The course helps students explore and learn about popular real-world topics using statistics as a tool. It discusses statistical application in population growth, economic developments, income distribution and environmental changes. Key statistical tools will be introduced through their applications in real world issues.
This course helps students handle statistical exploratory, descriptive and estimation tools in business applications. It includes data collection, tabular and graphical presentation, descriptive statistics, probability distributions, sampling distributions and statistical estimation.
This course helps students use statistical methods for making decisions in Business and Economics. This course includes hypothesis testing for one and two means and for one and two proportions, nonparametric tests, single factor analysis of variance, chi-square test for goodness-of-fit, chi-square test for independence, contingency tables, simple and multiple regression and time series analysis.
This course introduces students to the fundamental concepts of statistics and trains them to apply the basic methods and techniques of statistical analysis in business and economics problems. It covers basic concepts, sources and methods of data collection, tabular and graphical presentation of data, descriptive statistics, introduction to probability and probability distributions, sampling distributions, statistical estimation, hypotheses testing, analysis of variance, chi-square test of independence, and correlation and regression analysis.
This course introduces the basic concepts and elementary applications of statistics that are widely utilized by psychologists. It covers data description, central tendency measures, variability indicators, and degrees of peakedness and asymmetry of data distributions. In addition, the normal distribution, standard scores, correlation and their applications in psychology and as well as hypothesis testing will be studied in this course. Statistical packages will be used throughout the course to work out psychological applications.
This course introduces students to events and sample space, probability, conditional probability, random variables, cumulative distribution function and probability density function, moments of random variables, common distribution functions, elementary introduction to statistics with emphasis on applications and model formulation, descriptive statistics, sampling and sampling distributions, inference, t tests, one and two factors analysis of variance, randomized complete block design, correlation and regression, and chi-square tests.
This course provides students with statistical methods for modeling and analyzing social data. It includes data collection, tabulation and graphical presentation, statistical measures, cross-tabulation analysis, and principles of survey data analysis using statistical packages. It emphasizes the use of the computer package (SPSS) to analyze real social data.
This course provides students with statistical methods for modeling and analyzing social data. It includes data collection, tabulation and graphical presentation, statistical measures, hypothesis testing, principles of survey data analysis using statistical packages.
This course is an introduction to the principles and laws of probability. It gives the student a thorough understanding of the concepts of probability, conditional probability, random variables and probability distributions, moment generating function, bivariate and marginal distribution functions, conditional distributions and expectations. Although the primary focus of the course is on a mathematical development of the subject, it also includes a variety of illustrative examples and exercises that are oriented towards applications in the social and physical sciences.
This is an introductory course for students in biological sciences who have no formal background in statistics. It covers the basic statistical methods for describing and analyzing data arising in the biological sciences. The emphasis will be on the intuitive understanding of concepts rather than the underlying mathematical developments. Applications and data analysis will be based on the statistic package Minitab.
This course develops students' understanding of the methodology and the theory underlying a number of statistical techniques applicable in solving real-life inference problems under minimal assumptions about the underlying distribution of the data. It covers the following topics: order statistics, distribution free tests, single and multi-sample rank statistics, Pittman's efficiency and rank correlations.
The course introduces students to the basic concepts and methods of probability and statistics with applications in the education field. It includes sample spaces and events; counting techniques; probability; conditional probability; random variables; cumulative distribution function and probability density function; moments of random variables; sampling and sampling distributions, inference about means and proportions, correlation and simple regression.
This course introduces the basic concepts of statistical inference and their applications in psychology. It covers sampling distributions, point and interval estimation, statistical hypothesis testing, correlation, regression and prediction, analysis of variance and factorial ANOVA. Statistical packages will be used throughout the course to work out psychological applications.
This course introduces students to Stochastic processes as models of time-dependent random phenomena. It covers Markov chains; Autocorrelation and Stationary; Fourier Transforms; Queuing Theory.
This course helps students select the appropriate design for an experiment and analyze its results using statistical packages. It includes complete randomized designs, ANOVA, multiple comparisons, residual analysis, factorial experiments, ANCOVA, randomized block designs, Latin squares.
This course introduces students to the methods of regression analysis and trains them to fit regression models to data. This course includes simple and multiple linear regression, dummy variable regression, model selection, diagnostics for residuals, multi-collinearity detection, transformations, lack-of-fit tests, partial and sequential F-tests.
This course introduces the basic concepts of estimation and hypothesis testing. It includes point estimation, properties of estimators, method of moments, method of maximum likelihood, method of least squares, interval estimation, most powerful tests and likelihood ratio tests. It also covers some common confidence intervals and tests for means, variances and proportions.
This course introduces techniques of demographic analysis and their applications using computer packages. It covers vital statistics, rates and proportions, population distribution by age and gender, mortality, fertility and migration, life tables, population projections, and estimation.
The course develops an understanding of survey research methodologies and data collection methods from scientific and practical perspectives. It emphasizes training students on alternative sample designs used to produce statistical inferences to solve real-life problems. In addition to discussing survey methods and design, it covers: simple, stratified, systematic and cluster sampling, ratio and regression estimates, errors in sample surveys and case studies.
This course trains students to select the appropriate time series model, estimate the parameters and make forecasts. It includes time series regression, classical decomposition, exponential smoothing, autocorrelation and partial autocorrelation functions, stationary and homogeneous time series, autoregressive, moving average, ARMA and ARIMA models and seasonal models, Box-Jenkins methodology and business applications.
This course introduces students to the methodology and applications of multivariate statistical analysis. It covers multivariate analysis of variance and regression, canonical correlations, principal components, factor analysis, discrimination, classification and cluster analysis. The emphasis is on computer implementation and applications to the various sciences rather than the theoretical aspects of the topics.
This course is an introduction to topics in categorical data analysis. It is an applied course emphasizing the modeling and the analysis of categorical data using the statistical package SPSS. Both descriptive and inferential methods are discussed. The covered topics include measures of association, tests of goodness-of-fit, tests of independence, exact tests, logit and probit models and discriminant analysis.
This course introduces the basic process control and acceptance sampling techniques. It covers the objectives of statistical quality control, control charts for variables, control charts for attributes, acceptance sampling, single, double and multiple sampling, and the OC curve.
The course introduces students to common computational techniques needed in statistics. It covers, in particular, data manipulation and cleaning techniques, sampling, simulation, resampling, maximum likelihood estimation and elementary Bayesian analysis. These techniques will be demonstrated using prominent statistical packages.
This course uses the case teaching technique. During the course students will work in groups to solve various cases / capstone experiences / projects. Students are also expected to write reports and give oral presentations for each project. Each group will be assigned a project that requires the use of international, national and /or official statistical databases.
This course is dedicated to graduate students from College of Science. It introduces the students to the basic statistical procedures commonly used in the analysis of scientific and environmental problems. These statistical applications complement and reinforce scientific and environmental concepts and methods, particularly in practical, development and assessment models, and interpretation of data and results. It includes numerical and graphical description of data, techniques for significance evaluation and relationships.
The course provides a structured approach for describing, analyzing, and finalizing decisions involving uncertainty. It introduces various decision analysis techniques and principles of designing decision support systems for carrying out sensitivity analysis. It also presents key probability and statistical techniques used in modeling and analyzing business data and providing empirical evidence for action recommendation. Topics include decision analysis techniques, descriptive and inferential statistics, one-way and two-way analysis of variance, modelling using regression analysis, times series regression, exponential smoothing and forecasting.
This courses provides students with an understanding of the required steps in planning experiments; principles of experimental design; application of some designs in product development systems and evaluation factorial design; linear programming, CRD, RCD, LS, regression and correlation: and inspection of mean differences.
This course focuses on design of experiments, optimum selection of input for experiments, and the analysis of results. Full factorial as well as fractional factorial designs, response surface designs, complete randomized designs, ANOVA, multiple regression, normal probability plot, importance of analyzing interactions, signal to noise ratios, confidence intervals, and variance reduction analysis are covered in this course. Statistical analysis software such as SPSS and Minitab will be used.
This course provides students with an understanding of mathematical models for evaluating resource management strategies. It covers stochastic and deterministic simulation for optimization, System control structures and team modeling approach.
This course prepares MBA students to design and conduct research to address and solve business challenges. It provides an empirical basis for the analysis and action recommendations for the solution of business problems or for the achievement of business objectives. MBA students will learn to frame, plan, and conduct research projects as well as developing and fine-tuning forecasting models. Students will apply key statistical techniques used in modeling and analyzing research findings and business data.
This is a graduate course that covers the principles of risk and uncertainty applied to hydraulic, environmental and other water-related problems. It includes such topics as statistical measures and graphs, parametric and non-parametric statistical inference, analysis of variance, multiple regression and correlation.
This course provides students with an understanding of computer-based methods in geographical analysis. It focuses on bivariate and multivariate regression, discriminant analysis, factor analysis, and analysis of spatial and temporal data.
This course provides students with an understanding of computer-based statistical methods in petroleum sciences and engineering. Focuses on estimation of parameters, comparisons of treatments, multivariate techniques such as multivariate regression, discrimination analysis and Statistical analysis of field and petroleum engineering data.