An Overview of Statistical Techniques for Analysis of Data in Agricultural Research | Chapter 11 | Emerging Issues in Agricultural Sciences Vol. 8

 The significance of statistical analysis is paramount in research, particularly in situations involving the collection, classification, analysis, and interpretation of numerical data. Statistical principles find widespread application in various types of experimental studies and serve as a critical component in agricultural research endeavors. The inherent variability present in commonly used experimental materials within agricultural research necessitates the utilization of statistical methods, leading to numerous advancements and innovations in the field of statistics. The selection of an appropriate tool for data analysis and subsequent processes involving statistical components has become a matter of concern. This chapter delves into the diverse statistical techniques essential for the analysis of agricultural data and the derivation of valid conclusions.

Author(s) Details:

Rahul Banerjee,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Bharti,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Pankaj Das,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Varun Srivastava,
Department of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India.

Ankita,
Department of Agricultural Statistic and Computer Application, Birsa Agricultural University, Kanke, Ranchi, India.

Suraj Kataria,
Department of Anthropology, University of Delhi, Delhi, India.

Bulbul Ahmed,
Department of Agriculture, Galgotias University, Greater Noida, India.

Nitin Varshney,
Department of Agricultural Statistics, Navsari Agricultural University, Navsari, India.

Please see the link here: https://stm.bookpi.org/EIAS-V8/article/view/12877

Transformation of Data in Agricultural Research | Chapter 7 | Current Topics in Agricultural Sciences Vol. 8

When variances are diverse and some functions of means, data transformation is the most suitable corrective action. This method involves converting the original data to a new scale, producing a new data set that is anticipated to fulfil the homogeneity of variances. The comparative values between treatments are not changed, and comparisons between them are still valid, because a same transformation scale is applied to all data. The remedy for heterogeneity in trials, when certain treatments have mistakes that are noticeably larger (lower) than others because of the nature of the treatments investigated, is called error partitioning. We covered the most popular data transformation methods in this chapter along with instances from the real world.

Author(s) Details:

Bhim Singh,
Department of Basic Science, College of Agriculture, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, (U.P.), India.

Amar Singh,
Department of Agricultural Statistics, CSSS PG College (Affiliated to CCS University, Meerut, U.P.), Machhra, Meerut, (U.P.), India.

Prerna Sharma,
Department of Basic Science, College of Agriculture, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, (U.P.), India.

Please see the link here: https://stm.bookpi.org/CTAS-V8/article/view/7256