The book entitled “Basic Concepts in Statistics” has been written for all P.G., Ph.D.students in the statistics, social science, biological science and faculty members of statistics, agricultural statistics, statistics, agricultural scientists and other students who have to appear in competitive examinations with statistics as a subject in state P.S.C., U.P.S.C., A.S.R.B. & I.S.S. etc.  This book is the outcome of 24 years of teaching experience to U.G., P.G. & Ph.D. students of the disciplines, agricultural engineering and agricultural statistics. The book contains 15 chapters covering different topics of statistics and has been written with views for its readers and users to get familiar with basic knowledge of important topics of statistics.  This book will broaden the horizon of basic concepts and knowledge of its readers. During the preparation of the manuscript, the author has incorporated the fruitful academic suggestions provided by Dr. Housal P. Singh, (Prof. & Head, School of Studies in Statistics, Vikram University, Ujjain, M.P.) to enrich the book. The authors are very much thankful and extends their honourable honor of high order to the Vice Chancellor (JNKVV, Jabalpur) and Director (IISR, Lucknow) for their kind permission to hand over the manuscript to “New India Publishing Agency” New Delhi to publish it in the shape of a book.

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

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

Preliminaries      Minimum Variance Unbiased Estimator and Cramer, Rao Inequality      Minimum Variance Unbiased Estimator (M.V.U.E.) and Black Wellisation      Results on UMVUE and MVUE      Efficiency and Consistency      Consistency      Sufficiency and Completeness      Distribution Possessing Sufficient Statistics      Sufficient Statistic for Several Parameters      Completeness of a Sufficient Statistic      Some Important Results in Theorem Forms      Minimal Sufficient Statistic      Results on Complete Sufficient Statistic

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

Preliminaries and Test Based on N. P. Lemma      More on Testing of Hypothesis

Q.1.    What is the most fundamental assumption to be followed by random variable for using the standard method of parametric statistics or parametric inference ?     Answer          The most fundamental assumption required to be followed by random varibale for using the parametric statistics to have statistical inference drawn from certain set of data is that the random variables have normal distribution.

Bibliography 1.    Cochran, W.G. and Cos, G. M. (1957) : Experimental Designs, II, Edition, John Wiley and Sons, New York.      2.    Fisher, R. A. (1947) : The Design of Experiment, Alive and Beyond, London.      3.    Federer, W. T. (1967) : Experimental Design. The Macmillan Co. New York.      4.    Goon, A. M. , Gupta, M. K. and Dasgupta, B. (1972) : An Outline of Statistical Theory, Vol II, The World Press Pvt. Ltd. Calcutta.      5.    Gupta, H. C. and Kapoor, V. K. (1990) : Fundamental of Mathematical Statistics, Sultan Chand and Sons, New Delhi.      6.    Gupta, H. C. and Kapoor, V. K. (1990) : Fundamental of Applied Statistics, Sultan Chand and Sons, New Delhi.      7.    Kendal, M. G. and Stuart, A. (1967) : Advance Theory of Statistics, Inference and  Relationship, Vol II, Charles, Griffin and Co. Ltd. London.      8.    Murray, R. Spiegel (1961) : Theory and Problems of Statistics, Schaum Publishing Company, U. S. A.      9.    Mood, A. M., Grajybill, F. A. and Bose, D. C. (1974) : Introduction to The Theory of Statistics, McGraw Hill. Kogakusha, Ltd. London.      10.    Panse, V. G. and Sukhatme, P. V. (1967) : Statistic Methods