metric. Each class usually has an ideal member that possesses the necessary characteristics of the class.

Abstract
Proteins are comprised of domains, folds and motifs, which form its basic building blocks. Most efforts to understand protein evolution and its function have focused on domains as these can fold into a stable three-dimensional structure independent of the rest of the protein chain and generally perform a unique function conserved over the evolution. Genetic recombinant techniques allow reorganization of domains, called domain shuffling, resulting in different combinations of domains in different proteins. This along with swapping and insertion of domains results in complex architectures and are responsible for new protein functions in evolution. Protein domains are very useful in analyzing the mechanisms of protein folding and their stability and structural transformations in various conditions. This can be done reliably if a multi-domain target is considered only in terms of its constituent domains. The underlying goal is to reduce a complex protein structure to a set of simpler yet structurally meaningful units, each of which can be analyzed independently.

Introduction

Natural evolution gifted the biological world a treasure of diverse bio-molecules that take part in various cellular activities. Proteins, one of the vital molecules of biological function are wisely folded polymers comprising 20 different aminoacids. Function of each protein is encoded in its aminoacid sequence. Aminoacid residues can indulge in bonded or non-bonded interactions depending on the environment. These inter residue interactions with in a polypeptide drive its folding in to a compact conformation from a linear molecule. Biochemical properties as well as the linear arrangement of the aminoacids determines the three dimensional structure of a protein. These determinants of defined structure directly or indirectly dictates functional fate of the folded protein also.

Modern research on life science is progressing at the nanoscale not only with the objective to reduce time and cost but also to ease the handling and working conditions. Application of nanotechnology in various domains of science has led to the enormous innovations. This field facilitates better understanding as well as treatment of living system, activate biotechnological processes, synthesizing new drugs for targeted delivery and biocompatible materials for sustainable environment. Throughout the world nano science is getting wide propaganda. Its application to medicine is increasing rapidly in order to develop nano-drugs with prolonged availability and efficient delivery through rearrangement of molecules at the nanoscale using molecular interactions for example van der Waals forces, H bonds, electrostatic dipoles and various surface forces.

Introduction

India is an agriculture based country with 60% of the manpower employed in agriculture (World Bank, 2010). Few of the main environmental issues are soil erosion, water pollution from raw sewage and runoff of agricultural pesticides. There is a need to maintain a sustainable environment and a sustainable agricultural system.  Sustainable Agricultural system is a self sustaining system, is dependent up the ability to bring together and manage simultaneously the soil, water, plant and animal resources with the limitation of climatic and environment in such a way that the agricultural production/profitability can be maintained in the future too. So initiatives need to be taken to achieve a sustainable agricultural system. Tools have been developed to aid agriculturist, extension personnel and policy makers. The World Wide Web has made the tools easy accessible.

Introduction
DNA barcoding is a molecular technique that uses a short section of DNA (genetic marker) extracted from a standardized region of tissue to identify it as belonging to a particular species. A desirable locus for DNA barcoding should be standardized and that should present in most of the taxa of interest and sequencable without species-specific PCR primers. A large database of sequences for the desirable locus needs to be developed. The desirable locus should be short enough to be easily sequenced with current technology, and provide a large variation between species yet a relatively small amount of variation within a species. Although several loci have been suggested, but the mitochondrial Cox1 became a gene of choice for animals and many other eukaryotes. However, the DNA regions that are being used are chosen to optimize the resolution for their specific experimental purposes.

Introduction

Spatial planning tools brings together multiple users of the earth to make informed and coordinated decisions about how to use of resources  and helps to organize, analyze and present information from a number of different sources.  Thus, viewing from single interest or multiple use viewpoints is enhanced and can make a very important contribution to environment. Use of Remote Sensing (RS) and Geographical Information Systems (GIS) tools has the capacity to bring together experts from a variety of disciplines to address complex spatial problems.

Introduction 
Plant disease has been highly impacted by the development of new technology. The degree of the impact depends on how much the technology affects the component steps of the system. Many of the steps in plant disease diagnosis and management depend heavily on accurate and timely information and so can greatly benefit from the development of improved methods of accessing and disseminating information. Computers provide a wide range of tools which are extensively used in plant disease diagnosis and management. In fact, they will be a key to improve farm practices in the coming decades as it is scientifically pertinent to organize farm information in spatial databases because agricultural systems are inherently spatial. Biological and physical aspects of agricultural system create spatial heterogeneity, and as a result, patchiness is the rule in the occurrence and distribution of plant pathogens and disease (Campbell and Madden, 1990). In the area of plant diseases computers are useful

Abstract

Accurate forecasting of the rice yields is very important for the organization to make a better planning and decision making. The aim of the study is to investigate the ability of neural network for yield prediction of rice in Andaman and Nicobar Islands. Field experiments were conducted at the various regions of Andaman and Nicobar Islands during 1980 to 2002 and data collected was subjected to Artificial Neural Networks (ANN) approach for developing a meaningful yield prediction for four varieties of rice C14-8, Quing Livan No.1, Taichung , Sen Yu, Jaya under island conditions with the objective to estimate the crop production a few days or months before harvesting of crop. The observations of all experiments viz., Varietal spectrum, Weather parameters , Yield components, Soil, Abiotic stressors, Biotic stressors for the period of twenty years from 1980 to 2002 were collected from the basic records of experimental field. All the data were used for training data set. Prediction of crop yield, mainly strategic plant of rice has been an interesting research area to agronomists since long for its importance in national and international economic programming. Application of ANN was found to be significant tool for prediction of yield in rice.

Biotechnology in the food processing sector targets the selection and improvement of microorganisms, including bacteria, yeasts and moulds with the objectives of improving process control, yields and efficiency as well as the quality, safety and consistency of bioprocessed products. They perform a series of processes such as fermentation, hydrolysis, etc. and even spoilage of food, depending upon the species of organisms, nature of raw material (food), environmental factors (temperature, pH, salt concentration, etc.), duration, etc.

Fermentation
Is the process of bioconversion of organic substances by microorganisms and/or enzymes (complex proteins) of microbial, plant or animal origin. It is one of the oldest forms of food preservation which is applied globally.

Computation has become an essential tool in life science research. This is exemplified in genomics, where first microarrays and now massively parallel DNA sequencing have enabled a variety of genome-wide functional assays that require increasingly complex analysis tools. The problem of accessibility of computational tools has long been recognized. Without programming or informatics expertise, scientists needing to use computational approaches are impeded by problems ranging from tool installation; to determining which parameter values to use; to efficiently combining multiple tools together in an analysis chain. The severity of these problems is evidenced by the numerous solutions to address them. Kowledge on software  and web-based interfaces for tools all improve the accessibility of computation. These approaches each have advantages, but do not offer a general solution that enables a computational tool to be easily included in an analysis chain and run by scientists without programming experience.However, making tools accessible does not necessarily address the crucial problem of reproducibility.

Introduction
Science and its development have had a great impact on the biological research. The main problem arises to understand and interpret the data which are the results of the higher dimensional research in the above mentioned field, and it is extremely difficult some times to give answers for complex and tedious biological questions generated through the large datasets. Parallel developments of statistical methods provide relaxation over these issues. The statistical method by bioinformatics presents many new and logical problems for researchers, scientists, and scholars related to these communities. Thus the knowledge of statistical concepts and techniques are prime important in this chain. The effort is made to cover stochastic theory, finite Markov chains in learning models to diffusion processes in population genetics, advanced statistical methods with Hidden Markov model and provide some literature on Bayesian computation like MCMC and their resources. Fortunately the applied aspect of probability theory and theory of stochastic processes seem to have been extensively used in the research and development level of biological field but the lacking of required material is a very problematic aspect in the system with proper and systematic ways.

In recent years, a new branch of biological science, called as bio-informatics receives more attention among its users and calls for application of computational statistical algorithms to a greater extent and needs employment of more sophisticated mathematical/statistical tools. Though biological science and information technology forms a major part in bio-informatics research as applied to agriculture, statistical tools act as their backbone. Its utility is more felt starting from the stage of framing of hypothesis till analyzing/interpreting results of a scientific experiment.  It can also be viewed as a three way multi-disciplinary approach with biotechnology, information technology and statistical technology as its three faces, with the principles of statistics applied to biological science through an interface information technology.

Abstract
This paper presents a lucid and application-oriented delineation of the theory of probability which has proved itself as a basic tool in the theoretical and applied researches in almost all disciplines of science, specially, in the problems encompassing the real-life issues. Bio-informatics has recently emerged as a potential discipline of science and, needless to mention that the application of the probability theory has delved deep to elucidate the laws under its ambit. The inference procedures based on biochemical systems require the formulation of stochastic models (owing to the prevalence of low to moderate levels of stochasticity and noise in their dynamics) which, in turn, are developed using the probability laws in abundance. In Systems Biology, the estimation of parameters poses a prime task in biokinetic models developed on experimental data, and such estimation procedures have been possible only by applying appropriate tools derived using probability. Likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty is an off-shoot of the celebrated, Bayes' theorem of probability.

A DNA microarray is a multiplex technology used in molecular biology. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, each containing picomoles (10-12 moles) of a specific DNA sequence, known as probes (or reporters).

Introduction
Recently, there is a great need to analyze and exploit the huge amount of biological data information generated through high throughput modern technologies like phenotyping, allelic variation, gene expression, chromatography, DArT, molecular marker etc. Principal component analysis (PCA) has stronghold on modern data analysis - A black box that is widely used but poorly understood. The goal of this article is to dispel the magic behind this black box and to focus on building a solid intuition for how and why principal component analysis works in huge numbers of high throughput biological data set. It is a versatile and easy-to-use multivariate mathematical statistical method (Jolliffe et al, 2003) developed to extract maximal information from large data matrices containing numerous columns and rows. It also makes possible the elucidation of the relationship between the columns and rows of any data matrix without being one of the dependent variable. So the PCA is a projection method representing the original data in smaller dimensions.

Data mining is becoming an increasingly important tool to transform the data into information. In recent years, data mining has been widely used in area of Bioinformatics for better understanding of gene expression, drug design, and other emerging problems in genomics and proteomics. Data mining is used to understand gene expression analysis, searching and understanding of protein mass spectroscopy data, 3D structural and functional analysis and mining of DNA and protein sequences for structural and functional motifs. For the first several hundred years of research in biology, the main bottleneck to scientific progress was data collection. Our new found data-richness, however, has shifted this bottleneck from collection to analysis. Biomedical literatures have been increased at the exponential rate. To find the useful and needed information from such a huge data set is a daunting task for users. Text mining is a powerful tool to solve this problem and it is used for biological knowledge discovery.

For most of the past 50 years world food production has outpaced rising demand. World population has doubled since World War II, whereas food production has tripled. In the developing world the calories available per person per day increased from an average of 1,925 calories in 1961 to 2,540 in 1992. World food production has expanded due to the Green Revolution—adoption of crop rotation, the mass production and use of petroleum-based fertilizers and chemical pesticides, expanded irrigation, and the introduction of genetically superior, disease-resistant cultivars. The trend may now be changing for the worse, however. Since about 1990 global grain production has risen only slightly and, despite slower rates of population growth, grain supplies per capita have fallen. In the worst case, Africa now produces nearly 30% less food per person than it did in 1967. As per FAO estimates, world food production would have to double to provide food security for the 8 billion people projected for 2025. By 2050, when world population is projected to be over 9 billion, the situation would be even more challenging.

Introduction
At the beginning of the 21^st century, with a population of 6.1 billion in 2000 and expected to be headed for 9.1 billion by 2050 (UN News Centre, 2005), the challenge of yet again increase food production by many folds only in 50 years has become a daunting task in itself. The situation is further worsen because now, we need to increase food production in sustainable way by 2050 on approximately the same area of arable land using less resources, particularly, fossil fuel, water and nitrogen, at a time when we must also mitigate some of the enormous challenges associated with climate change. Furthermore, there is the critical and urgent humanitarian need to alleviate poverty, hunger and malnutrition which is afflicting more than 1 billion people

Bananas and plantains constitute the fourth most important global food commodity (after rice, wheat and maize). They are cultivated in over 120 countries in the tropical and subtropical regions of the world in an area of  approximately 100 million hectares, where they constitute a major staple food crop for millions of people, as well as providing a valued source of income through local and international trade.
 
Banana is one of the oldest fruit crops of India, finding its reference even in epics like Ramayana. India is  believed to be one of centres of  origin, besides southeast Asian countries like Burma, Thailand, Malaysia and Indonesia. It is a crop having multifarious uses as every part of the plant is utilized and hence aptly  called as ‘Kalpatharu’ the plant of virtues. It is important internationally from the point of view of  trade as well as livelihood security.

2.1    Origin
    
2.2    Production
    
2.3    Distribution

2.4    World Scenario

Edible bananas are classified into several main groups and subgroups. In the first place is the diploid M. acuminata group which originated in Malaya, Indonesia, the Philippines, Southern India, East Africa, Burma, Thailand, the West Indies, Colombia and Brazil. This group is characterized by dark-brown sheaths of pseudostem,  and the leaves are nearly free from wax. The bunches are small and the fruits small, thin-skinned and sweet. Cultivars of this group are more important in New Guinea than any where else. Lady Finger which was popular in Queensland and New South Wales belonged to this group.

In second place, there was a group represented by the prominent and widely cultivated ‘Gros Michel’ originally from Burma, Thailand, Malaya, Indonesia and Ceylon. It was introduced into Martinique early in the 19th Century by a French naval officer and, a few years later, was taken to Jamaica; from there it was carried to Fiji, Nicaragua, Hawaii and Australia, in that sequence. It is a large, tall plant bearing long bunches of large, yellow fruits, and it was formerly the leading commercial cultivar in Central Africa, Latin America and the Caribbean, but has been phased out because of its great susceptibility to Panama disease. It has given rise to several named sports or mutants (Mortan, 1987). 

4.1    Importance of Banana in International Trade
    
4.2    Major Exporting Countries
    
4.3    Major Importing Countries
    
4.4    World Market Structure for Fresh Bananas
    
4.5    Domestic Trade
    
4.6    Banana Trade Wars : A Glimpse
    
4.7    Fair Trade Bananas

Most banana producers in Africa, Latin America and Asia are small-scale farmers who grow bananas either for home consumption or local markets. Because bananas and plantains produce fruit year-round, they provide an extremely valuable food source during the hunger season (when the food from one annual/semi-annual harvest has been consumed, and the next is still to come). Bananas and plantains are therefore critical to global food security. However, the export trade of banana depends mainly on commercial plantations in Latin America, East Asia and Africa. These plantations are owned by multinational corporations.

6.1    Phytochemicals
    
6.2    Health Benefits  and Therapeutic Properties of Banana
    
6.3    Validation of Health and Therapeutic Claims

7.1    Pre-Harvest Factors Responsible for Post Harvest Losses
    
7.2    Harvesting Method
    
7.3    Post-Harvest Causes Responsible for Losses

8.1    Climatic Conditions
    
8.2    Water
    
8.3    Spacing
    
8.4    Crop Nutrition
    
8.5    Pests and Diseases
    
8.6    Bunch Covering
    
8.7    Maturity and Maturity Indices
    
8.8    Maturity vs Shelf life and Quality
    
8.9    Pre-Harvest Sprays
    
8.10    Harvesting

9.1    Temperature
    
9.2    Relative Humidity
    
9.3    Physical Damage
    
9.4    Microbial Load
    
9.5    Packaging
    
9.6    Transportation

10.1    Respiration
    
10.2    Effect of Ethylene on Ripening
    
10.3    Transpiration Loss
    
10.4    Postharvest Biochemistry of Banana
    
10.5    Postharvest Pathology of Banana

11.1    Maturity Indices
    
11.2    Harvesting and Field Handling
    
11.3    Packhouse Operations
    
11.4    Packaging
    
11.5    Precooling

12.1    Shelf Life
    
12.2    Pre-Treatments Vs. Shelf Life/Storage (Hot Water, Fungicides, Coatings and other Chemicals)
    
12.3    Surface Coatings
    
12.4    1-MCP Gas Exposure
    
12.5    Use of Botanicals
    
12.6    Other Chemicals/Methods
    
12.7    Evaporative Cool Storage
    
12.8    Modified Atmosphere Storage (MAS)
    
12.9    Controlled Atmosphere Storage (CAS)
    
12.10    Vacuum Packaging
    
12.11    Hypobaric Storage
    
12.12    Irradiation
    
12.13    Chilling Injury (CI)

13.1    Biochemical Changes During Banana Ripening
    
13.2    Commercial Ripening

By definition, minimal processing would encompass any procedure, short of traditional complete preservation procedures (heat sterilization etc.) that adds value to the fruit or vegetables (Floros, 1993). Other terms used to refer minimally processed products are lightly processed, partially processed, fresh processed, fresh cut and prepared. Minimally processed fruits and vegetables are products those have the attributes of ‘fresh-like’ quality. A number of minimally processed products of fruits and vegetables are now being produced like peeled and sliced potatoes, shredded lettuce and cabbage, washed and trimmed spinach, chilled peach, mango, banana, melon and other fruit slices, vegetable snacks such as carrot and celery stick  and cauliflower and broccoli florets, packaged mixed salads, cleaned and diced onion, peeled and cored pineapple, fresh sauces, peeled citrus fruits and microwaveable fresh vegetable trays. Minimal processing of fruits and vegetables has two purposes. First, it is important to keep the product fresh but convenient without losing its nutritional quality. Second, the product should have a shelf life sufficient enough to facilitate distribution within the region of consumption. The microbiological, sensory and nutritional shelf life of minimally processed fruits and vegetables should be at least 4-7 days but preferably even longer (Ahvenainen, 2000).

15.1    Banana Chips/Crisps
    
15.2    Banana Puree/Pulp
    
15.3    Banana Flour
    
15.4    Banana Powder
    
15.5    Banana Flakes
    
15.6    Banana Figs or Dehydrated Banana
    
15.7    Banana Jam
    
15.8    Ready-To-Serve (Rts) Beverage
    
15.9    Banana Beer and Wine
    
15.10    Banana Vinegar
    
15.11    Banana Catsup
    
15.12    Banana Inflorescence Relish
    
15.13    Candied Banana
    
15.14    Other Banana Based Products

16.1    Banana Alcohol
    
16.2    Banana Pseudostem Fibre and Fibre Reinforced Products
    
16.3    Banana Pseudostem Juice
    
16.4    Banana Starch
    
16.5    Banana Waste for Mushroom Production
    
16.6    Paper and Pulp from Banana Waste
    
16.7    Vermicompost and Vermiwash from Banana Waste
    
16.8    Animal Feed from Banana Waste
    
16.9    Banana Leaf

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.

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

Partialy Balance Incomplete Block Design (PBIBD)
    
Interblock and Intrablock Information

Preliminaries
    
Results on 22 Factorial Experiment
    
23 Experiment in r Replication
    
Confounding

Q.1.    What is split plot experiment ?

    Answer
    
    If one has to study two type of factors say A and B in the same experiment where A requires larger plot size and B requires smaller plot size then the structure of the experiment will be that of plots within blocks are known as split plots and blocks within replication are known as main (whole) plots. Such a layout of the experiment is known as split plot experiment.

Q.1.    Define strip plot design ?

    Answer
    
    There is a variant of the split plot design in which both factors A and B will be applied to the larger strips by dividing the experimental field into as many rows as the number of levels of one factor A and into as many columns as the number of levels of second factor B. Then the two factors will be applied at random to the rows and columns. Such a layout is known as strip plot design.

Q.1.    What is progeny row trial ?

    Answer
    
    The replicated experiment designed to compare a number of parent plants with respect to any characteristic under study is known as progeny row trial.

Q.1.    In what sense the analysis of covariance is an extension of ANOVA ?

    Answer
    
    The analysis of covariance is an extension of ANOVA in the sense that it covers the case where observations are taken on more than one variable from each experimental unit. The interest centers on the study variable y which depends upon on auxillary variable x.

Test for Significance for Population Mean
    
Test for Significance of Population Proportion