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Course Title[Course Code]:Mathematics (4)[BSC 228]

Faculty: Computers and Artificial Intelligence
Department: Artificial Intelligence
Program: Computers System
Compulsory / Elective:Compulsory
Undergraduate(Second Year-Second Semester)
Lecture:( 4 ) Practical / Clinical:( - ) Tutorial:( 3 )

Course Description:
The course aims at introducing the Review of sampling theory and distributions. Estimation theory: Unbiasedness, efficiency, points estimates, confidence interval estimates (for means, proportions, differences, sums, variances, and variance ratios), maximum likelihood estimates. Tests of hypotheses and significance: Null hypothesis, type I and type II errors, level of significance, special tests of significance for large or for small samples, operating characteristic curves, quality control chart, fitting theoretical distributions to sample frequency distributions, goodness of fit. Curve fitting, regression and correlation: Method of least squares, multiple regression, (linear generalized and rank) correlation, correlation and dependence. Analysis of variance: Purpose, one-factor experiments, variation, linear mathematical models, F-test for the null hypothesis of equal means, modifications for unequal numbers of observations, two-factor experiments, experimental design.