JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives

to understand the role of statistic and probability in the spatial data analysis and

design

Course Outcomes

at the end of the course the student will able to learn to Understand/(Solve the

problems using) the advanced statistical approaches, Identify the statistical methods for solving geospatial problems, apply the advanced statistical methods for image processing and to use geo- statistics for studying spatially varying phenomena

UNIT - I

Basic Statistics: Sources of Data, Organization of Data, The Histogram, Measures of central tendency, Mean Deviation, Standard Deviation, Correlation, Coefficient of correlation, Rank correlation, Regression.

UNIT - II

Probability: equally likely, mutually exclusive events, definitions of probability, additions & multiplication theorems of probability and problems based on them. Bayesian approach, distributions; Poisson, normal, Erlang, Gamma and Weibull probability distributions

UNIT - III

Multivariate Data: Random Vectors and Matrices, sample estimate of centroid, standard deviation, SSCP, dispersion, variance, covariance, correlation matrices.

UNIT - IV

Multivariate Regression Models, Multiple linear Regression: Multiple parameter estimation by method of least squares, tests of significance use of dummy variables, problems associated with multi co- linearity, heteroscadasticity.

UNIT - V

Geostatistics- Pattern Analysis, Measures of Arrangements & dispersion, Auto Correlation, Semiveriogram, Kriging;

TEXT BOOKS:

• 1. Gupta, S.C. and Kapoor, V.K., “Fundamentals of Mathematics Statistics”, Sultan Chand and Sons, 2001.
• 2. Johnson, R.J., “Miller and Freund’s Probability and Statistics for Engineers” 6th Edition, Prentice Hall of India, 2002.

REFERENCES:

• 1. Jay L. Devore, “Probability and statistics for Engineering and the Sciences”, Thomson and Duxbbury, 2002.
• 2. Sarma, D.D. “Geostatistics with Applications in Earth Sciences”, Capital Publishing Company, 2002.
• 3. Cooley W. W and Lohnes P.R .- Multivariate Data Analysis, John Wiley and Sons,1971.