Introduction Example
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INTRODUCTION TO EDUCATION
Department of Education Bodoland University Kokrajhar-783370 B.A. 1st Semester Paper Title: Introduction to Education (Major Course) Paper Code: EDNMAJ-101-4 PAPER CREDIT: 04 (4T) Total No. of Lectures=60 Total Marks= 100 (T70+IA30) Course objectives:- • To develop an understanding of the meaning, aims, objectives and functions of Education. • To acquaint with the school and its functions and relationship with society. • To develop knowledge about various types of curriculum, correlation of studies and forms of discipline. Course Learning outcomes: After completion of this course the learner will be able to: • Discuss the basic aims of education and its fundamental concept. • Apply knowledge of foundational concept and theories to real world educational scenarios. • Develop critical thinking skills to evaluate educational policies, practices and research.
Introduction to Social Research
'Introduction to Social Research' presents the essential elements of both qualitative and quantitative approaches for conducting empirical research in the social sciences.
Introduction to Time Series and Forecasting
Author: Peter J. Brockwell
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-14
Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.