Plan of Minor in Statistics
Course
Description:
-
Stat 231(3) minor
changes.
data collection, description of data, data analysis, measures of central tendency, measures of dispersion. Normal distribution, chi-square, t-distributions. Introduction to probability, conditional probability, Bayes’ theorem. Probability distributions such as binomial, and poisson distributions. Sampling and sampling distributions. Inference about the mean and standard deviation. Hypothesis testing. Simple regression and correlation.
Prerequisite: No prerequisite .
Introduction to statistics: data collection, description of data, data analysis, measures of central tendency, measures of dispersion. Normal distribution, t-distributions, Introduction to probability, conditional probability, binomial distributions. Sampling and sampling distributions. Inference about the mean and standard deviation. Hypothesis testing. Introduction to regression and correlation.
Prerequisite:
Math 135 or Tawjihi scientific stream
(No credit for science and engineering students)
Stat.331: Statistical
Methods:
Inference on
difference of means, ANOVA, chi-square, test (univariate and bivariate)
inference on population variance and ratio of variances, regression analysis
using least squares estimation, nonparametric tests.
Prerequisite: Stat 231 or equivalent
Stat. 332: Introduction to Mathematical Statistics:
Probability, Probability distributions, both continuous and discreet such as, normal distribution, Chi-square, Gamma, Beta, T, F and other continuous distributions. For the discrete case, Binomial, Poisson and other distributions should be considered. Finding distributions for transformed random variables using moment generating function technique and the Jacobean method for transformations with several variables. Limiting distributions, central limit Theorem.
Prerequisite: Stat 231 and math 231 for science students, or Stat 236, Math 237 for others.
Stat. 333: Introduction to Mathematical Statistics II:
Point Estimation, confidence interval estimation for population mean, difference between two means. Testing of hypothesis. Chi-square-test, sufficient statistics. Properties of sufficient statistics, complete sufficient statistics. Exponential family, Bayer estimation Fisher information, Cramer-Rao inequality, limiting distribution for maximum likelihood estimators. Uniformly most power full test. Likelihood ration test. Estimation related to normal models.
Prerequisite: Stat 332.
Stat.334: Regression Analysis:
Simple regression, estimation of regression function, error estimation of regression function. Inference related regression coefficients. Regression applications residual treatment, F-test of the aptness of the model simultaneous estimation of regression coefficients. Matrix method in regression analysis. Multiple regression. Polynomial regression. Qualitative independent variables construction of the model.
Prerequisite: Stat 333, Stat 331.
Stat.337: Categorical Data Analysis:
Measures of association, such as Phi, Lemad, Somors, etc…Mantel-Haenszel method. Weighted least square. Maximum likelihood estimates of linear models including log linear models
Prerequisite: Stat. 333.
Stat. 431: Sampling Techniques(1):
Simple random sampling, properties of estimators variance of estimators, ratio estimators. Estimation of totals. Sampling of estimators, ratio estimators. Estimation of totals. Estimation of totals. Sampling of percentages. Effect of p on standard error. Conditional distribution of P. Comparison of two percentages. Estimation of percentages in case of cluster sampling, Estimation of sample size for both quantitative and qualitative data. Stratified sampling including properties of estimates and sample size estimation.
Prerequisite: Stat 332, Stat 331
Stat. 432: Sampling Techniques(II):
Stratified sampling including effect of deviation from optimal allocation. Deciding number of strata construction of strata. Two ways stratification. Approximation of variance for ratio estimators and other things related to ratio estimators. Regression estimators and related topics. Cluster sampling and related topics. Systematic sampling, Cluster sampling for unequal sized cluster. Sub sampling for both equal and unequal sized units. Double sampling, sources of error in surveys.
Prerequisite: Stat 431.
Special topics in statistics will be offered according to student needs.
Prerequisite: Approval of the department.
