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Course Contents

1. Basic Concept of Probability and Distributions. Experimental Error and their Characteristics (a) Random error (b) Bias(c) Propagation of random error(d) Error due to rounding off (e) Calibration. Adjustment Computations (a) Condition method (b) Observation method (c) Combined method4. Sampling (a) Population and sample (b) Sample designs (c) Sample statistics (d) Sampling distribution of mean and variance (e) Question of sample size. Estimation and Hypothesis Testing (a) Properties of good estimates (b) Interval estimation (c) Maximum likelihood estimates (d) Sample size determination (e) Basic format of hypothesis testing (f) 'Type I and Type II errors (g) One and two tailed tests (h) Tests on mean and variance from samples under different assumptions and knowledge of the underlying distribution6. Regression Analysis and Hypothesis Testing (a) OLS estimates (b) Assumptions and proof of BLUE (c) Detection, effect, and remedy of multi co linearity (d) Detection, effect, and remedy of hetero skedasticity (e) Detection, effect, and remedy of auto correlation (f) Misspecification errors and regression model building (g) Hypothesis testing on OLS estimates (h) GIS (i) Comparison of regression model U) Use of dummy independent variables (k) Robust regression and effect of outliers. Miscellaneous Topics (a) Fitting theoretical distributions to observed frequency distributions (b) Tests of goodness sophist (chi square test, Kolmogorov Smirnov test) (c) Identification of out liers (d) Cluster analysis. Practical applications with (Civil) engineering data.