Courses

BIO 742 – Biometric Models Applied to Genetic Improvement 4(4-0) I.

(Prior course FIT 770 or ZOO 660).

Basic Statistical Principles Genotype X environment interaction. Analysis of stability and adaptability. Estimation of gain by selection. Simultaneous selection of characters. Phenotypic, genotypic and environmental correlations. Path analysis. Diallel analysis. Estimation of repeatability coefficients. Cross analysis between lineages and testers. Analysis of generations or joint scaling test. Genetic zoning. Genetic divergence. Computational resources for processing and analysis of experimental data.

BIO 746 – Biometric Models Applied to Genetic Improvement II 4(4-0) II.

(Prior course BIO 742 or ZOO 660).

Estimation of genetic parameters. Pooled analysis and data correction. Estimation of gain by selection. Study of combining ability. Correlations between characters. Analysis of stability and adaptability. Simultaneous selection of characters. Analysis of factors in breeding. Discriminant analysis in genetic improvement. Analysis of segregating lines with intercalary progenitors. Analysis of genetic diversity. Genetic and environmental progress.

EST 610 – Probability 4(4-0) I and II.

Probability fundamentals. Conditional Probability and Stochastic Independence. Random Variables. Multidimensional Random Variables. Discrete Probabilistic Models. Continuum Probabilistic Models. The Jacobian Method. Expected Value Moment-Generating Function and Characteristic Function Limits Theorems.

EST 611 – Statistical Inference 4(4-0) I and II.

Fundamentals of statistical inference. Sampling distributions. Point estimation. Properties of point estimators. Interval estimation. Hypothesis testing.

EST 613 – Bayesian Statistics 4(4-0) I and II.

Bayesian Methodology Versus Classical Methodology. Bayes’ theorem as a principle of updating information. Parametric model. Elicitation of prior distributions. Estimation theory. Analysis of some discrete models. Analysis of some continuous models. Analytical and computational techniques to implement the Bayesian paradigm. Simulation methods to sample the posterior distribution.

EST 622 – Experimental Statistics 4(4-0) I and II.

(Prior course EST 612)

Tests and assumptions for analysis of data. Classification of factors and variables. Experiments and data analysis. Alternative analysis.

EST 629 – Computational Statistics 2(0-4) I and II.

(Prior course EST 612)

Use of software for statistical analysis of experimental data. Obtaining descriptive statistics. Analysis of balanced and unbalanced linear models. Analysis of linear and nonlinear regression models.

EST 630 – Statistical Methods I 4(4-0) I.

Expected Value. Covariance. Hypothesis testing. Estimation of population parameters. Regression analysis. Identity test of regression models. Correlation analysis. Using software.

EST 631 – Statistical Methods II 4(4-0) II.

Experimental designs with emphasis on variance components. Data processing. Experiments in split plots. Factorial experiments. Confounding. Fractional repetition. Response surfaces.

EST 633 – Statistics in Quality Control 4(4-0) I.

Presenting statistical software. Introduction to experimentation. Acceptance sampling. Reliability analysis. Exploratory data analysis. Control Charts. Experiments with a factor of interest. Full and fractional factorial experiments. Response surfaces. Mixing experiments. Notions of multivariate analysis.

EST 635 – Applied Spatial Statistics 4(4-0) II.

Concepts in Probability and Inference. Spatial description. Spatial autocorrelation. Tests for spatial autocorrelation. Semivariograms and Cross-variograms. Linear prediction and Kriging. Cross-validation. Anisotropy. Block Kriging and Cokriging. Regression with spatially autocorrelated errors. Analysis of experimental designs considering spatial correlation. Spatial sampling.

EST 636 – Theory and Practice of Simulation 4(2-4) I and II.

Basic concepts in simulation. Topics in probability and inference. Software for simulation. Generation of random variables and samples. Topics and special applications of stochastic simulation in problems with plants and animals. Introduction to the Bootstrap and MCMC.

EST 637 – Time Series 4(4-0) I and II.

Time Series. Models for time series. Decomposition models: unobservable components. Auto-regression models. Box & Jenkins models.

EST 638 – Survival Analysis 4(4-0) I and II.

Basic concepts. Functions of interest. Nonparametric Methods for Survival Data Analysis Parametric Methods for Survival Data Analysis Regression models in survival analysis. Proportional Hazards Models. Aalen’s additive hazards model. Interval-censored and grouped data. Multivariate survival analysis.

EST 640 – Linear Models I 4(4-0) I.

Generalized inverse of real matrices. Systems of linear equations. Quadratic forms and distributions. Regression models or full rank models. Correlation. Models of experimental designs. Computational resources for processing and analysis of experimental data.

EST 641 – Linear Models II 4(4-0) II.

(Prior course EST 640)

Model for incomplete blocks. Designs in square lattices. Models with hierarchical classification. Models with two cross-classification criteria with equal and unequal numbers in subclasses. Analysis of covariance. Variance components. Mixed models.

EST 643 – Generalized Linear Models 4(4-0) I and II.

Fundamentals of Generalized Linear Models. Inference in Generalized Linear Models. Cerification techniques of the model. Discrete models. Continuous models.

EST 746 – Multivariate Analysis 4(4-0) II.

(Prior course EST 640)

Multinormal distributions, Wishart and Hotelling’s T2. Analysis of multivariate variance and hypothesis testing by Wilks criteria, trace of Hotelling-Lawley, Pillai trace and maximum eigenvalue of Roy. Analysis of main components. Canonical correlations. Discriminant analysis. Cluster analysis. Factor analysis. Computational resources for processing and analysis of experimental data.

EST 790 – Special Topics I 1( – ) I, II and III (Consent of the Coordinator of the Course)

Course not regularly offered, taught by visiting professors or from the institution itself, concentrated or not. Variable content, covering topics important to the overall formation of the student, not covered in the regular courses offered at UFV.

EST 791 – Special Topics II 2( – ) I, II and III (Consent of the Coordinator of the Course)

Course not regularly offered, taught by visiting professors or from the institution itself, concentrated or not. Variable content, covering topics important to the overall formation of the student, not covered in the regular courses offered at UFV.

EST 792 – Special Topics III 3( – ) I, II and III (Consent of the Coordinator of the Course)

Course not regularly offered, taught by visiting professors or from the institution itself, concentrated or not. Variable content, covering topics important to the overall formation of the student, not covered in the regular courses offered at UFV.

EST 797 – Seminar 0(1-0) I an II

Before applying to the defense of the dissertation the student must present three seminars, one free theme, another on the research project and the other on his dissertation, including the results.

EST 799 – Research ( – ) I and II

Research for the preparation of the dissertation. Course in which the student must enroll after the end of the credits.

FIT 773 – Statistical Genetics in Plant Breeding 4(4-0) I

Concepts of classical and molecular genetics. Laws of probability in genetics. Balance laws in large panmitic populations. Gene frequency. Genetic parameters. Imbalance and evolution. Functions of high order probability. Systematic crossbreeding. Estimation of the level of outcrossing in populations. Averages, variance and covariance with inbreeding. Genetic variation within and between finite populations. Fixed and random effects on plant genetics. Distribution of genetic effects on genetic designs. Artificial selection models.

Related Field Courses

BIO 642 – Genomics Statistics 4(4-0) I

Biology in genetical genomics. Introduction to genetical genomics. Genomics statistics Methods of estimation and estimators. Mapping functions. Model with a single place. Model with two places – controlled crossings. Linking groups. QTL mapping. Use of computational applications for mapping and QTL analysis.

BIO 647 – Quantitative Genetics 4(4-0) II

Continuous variation. Genotypic and breeding values. Components of the genotypic variances. Topics in estimation of variance components. Genotypic variances between and within populations structured in families. Genetic designs and estimation of genotypic variance components. Heritability. Genotypic correlation. Selection.

EDU 660 – Methodology of Higher Education 3(2-2) I and II

Introduction to sociology of the curriculum. The curriculum and profesional training. The different paradigms that guide the formation of the teacher. The teaching process: links between objectives, planning, methods and teaching and assessment techniques. Problems and alternatives for the higher education.

ENF 610 – Remote Sensing 3(2-2) II

(Prior course ENF 310, ENF 312, ENF 313 or agreement of the course coordinator)

Concept and history of remote sensing. Nature and origin sources of measured energy by remote sensing systems. Interactions between energy and matter. Data acquisition. Management and pre-processing of data. Extraction and interpretation of data. Use of data for studies of land use and agricultural and forestry managements.

ENF 612 – Introduction to Geographic Information System 3(2-2) I

Conceptualization and importance. Data models. Creating a georeferenced database. Algebraic operations. Spatial modeling. Case study.

ENF 613 – Advanced Topics in Geographic Information Systems 3(2-2) II

Prior course ENF 612 or agreement of the course coordinator)

Conceptualization and importance. Acquisition of spatial data. Mapmaking. Network models. Spatial modeling.

ENG 639 – Digital Image Processing 4 (3-2) I

Characterization of the image processing system. Image acquisition. Highlighting images. Image segmentation. Colored images processing. Image analysis.

ERU 626 – Econometrics I 3(3-0) II

Economic and econometric models. Classical linear regression model. Analysis of the assumptions of the classical model. Use of dummy variables in the regression model. Models with lagged variables. Introduction to time series analysis.

ERU 726 – Econometrics II 3(3-0) I

Basic concepts of asymptotic probability distribution. Estimation method of Maximum Likelihood (ML). Estimation method of generalized method of moments (GMM). Systems of seemingly unrelated equations (SUR): expenditure systems and plots equations (translog). Models of simultaneous equations. Models for panel data. Models with qualitative dependent variable. Models with limited dependent variable (Tobit and sample selection). Introduction to spatial econometrics. Analysis of efficiency and productivity: stochastic frontier.

EST 776 – Teaching Internship I 1(0-2) I and II (Consent of the Course Coordinator).

This course aims at providing graduate students teaching experience, trough the planning, preparation and teaching of theoretical and practical courses at undergraduate level of the Department of Statistics under the supervision and monitoring of the teacher of the respective course.

EST 777 – Teaching Internship II 2(0-4) I and II (Consent of the Course Coordinator).

This course aims at providing graduate students teaching experience, trough the planning, preparation and teaching of theoretical and practical courses at undergraduate level of the Department of Statistics under the supervision and monitoring of the teacher of the respective course.

EST 798 – Teaching Internship III 3(0-6) I and II (Consent of the Course Coordinator).

This course aims at providing graduate students teaching experience, trough the planning, preparation and teaching of theoretical and practical courses at undergraduate level of the Department of Statistics under the supervision and monitoring of the teacher of the respective course.

INF 620 – Systems of Decision Support 4(4-0) II

Systems of management support. Systems architecture of decision support. Artificial intelligence and decision support. Neural networks and decision support. Construction of decision support systems.

INF 682 – Optimization I 4(4-0) I

Modeling and optimization under linear conditions. Modeling and optimization under non-linear conditions. Modeling and optimization in networks. Multi-criteria decision making.

INF 683 – Optimization II 4(4-0) II

Economic evaluation of projects. Simulation, stock and row in production systems. Stochastic processes in production. Dynamic models of production. System dynamics.

LET 610 – English for Specific Purposes I 4(4-0) I and II

Vocabulary analysis. Study of linguistic structures. Characteristics of academic speech. Application of techniques for reading and understanding of scientific technical texts.

ZOO 760 – Mixed Models Applied to Genetic Improvement 4(4-0) I.

(Prior course EST 640, ZOO 661 or FIT 770).

Prediction methods. Genetic evaluation models. Estimation of variance components. Use of computer programs in genetic evaluation.