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Ph.D Awarded
Ph.D Pursuing
It shall be made compulsory for all the internal Ph.D. Scholars to pursue the following courses for 100 Hours each at F.R.I (University) or its Research Centers. However, pursuance of these courses by an external research Scholar shall be optional unless specifically recommended by the R.A.C concerned:-
  1. Computer application course as per syllabus vide Appendix - 1
  2. Statistical analysis course as per syllabus vide Appendix – 2

Short-term basic course in Silviculture/Forestry/Forest Management/ Mensuration/Forest Ecology subjects for those scholars who may be deficient in knowledge of these subjects in the opinion of the RAC. In such cases the RAC concerned may recommend all or any of the subjects to be pursued and passed by the scholars before the award of Ph.D. Degree.  (Amended vide resolution no. 2 of the academic council meeting held on 11.11.2005)


In all such cases, where research scholar may have pursued these courses as given in item no. (i) & (ii) above as a part of curriculum in any other course, the research scholar concerned shall be allowed to appear in the exam directly and be exempted to appear in the regular classes. The scholar will have to pass the exam as per the rules of the FRI (Deemed University) and a certificate to that effect will have to be issued by the research center concerned and submit to the office of the Registrar, FRI (Deemed University) at the time of submission of the thesis (Amended vide resolution of the academic council meeting held on 3-3-2004). 
APPENDIX- 1

Syllabus for computer application course for Ph.D. Research scholars

1. COMPUTER FUNDAMENTALS
  1. Computer Basics
  2. What is computer?
  3. Why computers?
  4. Types of computers
  5. Classification of Digitals
  6. Elaboration of Hardware, Software & Manware
  7. Computer Terms into computer terminology
2. WORD PROCESSOR (MICROSOFT WORD-2000)
  1. What is a word processor?
  2. What is desktop Publishing?
  3. Uses of word Processor
  4. Understanding different menus submenu
  5. Understanding hotkeys
  6. Utilities of word processor for normal and desktop
3. OPERATING SYSTEM (MSDOS+WINDOWS 95/98)

    a)   Commands utilities
    b)   Difference between GUI & CUI
    c)   Internal & external Dos commands
    d)   Wildcards
    e)   Win 95 / 98 Desktop
    f)    Win 95 / 98 Menus
    g)   Windows as a view

4. ELECTRONIC SPREAD SHEET (MICROSOFT EXCEL-2000)
  1. What is spread sheet?
  2. Areas of Application
  3. Construction formulas
  4. Understanding menus and submenus
  5. Designing graphs and charts
  6. Understanding uses of spreadsheets and workbook
5. PRESENTATION GRAPHS (MICROSOFT POWERPOINT-2000)
  1. Understanding presentation graphics
  2. Designing slides
  3. Designing slideshow
  4. Understanding transitions
  5. Understanding menus and submenus
  6. Project
6. DATABASE (MICROSOFT ACCESS-2000)
  1. Understanding database
  2. Understanding Data mining
  3. Understanding menus and submenus
  4. Constructing Quires
  5. Constructing forms
  6. Constructing tables
  7. Constructing Reports
  8. Data Management
  9. Projects
7. PROGRAMMING LOGICS
  1. Programming Basics
  2. Flow charting and data diagrams
  3. Problem solving approach
  4. Differentiating between structured and object oriented programming
  5. Making Pseudopodia
  6. Making Algorithms
  7. Decision Making
  8. Making Progra
8. INTERNET TECHNOLOGY
APPENDIX- 2

Syllabus For Statistical Analysis course for Ph.D. Students
1. SOME BASIC CONCEPTS OF STATISTICS

Variable, Population, Sample, Mean, Variance, Parameter, Statistic, Normal distribution, Standard error, Degrees of freedom.

2. TEST OF SIGNIFICANCE

Introduction, Level of significance, null, and alternate, hypothesis, general procedure of test of significance, test of significance for difference of means students t- test, testing significance of difference between two sample means, testing significance of an observed sample correlation coefficient, testing significance of regression coefficient, testing the equality of variances. 

3. ANALYSIS OF VARIANCE

Introduction, assumption, model, assumption in the model, degree of freedom, mean sum of square, test statistic, one way analysis of variance, critical difference and bar diagrams two way analysis of variance, (ANOVA) with single observation per cell two way ANOVA with more than one observation per cell.

4. DESIGN OF EXPERIMENT

Introduction, experiment and design, making inferences, principles and designs of experiment-randomization, replication error control, completely randomized design (CRD), layout of CRD, model analysis (ANOVA) advantages and disadvantages of CRD application of CRD randomized block design (RBD) layout of RBD, model, ANOVA, advantages of disadvantages of RBD, Latin square design (LSD) layout of LSD advantages and disadvantages of LSD.

5. FACTORIAL EXPERIMENT

Introduction, layout model, ANOVA advantages and disadvantages, split plot design, main features of split plot design.  

6. INCOMPLETE BLOCK DESIGN

Introduction, balanced incomplete block design parameter condition, layout of entire systematic BIBD, model, ANOVA, advantages and disadvantages.

7. SAMPLING TERMINOLOGY AND CONCEPTS

Population, sampling units frame sample intensity some common methods of probability sampling, population total and population mean-population variance, coefficient or variation-parameter estimator estimate bias in estimation sampling variance standard error accuracy and precision confidence limits. 

8. SIMPLE RANDOM SAMPLING

Definition Method of selection of random sample, notations, estimate of population mean and sampling variance- A practical demonstration of the theory of simple random sampling- Ratio method estimation- sampling with probability proportional to size of sampling units- Methods of selection a sample and examples.

9. STRATIFIED SAMPLING

Basis of stratification-number if strata- allocation of sample within the scrate- selection of units within the strata- notation- estimation of mean and variance, standardization sampling – guidance to stratification, stratified sampling with units in each strata and examples.

10. SYSTEMATIC SAMPLING

Introduction- a simple example- periodicity in population meaningful ordering of the unit’s estimation of precision from a systematic sample

11. CHOICE OF SAMPLE SIZE (SAMPLING INTEREST)

Choice of basis of sampling units- size of samples for a desired precision or cost- simple random sampling – stratified random sampling examples.

12. DOUBLE SAMPLINGS

Introduction- regression and ratio estimation in double sampling examples.

13. POINT SAMPLINGS

 Introduction – instrument used in point sampling explanation of the method- definition of terms used in point sampling. Theory of points sampling. Particular cases- basal area per hectare- number of stems per hectare- volume per hectare- practical.

14. MULTIVARIATE INTRODUCTION
  1. Out line of multivariate methods.
  2. Multivariate normal distribution
  3. Clustering
  4. Discriminant analysis
15. CORRELATION AND EGRESSION

Introduction, Pearson’s coefficient of correlation, rank and correlation, partial correlation, multiple correlation. Introduction of regression, line of regression, regression coefficient.
Multiple regression with one variable, linear combination and regression coefficient, muti co-linearity.