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.

- Analyze Distributional Properties As They Apply To A Real Life Issue.
- Apply Statistical Techniques To Compute Common Statistical Measures Used In The Modern World.
- Define And Formulate Real-World Problems In Scientific/Probabilistic Terms Using Statistical Techniques.
- Identify Statistical Indicators Used To Describe World Social And Economic Developments.
- Interpret Statistical Results And Draw Conclusions Concerning The Use Of Statistics In Solving Real-World Problems.

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.

- Apply Descriptive Statistics Techniques To Describe Business Data Using Computer Package.
- Apply Statistical Methods And Models In Business Situations Using Computer Package.
- Communicate Statistical Information In Oral And Written Form.
- Define And Apply The Basic Concepts Of Probability Theory And Statistics To Business Problems.
- Define Random Variables And Their Distributions.
- Identify Statistical Methods Used In Estimation, Testing And Regression Models.

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.

- Analyze And Interpret Relationships Between Qualitative Or Quantitative Variables Using Correlation And Contingency Tables.
- Apply Descriptive Statistical Techniques To Summarize And Describe Data Arising In Psychology Using A Computer Package.
- Describe The Elements Of A Statistical Test.
- Describe The Fundamental Statistical Concepts In Psychology.
- Perform Point And Interval Estimation Of The Population Mean And Proportion.

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.

- Apply Descriptive Statistical Techniques To Describe Data Using Statistical Software.
- Communicate Statistical Information In Oral And Written Form.
- Define And Apply The Basic Concepts Of Probability Theory And Statistics To Real Situations.
- Define And Compute The Common Probability Distributions Used In Modeling Data Arising In Engineering And It.
- Plan, Analyze, And Interpret The Results Of Experiments.
- Select And Apply The Appropriate Statistical Methods In Analyzing Data Using Statistical Software.

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.

- Compute Unconditional And Conditional Expectations, Including Moments And Moment Generating Functions.
- Define Conditional Probability And Compute Posterior Probabilities.
- Describe The Concepts Of Probability And Its Basic Properties And Laws.
- Use A Variety Of Counting Techniques To Compute Probabilities.
- Use Random Variables And Their Distributions In Modeling Applied Problems.

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.

- Apply Appropriate Statistical Methods For Describing And Presenting Data Arising In Biological Sciences.
- Appropriately Choose Probability Models For Use In Problems Arising In Biological Sciences.
- Define The Fundamental Statistical Concepts In The Design And Analysis Of Biological Studies.
- Identify And Conduct Appropriate Methods Of Hypothesis Testing.
- Identify And Utilize Appropriate Methods Of Estimation For Application To Data.
- Identify The Types Of Data Arising In Biology And Health Sciences.
- Plan, Analyze And Interpret The Results Of Statistical Analyses.

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.

- Apply Nonparametric Techniques To Real Life Problems.
- Compare Nonparametric And Parametric Statistical Methods.
- Describe The Role Of Nonparametric Statistics In Statistical Science.
- Recognize Nonparametric Techniques Used In Testing, Contingency Tables, Correlation And Regression, And Goodness Of Fit.
- Use A Statistical Package To Implement Nonparametric Methods

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.

- Define the basic concepts of analysis of variance.
- Discuss the basic concepts of experimental designs.
- Choose and plan an adequate statistical design for various experimental setups including factorial experiments, randomized block designs and ANCOVA.
- Recognize the ethical dimension related to experimental design.
- Analyse data and interpret results from various experimental designs such as CRD, RBD and ANCOVA.
- Use statistical packages in the design and analysis of experiments.

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.

- (STAT130 with a minimum grade D or STAT210 with a minimum grade D or STAT235 with a minimum grade D)
- STAT230 with a minimum grade D

- Apply Model Diagnostic And Remedial Methods (Outliers, Multi-Collinearity, Heteroschedasticity).
- Demonstrate In Depth Knowledge Of Concepts And Methods Of Regression Analysis.
- Interpret Regression Results And Parameters.
- Select An Appropriate Regression Model That Fits A Data Set.
- Test Regression Model Validity.
- Use Appropriate Statistical Software To Explore And Analyze Regression Data.

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.

- STAT230 with a minimum grade D
- (STAT130 with a minimum grade D or STAT210 with a minimum grade D or STAT235 with a minimum grade D)

- Apply Common Tests About Means, Proportions And Variances.
- Compare Estimators Based On Their Properties.
- Construct Appropriate Tests For Simple And Composite Hypotheses.
- Derive Confidence Intervals For Various Estimation Problems.
- Derive Point Estimators Using Various Methods.
- Describe The Basic Concepts Of Hypothesis Testing.
- Describe The Basic Concepts Of Point And Interval Estimation.
- Identify The Common Statistical Distributions And The Common Methods For Deriving Sampling Distributions.

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.

- Demonstrate A Working Knowledge And Experience In The Use Of Computers In Demography Including Access To Electronic Data Sets, Internet Resources For Demographers And The Use Of Projection Software.
- Demonstrate Practical Skills In Analyzing, Manipulating Demographic Data, Making Population Projections And Forecasts, And Constructing Life Tables
- Interpret And Communicate Results Of Demographic Analyses Orally And In The Forms Of Charts, Tables And Written Reports.
- Operate Individually Or In A Team To Solve Demographic Problems While Demonstrating Ethical And Social Awareness.
- Recognize Key Concepts And Methods Of Demography, Its Fundamental Theories And Major Applications And Data Sources.

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.

- Calculate Point And Interval Estimators Of Relevant Parameters Under Different Sampling Designs.
- Calculate Sample Sizes For Prescribed Precision And Power.
- Define The Different Sampling Procedures, Study Designs, And Sources Of Errors In Sample Survey.
- Determine The Accuracy Of The Estimators Of Relevant Population Parameters.
- Identify A Suitable Sampling Design Given Available Information And Resources.
- Interpret Survey Findings Through Written Report And Oral Presentations.
- Use Appropriate Statistical Software To Select Random Samples And Analyze Survey Data.
- Use Different Data Collection Techniques.

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.

- Classify The Techniques Used To Analyze Data On One Dimension Time Series.
- Demonstrate Teamwork Skills, And Autonomy In Their Work.
- Develop Model Diagnostic Tests.
- Evaluate The Performance Of The Tentative Models.
- Identify The Patterns Of Time.
- Interpret The Forecasting Results Orally And In Writing.
- Use Graphs And Summary Statistics To Present Time Series In An Informative Way.
- Use Statistical Packages To Model, Estimate And Produce Forecasts.

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.

- Apply The Common Multivariate Statistical Methods Such As Principal Components, Factor Analysis, Discriminant Analysis, Cluster Analysis, And Multivariate Analysis Of Variance.
- Describe The Properties Of The Multivariate Normal Distribution.
- Interpret And Effectively Communicate The Results Of Multivariate Data Analysis.
- Model Multivariate Data And Select The Appropriate Method Of Analysis.
- Use Statistical Software To Analyze Multivariate Data.

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.

- Apply Goodness-Of-Fit Tests, And Logistic And Loglinear Models.
- Compute And Interpret Various Measures Of Association From Two-Way And Three-Way Contingency Tables.
- Interpret And Effectively Communicate The Results Of Categorical Data Analysis.
- Select And Fit Appropriate Models To Categorical Data And Make The Relevant Inference.
- Use Statistical Software To Analyze Categorical Data.

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.

- Analyze Single, Double And Multiple Sampling Plans.
- Construct And Apply Control Charts For Variables And Attributes.
- Define And Discuss The Role And Importance Of Statistical Quality Control In Modern Industry.
- Interpret And Communicate Control Charts For Variables And Attributes.
- Use Statistical Software To Analyze Statistical Quality Control Problems.

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.

- Apply And Execute Random Number Generation Techniques.
- Apply Data Manipulations Tools And Organize The Data To Fit The Framework Of The Required Analysis.
- Define And Apply Maximum Likelihood Estimation.
- Define And Apply Monte Carlo Simulation To Statistical Tests.
- Define And Apply Re-Sampling Techniques.
- Develop Simulation Of Data Following Certain Models.

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.

- Comply With Ethical Obligations And Responsibility To Research Subjects, Research Team Colleagues And Perform Their Work Responsibly.
- Compute Model Parameters’ Estimates, Test Hypothesis And Check Model Assumptions.
- Define And Discuss Basic Statistical Concepts And Methods.
- Explain The Integration And Interdependence Of Various Statistical Techniques.
- Identify And Formulate Problems That Can Be Solved By Statistical Techniques.
- Interpret And Communicate Results Of Statistical Analyses Orally And In A Written Format.
- Operate Individually Or In A Team And Apply Knowledge Acquired Throughout The Curriculum Using Different Statistical Software.
- Plan An Experiment; Choose The Sample Size And The Data Collection Mechanism.
- Recognize The Difference Between Statistical Techniques.
- Use Graphics And Summary Statistics To Describe Data And Apply Different Estimation And Inference Techniques And Compute And Compare Estimates.

The bridging course in statistics aims to give students with no statistical background a good knowledge of descriptive statistics and probability and probability distributions. These topics, covered in most introductory statistics courses, are a pre-requisite knowledge for the course STAT 609 (Decision Techniques and Data Analysis).

- Apply descriptive statistical techniques to describe data using statistical software.
- Define and apply the basic concepts of probability theory and statistics to real situations.
- Define and compute the common probability distributions used in modeling data.

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.

- Analyze Data Arising In Science And Environmental Sciences And Interpret The Results Of The Analysis.
- Describe The Common Data Collection And Analysis Methods.
- Select The Appropriate Statistical Model And Evaluate The Model Assumptions.
- Use Statistical Software To Explore And Analyze Data.
- Write Report On Study Findings.

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.

- Determine the appropriate statistical and decision analysis technique to help in the decision making process.
- Carry out the most common statistical analysis, like hypothesis testing, Anova and regression and time series modeling.
- Carry out decision and sensitivity analysis using decision trees.
- Apply these techniques in the modeling of business related situation.
- Carry out a model selection procedure, and relate the result to reality of the situation studied.
- Evaluate model assumptions and goodness-of-fit.

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.

- Analyse And Interpret The Results Of An Experiment.
- Describe The Principles Of Experimental Design.
- Select And Plan The Appropriate Statistical Design For An Experiment.
- Use Statistical Software In The Design And Analysis Of Experiments.

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.

- Apply A Systematic Process For Designing And Conducting An Experiment For A Given Application And Objective Of Experimentation. This Should Involve Selection Of Response Variables, The Selection And Characterization Of Factors, Levels, And Ranges, The Choice Of Experimental Design, Data Collection, And Statistical Analysis.
- Apply A Systematic Process For Designing And Conducting An Experiment For A Given Application And Objective Of Experimentation. This Should Involve Selection Of Response Variables, The Selection And Characterization Of Factors, Levels, And Ranges, The Choice Of Experimental Design, Data Collection, And Statistical Analysis
- Describe (Verbally/Written) The Basic Principles Of Experimental Design: Randomization, Replication, And Blocking
- Describe (Verbally/Written) The General Anova Procedure Including The Model For The Data, The Decomposition Of The Sum Of Squares, The Calculation Of Means Square, Hypothesis Testing And Sampling Distributions, And Model Adequacy Checking
- Describe (Verbally/Written) The Basic Principles Of Experimental Design: Randomization, Replication, And Blocking.
- Describe (Verbally/Written) The General Anova Procedure Including The Model For The Data, The Decomposition Of The Sum Of Squares, The Calculation Of Means Square, Hypothesis Testing And Sampling Distributions, And Model Adequacy Checking.
- Implement Various Statistical Analyses Involved In A Designed Experiment In A Software Package Of Choice
- Review The Various Types Of Experimental Designs Covered In Class And Describe A Situation Where The Design Is Appropriate
- Implement Various Statistical Analyses Involved In A Designed Experiment In A Software Package Of Choice.
- Review The Various Types Of Experimental Designs Covered In Class And Describe A Situation Where The Design Is Appropriate.

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.

- Apply Statistical Techniques In The Modeling Of Business Related Situation.
- Choose And Apply The Appropriate Statistical Model/Technique To Help In The Decision Making Process.
- Choose The Appropriate Statistical Technique To Help In The Decision Making Process.
- Describe And Apply The Most Common Statistical Analysis, Like Hypothesis Testing, Anova And Regression Modeling.
- Describe The Most Common Statistical Techniques In The Modeling Of Business Related Situations.
- Evaluate The Model Assumptions, And Relate The Result To Reality Of The Situation Studied.
- Interpret And Communicate The Results Of Statistical Analysis.
- Interpret And Communicate The Results Of Statistical Analysis.
- Select Appropriate Model, Evaluate Model Assumptions, And Relate The Result To Reality Of The Situation Studied.
- Use Appropriate Statistical Software To Explore And Analyze Data.
- Use Appropriate Statistical Software To Explore And Analyze 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.

- Apply These Techniques In The Modeling Of Gis Related Situation.
- Carry Out A Model Selection Procedure, And Relate The Result To Reality Of The Situation Studied.
- Carry Out The Most Common Statistical Analysis, Like Hypothesis Testing, Anova Regression Modeling, Multivariate Analysis And Analysis Of Spatial/ Temporal Data.
- Determine The Appropriate Statistical Technique To Help In The Study And Decision Making Process.
- Evaluate Model Assumptions And Goodness-Of-Fit.

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.

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