MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS. Biostatistics

Size: px
Start display at page:

Download "MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS. Biostatistics"

Transcription

1 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: Contingency Tables and Log Linear Models Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish Lecture BİS 510 ECTS Credit Contingency table statistics and multiway Chi- square analysis, risk analysis, correspondence statistics, Roc curve, sensitivity and reliability analysis, logistic regression. Be familiar with the statistics of contingency tables. At the end of the lecture students can analyze categorical data. Agresti, A., Categorical Data Analysis, John Willey & Sons., 1990 Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Variables in contingency tables Variables in contingency tables Relationship statistics between two or more statistics. Relationship statistics between two or more statistics. Multiway chi square statistics.

2 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Risk analysis Risk analysis Correspondence statistics Correspondence statistics Sensitivity and reliability coefficients Sensitivity and reliability coefficients Roc Curve Binary variables and logistic regression Binary variables and logistic regression

3 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 504 Computer Programming via Q BASIC and FORTRAN 90 Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 and BİS 511 Lectures ECTS Credit Flow chart, algorithm, and programming languages: QBasic, Fortran, Pascal, Oracle, SQL, Delphi and biostatistics applications. Using programming language in Biostatistics. Student can use programming language in Biostatistics. Laboratory Work x % 50 Assessment Criteria Instructors Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Basic of programming languages and differences Description of variables and constants and assign a data and using of these data. Input and output flow charts Flow charts of comparison mentality. Flow charts of loops.

4 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Moneybox mentality Using of batches Writing simple algorithms Writing complex algorithms Creating text files, saving, and reading procedures Transfer of flow chart to programming languages. Programming of simple biostatistics applications. Programming of complex biostatistics applications. Preservation of data in database

5 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 505 Simulation Techniques Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish Lectures BİS 510 and BİS 511 ECTS Credit Concept of simulation, random number generation, simulations of Uniform, Normal, Z, T, F and Chi-Square and Multivariate normal distributions. s in Fortran 90 To be able to design a simulation study n Biostatistics. The students will be able to design a simulation study n Biostatistics. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Concept of simulation Random number generation Simulation of Uniform distribution. Simulation of Normal distribution. Simulation of Z distribution.

6 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Simulation of T distribution. Simulation of F distribution Simulation of Chi-Square distribution. Sampling and Hypotheses control via simulation. Sampling and Hypotheses control via simulation. Simulation of Multivariate normal distributions. s in Fortran 90 s in Fortran 90 s in Fortran 90

7 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Using Statistical Methods And Computer Technology in Health Science. Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Turkish ECTS Credit Contents Objectives Learning Outcomes and Competences Textbook and /or References Searching literature, discussion and using computer technology via seminars. of improvements and interpretation. To join common studies with the other health science departments. Students will be able to search for article, read them and understand the statistical material methods and interpret the results. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Searching Literature Searching Literature Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars

8 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars Discussion and using computer technology via seminars of improvements and interpretation. of improvements and interpretation. of improvements and interpretation. of improvements and interpretation.

9 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 507 Seminar Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Turkish ECTS Credit Contents Discussing on recent subjects and improvement in Biostatistics Objectives Learning Outcomes and Competences Textbook and /or References Teach the students how to do a research and how to present it. The students will be able to search new subject and grasp the new subject and present it. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing

10 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing Weekly discussing

11 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 510 Research Methods in Health Science I Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Turkish ECTS Credit Contents Objectives Learning Outcomes and Competences Textbook and /or References Basic statistical concepts, types of variables, descriptive statistics, commonly used statistical distributions and their properties. Sampling distributions. Hypotheses tests for one sample, two sample (dependent and independent) groups. To be able to solve basic statistical problems and interpret the results. The students will be able to understand the advanced statistical lectures. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Basic statistical concepts. Types of variables Descriptive statistics Inquire of relationship with table and graphics

12 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Commonly used statistical distributions and their properties. Sampling Distributions Research and sampling methods. Introduction to Hypotheses Testing Hypotheses tests for one sample Hypotheses tests for two independent samples. Hypotheses tests for two dependent samples. Hypotheses tests for k dependent and independent samples. Correlations and different correlation coefficients. Simple and multiple linear regressions.

13 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 511 Research Methods in Health Science II Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 Lecture ECTS Credit Variance analysis models and assumptions, data transformations, simple variance analysis and multiple comparisons, variance analysis of random block design, Latin square design, factorial variance analysis models and covariance analysis. Describing variance analysis experimental designs. Students can choose the best design according to data sets properties. Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Laboratory Work Assessment Criteria Other Committee Exam Instructors Content Week 1 Week 2 Week 3 Week 4 Variance analysis models Assumptions of variance analysis models Data transformations Simple variance analysis and multiple comparisons, variance analysis of random block design, Latin square design, factorial variance analysis models and covariance analysis.

14 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Multiple comparisons of simple variance analysis Two-way variance analysis. Analysis of repeated measurements. Analysis of repeated measurements. Variance analysis of random block design Variance analysis of random block design Latin square design Latin square design Covariance analysis. Covariance analysis.

15 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 512 Multivariate Statistical Methods I Program name: Biostatistics Level Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Turkish 510 and 511 Lectures ECTS Credit Basic matrix knowledge, multivariate descriptive statistics, multivariate normal distribution, missing value analysis, multivariate hypotheses testing, multiple linear regression, factor analysis, correspondence analysis, path analysis. Describe the properties of multivariate statistical methods Students will be able to know how to approach multivariate statistical methods 1. Manly, BFJ. Multivariate Statistical Methods: A Primer. Chapman and Hall, Johnson RA and Wichern DW. Applied Multivariate Statistical Analysis.. Prentice-Hall Inc., Özdamar K. Paket Programlar ile İstatistiksel Veri Analizi (Çok Degiiskenli Analizler) 2. Kaan Kitabevi, Eskisehir, Alpar R. Çok Degiskenli İstatistiksel Yöntemlere Giris. Nobel Yayın Dagıtım, Ankara, Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Basic matrix knowledge Multivariate descriptive statistics

16 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Multivariate extreme values. Multivariate normal distribution and standardization. Missing value analysis Multivariate hypotheses testing Multivariate hypotheses testing Multivariate two way variance analysis, repeated measures of variance analysis. Multiple linear regression. Factor analysis Factor analysis Correspondence analysis Correspondence analysis Path analysis.

17 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 512 Multivariate Statistical Methods II Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510, BİS 511 and BİS 512 Lectures ECTS Credit Cluster, discriminant, path, multi dimensional scaling and canonic correlation analysis. Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Describe the properties of multivariate statistical methods Students will be able to know how to approach multivariate statistical methods 1. Manly, BFJ. Multivariate Statistical Methods: A Primer. Chapman and Hall, Johnson RA and Wichern DW. Applied Multivariate Statistical Analysis.. Prentice-Hall Inc., Özdamar K. Paket Programlar ile İstatistiksel Veri Analizi (Çok Degiiskenli Analizler) 2. Kaan Kitabevi, Eskisehir, Alpar R. Çok Degiskenli İstatistiksel Yöntemlere Giris. Nobel Yayın Dagıtım, Ankara, Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 ClusterAnalysis ClusterAnalysis ClusterAnalysis

18 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Discriminant Analysis Discriminant Analysis Path Analysis Multi dimensional scaling Multi dimensional scaling Canonic correlation analysis. Canonic correlation analysis. Categorical data analysis. Categorical data analysis. Using package programs Using package programs

19 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 514 Multiple Comparisons Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Parametric and nonparametric multiple comparison methods. Objectives Learning Outcomes and Competences Textbook and /or References Description of parametric and nonparametric multiple comparison methods. The Student will be able to choose proper multiple comparison methods. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Introduction Experimantalwise and Comparisonwise errors. Linear and orthogonal contrasts. Fisher s LSD Tukey s multiple comparison method

20 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Using package programs Student Newman Keuls Procedure Duncan s Multiple Rank Test Using package programs Scheffe s multiple comparison method Bonferroni multiple comparison method Using package programs Nonparametric multiple comparison methods Nonparametric multiple comparison methods

21 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 515 Repeated Measurements Design Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Description of repeated measurements, assumptions, paired sample t test, simple and advanced repeated measurement designs. Objectives Learning Outcomes and Competences Textbook and /or References Describe the properties of repeated measurements design. Students will be able to know how to approach repeated measurements statistical methods Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Advantages and disadvantages of repeated measurement designs Paired sample t test Repeated measurement designs with one factor Using package programs Repeated measurement designs with two factors and one of them is repeated.

22 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Repeated measurement designs with two factors and two of them are repeated. Using package programs Repeated measurement designs with three factors and one of them is repeated. Using package programs Repeated measurement designs with three factors and two of them is repeated. Repeated measurement designs with three factors and three of them are repeated. Using package programs Covariance analysis for repeated measurement designs Using package programs

23 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 516 Linear Regression Models Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of linear regression models, simple regression models and hypothese controls of model coefficients and variable selection methods and regression diagnostics. Assumption controls of simple and multiple regression analysis, extreme and influence observations. Students will be able to know how to approach linear regression models. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Using vector notations Simple Linear Regression Estimation of simple linear regression coefficients via least square estimation. Statistical significance of simple regression coefficients Using package programs

24 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Multiple linear regression Estimation of multiple linear regression coefficients via least square estimation. Using package programs Regression Diagnostics Using package programs Variable selection methods for multiple linear regression. Using package programs Path Analysis Using package programs

25 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 519 Sampling Methods Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of sampling methods, selection of proper sample size, relationship between power and sample size. Objectives Learning Outcomes and Competences Textbook and /or References Decide best the sampling method and calculate the sample size. Students will be able to know how to choose right sampling method and calculate sample size. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Introduction to sampling theory. Estimators and their properties. Sampling distributions and standard error. Point and interval estimation Sample size calculation

26 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Simple random sampling I Simple random sampling II Stratified sampling I Stratified sampling II Systematic Sampling One Step Cluster Sampling Multiple Step Cluster Sampling Weighting of Sampling Non probabilistic Sampling

27 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 517 Special Methods for Health Statistics Level Program name: Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures ECTS Credit Health statistics, sensitivity, specifity, Roc Curve, relative risk, and odds ratio, Logistic Regression, Survival and Meta Analysis. Objectives Learning Outcomes and Competences Textbook and /or References Description of statistics used for Health Science The Student will be able to choose proper Health statistics for Population. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Health services for Biostatistics Definition of service region Demographic statistics Childbirth statistical methods for speed and rate Statistical methods for diseases

28 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Standardization methods for speed and rate Life tables Statistical methods for preventive studies Statistics for hospital assessment Statistical methods for Dental Health International classification of disease and mortality. Criteria for health levels Health Records

29 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 518 Nonparametric Statistics Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of nonparametric data, descriptive statistics for nonparametric tests, Median test, multiple comparison methods, relationship statistics. To be grasped assumptions and manual calculation of nonparametric statistics. Students can decide the best nonparametric statistics for data sets. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Definition of concepts, types of the scales Chi-Square and Fisher tests One sample Kolmogorov-Smirnov test Using package program Wilcoxen rank test, dependent sample Wilcoxen rank test

30 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Mann-Whitney U test Using package program Rank test and Cochrane q test Kruskal Wallis test Using package program Friedman test Rank korelation test Kendall Tau coefficients Using package program

31 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 520 Nonlinear Regression Analysis Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Description of nonlinear regression, building up nonlinear models and analysis, growing curves, dose-response models, and using models. Description of nonlinear regression, building up nonlinear models and analysis. Students can built up nonlinear models and interpret the results. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Nonlinear regression models Building up Nonlinear regression models Analysis of Nonlinear regression models Using package program Growing curves

32 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Growing curves Using package program 2 2 and 2 3 experimental design Using package program 2 4 Fractional Factorial experimental design Central composite design Non Central composite design Box Behnken design Using package program

33 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 535 Theoretical Distributions I Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures Properties of Discrete and Continuous Data ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Teaching students properties of discrete and continuous data Students will know properties of discrete and continuous data Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Mass Combination and permutation Probability Theory Probability Theory Random variable

34 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 One dimension Random variable Expected values and moments Conditional probability, independent events Bayesian Theory Discrete and continuous random variable Distribution functions, conditional probability and distribution functions.

35 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 535 Theoretical Distributions II Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures Properties of Discrete and Continuous Data ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Teaching students properties of discrete and continuous data Students will known properties of discrete and continuous data Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Expected value Moments Bernoulli and Binom distributions

36 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Poisson distribution Geometric and negative binomial distributions Hypergeometric Distribution Characteristic functions Moment functions Conditional expected value, conditional variance

37 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 522 Special Experimental Design Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 an BİS 511 Lectures ECTS Credit Latin square models, models include confounded factors, splitted parcels experimental designs Objectives Learning Outcomes and Competences Textbook and /or References Special experimental designs Students can decide the best experimental design. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Random experimental design Random block design Random block design

38 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Latin Square experimental design Latin Square experimental design Factorial experiments Factorial experiments Combination of factorial experiments and block design Combination of factorial experiments and block design

39 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 523 Bayesian Methods Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 an BİS 511 Lectures ECTS Credit Bayes Theory, bayes methods and usage fields, sampling and grouping via bayes. of biasian technique in clinical experiments. Description and usage of Bayesian concept. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Bayes Theory Bayes Theory Usage of Bayesian methods Usage of Bayesian methods

40 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Sampling and grouping via bayes Sampling and grouping via bayes of biasian technique in clinical experiments. of biasian technique in clinical experiments. of biasian technique in clinical experiments. of biasian technique in clinical experiments.

41 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 524 Population Genetics Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 and BİS 511 Lectures ECTS Credit Gene frequencies, population balance controls, statistical analysis for dependent and independent genes, gene differences between populations. Preparing student for introduction to statistical analysis. Students will be able to analyze genetic data and interpret results. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Gene frequencies Population balance controls Statistical analysis for dependent genes

42 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Statistical analysis for independent genes Gene differences between populations. Gene differences coefficients Gene flow Clustering Analysis

43 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 525 Survey Studies Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures Survey methods, preparation of questionnaire ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Teaching survey methods, preparation of questionnaire Student can prepare a survey study and questionnaire. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Survey methods Preparation of Questionnaire. Scaling

44 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Item Analysis Reliability and Validity analysis. Multivariate variance analysis. Factor analysis and survey Factor analysis and survey

45 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 526 Survival Analysis Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Survival analysis, life table, Kaplan Meier and Cox regression, comparison of survival curves Objectives Learning Outcomes and Competences Textbook and /or References Basic knowledge of life data analysis. Student can analyze life data. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Survival time, case and censoring types Estimation methods of Survival time, Kaplan Meier Method Estimation methods of Survival time, Life Table Comparison of survival times

46 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Comparison of survival times Hazard regression Cox regression Variable selection Assumptions

47 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 527 Quality Control Methods Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Turkish BİS 510 and BİS 511 Lectures ECTS Credit Quality Concept in Statistics, Importance of confidence interval in Quality Control, Process Control (SPC), Histogram, Pareto graphs, Fish Bone, Control graphs, Definition of control limits. Teach how to use statistical methods in Quality Control field. Using of statistics in Quality Control field. Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Quality Concept in Statistics Importance of confidence interval in Quality Control Process Control (SPC) Histogram

48 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Pareto graphs Fish Bone Control graphs Definition of control limits

49 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 531 Experimental Design Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures Clinical experimental designs ECTS Credit Objectives Learning Outcomes and Competences Textbook and /or References Assessment Criteria Instructors Designation of clinical experiments Student can design a clinical experiment Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Design and classification of clinical trial Case series Case control studies Cross Sectional Cohort Studies Comparison of Case control and Cohort Studies

50 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Experimental clinical and designs Trials with independent concurrent control Trial with self controls Trial with external controls Uncontrolled studies Random simple experimental design Trials wit Covariates Trial with two and more factors

51 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: BİS 532 Generalized Estimated Equations Level Program name: Biostatistics Master of Sci. Hours/week Ther. Recite. Lab. Others Total MEU Credit ECTS Credit And credit Language Compulsory / Prerequisites Turkish BİS 510 and BİS 511 Lectures Contents Objectives Learning Outcomes and Competences Textbook and /or References Laboratory Work x % 50 Assessment Criteria Other x % 50 Committee Exam Instructors Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Linear models in statistics Week 2 Design matrix Week 3 Quadratic form Week 4 Component of sum of squares Week 5

52 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Relationship between Regression and variance analysis Contrast comparisons Orthogonal comparisons Response surface Balanced blocks Missing blocks

53 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 533 Computer Programming Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Turkish BİS 510 and BİS 511 Lectures ECTS Credit Flow chart, algorithm, and programming languages: QBasic, Fortran, Pascal, Oracle, SQL, Delphi and biostatistics applications. Objectives Learning Outcomes and Competences Textbook and /or References Using programming language in Biostatistics. Student can use programming language in Biostatistics. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Basic of programming languages and differences Description of variables and constants and assign a data and using of these data. Input and output flow charts Flow charts of comparison mentality. Flow charts of loops.

54 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Moneybox mentality Using of batches Writing simple algorithms Writing complex algorithms Creating text files, saving, and reading procedures Transfer of flow chart to programming languages. In Structural questioning language, Structural questioning sentences In Structural questioning language, writing and deletion sentences Integration of Structural questioning languages via interfaces

55 MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: BİS 534 Matrix and Vectors Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci. MEU Credit And credit Language Compulsory / Prerequisites Contents Objectives Learning Outcomes and Competences Textbook and /or References Turkish BİS 510 and BİS 511 Lectures ECTS Credit Vector and matrix procedures, descriptions, transpose determination, inverse procedures and Using software for these procedures. Calculation matrix procedures Students can calculate matrix procedures by using software. Assessment Criteria Instructors Laboratory Work x % 50 Other x % 50 Committee Exam Doç. Dr. Arzu Kanık - Yrd. Doç. Dr. Bahar Taşdelen arzukanik@mersin.edu.tr bahartasdelen@mersin.edu.tr Content Week 1 Week 2 Week 3 Week 4 Week 5 Definition of Matrix concept Types of matrixes Summation, extraction and multiplication procedures for matrixes. Determinant Minor, cofactor and ad joint matrixes

56 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Inverse of a matrix Using package programs Orthogonal matrix Division of a matrix Linear dependency and independency concepts Rank concept and nonsingular the biggest sub matrix Linear equation systems Eigen values and eigen vectors Using package programs

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics. Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

More information

Statistics Graduate Courses

Statistics Graduate Courses Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.

More information

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics. Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing

More information

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This

More information

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in

More information

STAT 360 Probability and Statistics. Fall 2012

STAT 360 Probability and Statistics. Fall 2012 STAT 360 Probability and Statistics Fall 2012 1) General information: Crosslisted course offered as STAT 360, MATH 360 Semester: Fall 2012, Aug 20--Dec 07 Course name: Probability and Statistics Number

More information

Name of the module: Multivariate biostatistics and SPSS Number of module: 471-8-4081

Name of the module: Multivariate biostatistics and SPSS Number of module: 471-8-4081 Name of the module: Multivariate biostatistics and SPSS Number of module: 471-8-4081 BGU Credits: 1.5 ECTS credits: Academic year: 4 th Semester: 15 days during fall semester Hours of instruction: 8:00-17:00

More information

How To Understand The Theory Of Probability

How To Understand The Theory Of Probability Graduate Programs in Statistics Course Titles STAT 100 CALCULUS AND MATR IX ALGEBRA FOR STATISTICS. Differential and integral calculus; infinite series; matrix algebra STAT 195 INTRODUCTION TO MATHEMATICAL

More information

UNDERGRADUATE DEGREE DETAILS : BACHELOR OF SCIENCE WITH

UNDERGRADUATE DEGREE DETAILS : BACHELOR OF SCIENCE WITH QATAR UNIVERSITY COLLEGE OF ARTS & SCIENCES Department of Mathematics, Statistics, & Physics UNDERGRADUATE DEGREE DETAILS : Program Requirements and Descriptions BACHELOR OF SCIENCE WITH A MAJOR IN STATISTICS

More information

STATISTICS COURSES UNDERGRADUATE CERTIFICATE FACULTY. Explanation of Course Numbers. Bachelor's program. Master's programs.

STATISTICS COURSES UNDERGRADUATE CERTIFICATE FACULTY. Explanation of Course Numbers. Bachelor's program. Master's programs. STATISTICS Statistics is one of the natural, mathematical, and biomedical sciences programs in the Columbian College of Arts and Sciences. The curriculum emphasizes the important role of statistics as

More information

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012 Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts

More information

240ST014 - Data Analysis of Transport and Logistics

240ST014 - Data Analysis of Transport and Logistics Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2015 240 - ETSEIB - Barcelona School of Industrial Engineering 715 - EIO - Department of Statistics and Operations Research MASTER'S

More information

LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE

LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-

More information

200627 - AC - Clinical Trials

200627 - AC - Clinical Trials Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2014 200 - FME - School of Mathematics and Statistics 715 - EIO - Department of Statistics and Operations Research MASTER'S DEGREE

More information

STATISTICA Formula Guide: Logistic Regression. Table of Contents

STATISTICA Formula Guide: Logistic Regression. Table of Contents : Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

More information

Teaching Biostatistics to Postgraduate Students in Public Health

Teaching Biostatistics to Postgraduate Students in Public Health Teaching Biostatistics to Postgraduate Students in Public Health Peter A Lachenbruch - h s hgeles, California, USA 1. Introduction This paper describes how biostatistics is taught in US Schools of Public

More information

business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar

business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel

More information

Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)

Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Cairo University Faculty of Economics and Political Science Statistics Department English Section Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Prepared

More information

Description. Textbook. Grading. Objective

Description. Textbook. Grading. Objective EC151.02 Statistics for Business and Economics (MWF 8:00-8:50) Instructor: Chiu Yu Ko Office: 462D, 21 Campenalla Way Phone: 2-6093 Email: kocb@bc.edu Office Hours: by appointment Description This course

More information

430 Statistics and Financial Mathematics for Business

430 Statistics and Financial Mathematics for Business Prescription: 430 Statistics and Financial Mathematics for Business Elective prescription Level 4 Credit 20 Version 2 Aim Students will be able to summarise, analyse, interpret and present data, make predictions

More information

PROBABILITY AND STATISTICS. Ma 527. 1. To teach a knowledge of combinatorial reasoning.

PROBABILITY AND STATISTICS. Ma 527. 1. To teach a knowledge of combinatorial reasoning. PROBABILITY AND STATISTICS Ma 527 Course Description Prefaced by a study of the foundations of probability and statistics, this course is an extension of the elements of probability and statistics introduced

More information

MATH BOOK OF PROBLEMS SERIES. New from Pearson Custom Publishing!

MATH BOOK OF PROBLEMS SERIES. New from Pearson Custom Publishing! MATH BOOK OF PROBLEMS SERIES New from Pearson Custom Publishing! The Math Book of Problems Series is a database of math problems for the following courses: Pre-algebra Algebra Pre-calculus Calculus Statistics

More information

Quantitative Methods for Finance

Quantitative Methods for Finance Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain

More information

BIOM611 Biological Data Analysis

BIOM611 Biological Data Analysis BIOM611 Biological Data Analysis Spring, 2015 Tentative Syllabus Introduction BIOMED611 is a ½ unit course required for all 1 st year BGS students (except GCB students). It will provide an introduction

More information

QUALITY ENGINEERING PROGRAM

QUALITY ENGINEERING PROGRAM QUALITY ENGINEERING PROGRAM Production engineering deals with the practical engineering problems that occur in manufacturing planning, manufacturing processes and in the integration of the facilities and

More information

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different

More information

Master programme in Statistics

Master programme in Statistics Master programme in Statistics Björn Holmquist 1 1 Department of Statistics Lund University Cramérsällskapets årskonferens, 2010-03-25 Master programme Vad är ett Master programme? Breddmaster vs Djupmaster

More information

MATHEMATICAL METHODS OF STATISTICS

MATHEMATICAL METHODS OF STATISTICS MATHEMATICAL METHODS OF STATISTICS By HARALD CRAMER TROFESSOK IN THE UNIVERSITY OF STOCKHOLM Princeton PRINCETON UNIVERSITY PRESS 1946 TABLE OF CONTENTS. First Part. MATHEMATICAL INTRODUCTION. CHAPTERS

More information

COURSE PLAN BDA: Biomedical Data Analysis Master in Bioinformatics for Health Sciences. 2015-2016 Academic Year Qualification.

COURSE PLAN BDA: Biomedical Data Analysis Master in Bioinformatics for Health Sciences. 2015-2016 Academic Year Qualification. COURSE PLAN BDA: Biomedical Data Analysis Master in Bioinformatics for Health Sciences 2015-2016 Academic Year Qualification. Master's Degree 1. Description of the subject Subject name: Biomedical Data

More information

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application

More information

Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program

Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program Department of Mathematics and Statistics Degree Level Expectations, Learning Outcomes, Indicators of

More information

How To Understand And Solve A Linear Programming Problem

How To Understand And Solve A Linear Programming Problem At the end of the lesson, you should be able to: Chapter 2: Systems of Linear Equations and Matrices: 2.1: Solutions of Linear Systems by the Echelon Method Define linear systems, unique solution, inconsistent,

More information

Curriculum for to the PhD Program in Pharmacy Administration

Curriculum for to the PhD Program in Pharmacy Administration Curriculum for to the PhD Program in Pharmacy Administration Course Hours Course Title BSE 5113 3 Principles of Epidemiology BSE 5163 3 Biostatistics Methods I* BSE 5173 3 Biostatistics Methods II* BSE

More information

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 COURSE: POM 500 Statistical Analysis, ONLINE EDITION, Fall 2010 Prerequisite: Finite Math

More information

Least Squares Estimation

Least Squares Estimation Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David

More information

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not. Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

More information

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm

More information

Biostatistics: Types of Data Analysis

Biostatistics: Types of Data Analysis Biostatistics: Types of Data Analysis Theresa A Scott, MS Vanderbilt University Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott Theresa A Scott, MS

More information

SPSS Modules Features Statistics Premium

SPSS Modules Features Statistics Premium SPSS Modules Features Statistics Premium Core System Functionality (included in every license) Data access and management Data Prep features: Define Variable properties tool; copy data properties tool,

More information

PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050

PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050 PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050 Class Hours: 2.0 Credit Hours: 3.0 Laboratory Hours: 2.0 Date Revised: Fall 2013 Catalog Course Description: Descriptive

More information

Module 223 Major A: Concepts, methods and design in Epidemiology

Module 223 Major A: Concepts, methods and design in Epidemiology Module 223 Major A: Concepts, methods and design in Epidemiology Module : 223 UE coordinator Concepts, methods and design in Epidemiology Dates December 15 th to 19 th, 2014 Credits/ECTS UE description

More information

Organizing Your Approach to a Data Analysis

Organizing Your Approach to a Data Analysis Biost/Stat 578 B: Data Analysis Emerson, September 29, 2003 Handout #1 Organizing Your Approach to a Data Analysis The general theme should be to maximize thinking about the data analysis and to minimize

More information

Economic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007)

Economic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007) COURSE DESCRIPTION Title Code Level Semester Credits 3 Prerequisites Post requisites Introduction to Statistics ECON1005 (EC160) I I None Economic Statistics (ECON2006), Statistics and Research Design

More information

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19 PREFACE xi 1 INTRODUCTION 1 1.1 Overview 1 1.2 Definition 1 1.3 Preparation 2 1.3.1 Overview 2 1.3.2 Accessing Tabular Data 3 1.3.3 Accessing Unstructured Data 3 1.3.4 Understanding the Variables and Observations

More information

Mathematics within the Psychology Curriculum

Mathematics within the Psychology Curriculum Mathematics within the Psychology Curriculum Statistical Theory and Data Handling Statistical theory and data handling as studied on the GCSE Mathematics syllabus You may have learnt about statistics and

More information

STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

More information

200609 - ATV - Lifetime Data Analysis

200609 - ATV - Lifetime Data Analysis Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2015 200 - FME - School of Mathematics and Statistics 715 - EIO - Department of Statistics and Operations Research 1004 - UB - (ENG)Universitat

More information

Logistic Regression (1/24/13)

Logistic Regression (1/24/13) STA63/CBB540: Statistical methods in computational biology Logistic Regression (/24/3) Lecturer: Barbara Engelhardt Scribe: Dinesh Manandhar Introduction Logistic regression is model for regression used

More information

Regression Modeling Strategies

Regression Modeling Strategies Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions

More information

Data analysis process

Data analysis process Data analysis process Data collection and preparation Collect data Prepare codebook Set up structure of data Enter data Screen data for errors Exploration of data Descriptive Statistics Graphs Analysis

More information

Programme du parcours Clinical Epidemiology 2014-2015. UMR 1. Methods in therapeutic evaluation A Dechartres/A Flahault

Programme du parcours Clinical Epidemiology 2014-2015. UMR 1. Methods in therapeutic evaluation A Dechartres/A Flahault Programme du parcours Clinical Epidemiology 2014-2015 UR 1. ethods in therapeutic evaluation A /A Date cours Horaires 15/10/2014 14-17h General principal of therapeutic evaluation (1) 22/10/2014 14-17h

More information

AP Statistics: Syllabus 1

AP Statistics: Syllabus 1 AP Statistics: Syllabus 1 Scoring Components SC1 The course provides instruction in exploring data. 4 SC2 The course provides instruction in sampling. 5 SC3 The course provides instruction in experimentation.

More information

CBE 9190B ADVANCED STATISTICAL PROCESS ANALYSIS COURSE OUTLINE 2014 2015

CBE 9190B ADVANCED STATISTICAL PROCESS ANALYSIS COURSE OUTLINE 2014 2015 CBE 9190B ADVANCED STATISTICAL PROCESS ANALYSIS COURSE OUTLINE 2014 2015 Description This course is for engineers involved with experimental investigation and interpretation of data. Basic, applied statistical

More information

Precalculus REVERSE CORRELATION. Content Expectations for. Precalculus. Michigan CONTENT EXPECTATIONS FOR PRECALCULUS CHAPTER/LESSON TITLES

Precalculus REVERSE CORRELATION. Content Expectations for. Precalculus. Michigan CONTENT EXPECTATIONS FOR PRECALCULUS CHAPTER/LESSON TITLES Content Expectations for Precalculus Michigan Precalculus 2011 REVERSE CORRELATION CHAPTER/LESSON TITLES Chapter 0 Preparing for Precalculus 0-1 Sets There are no state-mandated Precalculus 0-2 Operations

More information

SAS Software to Fit the Generalized Linear Model

SAS Software to Fit the Generalized Linear Model SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling

More information

VI. Introduction to Logistic Regression

VI. Introduction to Logistic Regression VI. Introduction to Logistic Regression We turn our attention now to the topic of modeling a categorical outcome as a function of (possibly) several factors. The framework of generalized linear models

More information

Statistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013

Statistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013 Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.1-1.6) Objectives

More information

Study Design and Statistical Analysis

Study Design and Statistical Analysis Study Design and Statistical Analysis Anny H Xiang, PhD Department of Preventive Medicine University of Southern California Outline Designing Clinical Research Studies Statistical Data Analysis Designing

More information

Come scegliere un test statistico

Come scegliere un test statistico Come scegliere un test statistico Estratto dal Capitolo 37 of Intuitive Biostatistics (ISBN 0-19-508607-4) by Harvey Motulsky. Copyright 1995 by Oxfd University Press Inc. (disponibile in Iinternet) Table

More information

200628 - DAIC - Advanced Experimental Design in Clinical Research

200628 - DAIC - Advanced Experimental Design in Clinical Research Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2015 200 - FME - School of Mathematics and Statistics 1004 - UB - (ENG)Universitat de Barcelona MASTER'S DEGREE IN STATISTICS AND

More information

Fairfield Public Schools

Fairfield Public Schools Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity

More information

Interpretation of Somers D under four simple models

Interpretation of Somers D under four simple models Interpretation of Somers D under four simple models Roger B. Newson 03 September, 04 Introduction Somers D is an ordinal measure of association introduced by Somers (96)[9]. It can be defined in terms

More information

Exploratory Data Analysis with MATLAB

Exploratory Data Analysis with MATLAB Computer Science and Data Analysis Series Exploratory Data Analysis with MATLAB Second Edition Wendy L Martinez Angel R. Martinez Jeffrey L. Solka ( r ec) CRC Press VV J Taylor & Francis Group Boca Raton

More information

Multivariate Statistical Inference and Applications

Multivariate Statistical Inference and Applications Multivariate Statistical Inference and Applications ALVIN C. RENCHER Department of Statistics Brigham Young University A Wiley-Interscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim

More information

Why Is EngineRoom the Right Choice? 1. Cuts the Cost of Calculation

Why Is EngineRoom the Right Choice? 1. Cuts the Cost of Calculation What is EngineRoom? - A Web based data analysis application with an intuitive, drag-and-drop graphical interface. - A suite of powerful, simple-to-use Lean and Six Sigma data analysis tools that you can

More information

Linda K. Muthén Bengt Muthén. Copyright 2008 Muthén & Muthén www.statmodel.com. Table Of Contents

Linda K. Muthén Bengt Muthén. Copyright 2008 Muthén & Muthén www.statmodel.com. Table Of Contents Mplus Short Courses Topic 2 Regression Analysis, Eploratory Factor Analysis, Confirmatory Factor Analysis, And Structural Equation Modeling For Categorical, Censored, And Count Outcomes Linda K. Muthén

More information

How To Understand Multivariate Models

How To Understand Multivariate Models Neil H. Timm Applied Multivariate Analysis With 42 Figures Springer Contents Preface Acknowledgments List of Tables List of Figures vii ix xix xxiii 1 Introduction 1 1.1 Overview 1 1.2 Multivariate Models

More information

Diablo Valley College Catalog 2014-2015

Diablo Valley College Catalog 2014-2015 Mathematics MATH Michael Norris, Interim Dean Math and Computer Science Division Math Building, Room 267 Possible career opportunities Mathematicians work in a variety of fields, among them statistics,

More information

2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering

2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering 2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering Compulsory Courses IENG540 Optimization Models and Algorithms In the course important deterministic optimization

More information

Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini

Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building

More information

MASTER COURSE SYLLABUS-PROTOTYPE PSYCHOLOGY 2317 STATISTICAL METHODS FOR THE BEHAVIORAL SCIENCES

MASTER COURSE SYLLABUS-PROTOTYPE PSYCHOLOGY 2317 STATISTICAL METHODS FOR THE BEHAVIORAL SCIENCES MASTER COURSE SYLLABUS-PROTOTYPE THE PSYCHOLOGY DEPARTMENT VALUES ACADEMIC FREEDOM AND THUS OFFERS THIS MASTER SYLLABUS-PROTOTYPE ONLY AS A GUIDE. THE INSTRUCTORS ARE FREE TO ADAPT THEIR COURSE SYLLABI

More information

Mathematics (MAT) MAT 061 Basic Euclidean Geometry 3 Hours. MAT 051 Pre-Algebra 4 Hours

Mathematics (MAT) MAT 061 Basic Euclidean Geometry 3 Hours. MAT 051 Pre-Algebra 4 Hours MAT 051 Pre-Algebra Mathematics (MAT) MAT 051 is designed as a review of the basic operations of arithmetic and an introduction to algebra. The student must earn a grade of C or in order to enroll in MAT

More information

Predictive Modeling Techniques in Insurance

Predictive Modeling Techniques in Insurance Predictive Modeling Techniques in Insurance Tuesday May 5, 2015 JF. Breton Application Engineer 2014 The MathWorks, Inc. 1 Opening Presenter: JF. Breton: 13 years of experience in predictive analytics

More information

Curriculum - Doctor of Philosophy

Curriculum - Doctor of Philosophy Curriculum - Doctor of Philosophy CORE COURSES Pharm 545-546.Pharmacoeconomics, Healthcare Systems Review. (3, 3) Exploration of the cultural foundations of pharmacy. Development of the present state of

More information

Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015

Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015 1 Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015 Instructor: Joanne M. Garrett, PhD e-mail: joanne_garrett@med.unc.edu Class Notes: Copies of the class lecture slides

More information

Data Analysis, Research Study Design and the IRB

Data Analysis, Research Study Design and the IRB Minding the p-values p and Quartiles: Data Analysis, Research Study Design and the IRB Don Allensworth-Davies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB

More information

School of Public Health and Health Services Department of Epidemiology and Biostatistics

School of Public Health and Health Services Department of Epidemiology and Biostatistics School of Public Health and Health Services Department of Epidemiology and Biostatistics Master of Public Health and Graduate Certificate Biostatistics 0-04 Note: All curriculum revisions will be updated

More information

Imputing Values to Missing Data

Imputing Values to Missing Data Imputing Values to Missing Data In federated data, between 30%-70% of the data points will have at least one missing attribute - data wastage if we ignore all records with a missing value Remaining data

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

More information

Directions for using SPSS

Directions for using SPSS Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...

More information

UNIVERSITY OF NAIROBI

UNIVERSITY OF NAIROBI UNIVERSITY OF NAIROBI MASTERS IN PROJECT PLANNING AND MANAGEMENT NAME: SARU CAROLYNN ELIZABETH REGISTRATION NO: L50/61646/2013 COURSE CODE: LDP 603 COURSE TITLE: RESEARCH METHODS LECTURER: GAKUU CHRISTOPHER

More information

Graduate Certificate in Systems Engineering

Graduate Certificate in Systems Engineering Graduate Certificate in Systems Engineering Systems Engineering is a multi-disciplinary field that aims at integrating the engineering and management functions in the development and creation of a product,

More information

67 204 Mathematics for Business Analysis I Fall 2007

67 204 Mathematics for Business Analysis I Fall 2007 67 204 Mathematics for Business Analysis I Fall 2007 Instructor Asõkā Rāmanāyake Office: Swart 223 Office Hours: Monday 12:40 1:40 Wednesday 8:00 9:00 Thursday 9:10 11:20 If you cannot make my office hours,

More information

Computer-Aided Multivariate Analysis

Computer-Aided Multivariate Analysis Computer-Aided Multivariate Analysis FOURTH EDITION Abdelmonem Af if i Virginia A. Clark and Susanne May CHAPMAN & HALL/CRC A CRC Press Company Boca Raton London New York Washington, D.C Contents Preface

More information

A Prototype System for Educational Data Warehousing and Mining 1

A Prototype System for Educational Data Warehousing and Mining 1 A Prototype System for Educational Data Warehousing and Mining 1 Nikolaos Dimokas, Nikolaos Mittas, Alexandros Nanopoulos, Lefteris Angelis Department of Informatics, Aristotle University of Thessaloniki

More information

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and

More information

Methods for Meta-analysis in Medical Research

Methods for Meta-analysis in Medical Research Methods for Meta-analysis in Medical Research Alex J. Sutton University of Leicester, UK Keith R. Abrams University of Leicester, UK David R. Jones University of Leicester, UK Trevor A. Sheldon University

More information

Adequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection

Adequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection Directions in Statistical Methodology for Multivariable Predictive Modeling Frank E Harrell Jr University of Virginia Seattle WA 19May98 Overview of Modeling Process Model selection Regression shape Diagnostics

More information

Principle Component Analysis and Partial Least Squares: Two Dimension Reduction Techniques for Regression

Principle Component Analysis and Partial Least Squares: Two Dimension Reduction Techniques for Regression Principle Component Analysis and Partial Least Squares: Two Dimension Reduction Techniques for Regression Saikat Maitra and Jun Yan Abstract: Dimension reduction is one of the major tasks for multivariate

More information

THE UNIVERSITY OF TEXAS AT TYLER COLLEGE OF NURSING COURSE SYLLABUS NURS 5317 STATISTICS FOR HEALTH PROVIDERS. Fall 2013

THE UNIVERSITY OF TEXAS AT TYLER COLLEGE OF NURSING COURSE SYLLABUS NURS 5317 STATISTICS FOR HEALTH PROVIDERS. Fall 2013 THE UNIVERSITY OF TEXAS AT TYLER COLLEGE OF NURSING 1 COURSE SYLLABUS NURS 5317 STATISTICS FOR HEALTH PROVIDERS Fall 2013 & Danice B. Greer, Ph.D., RN, BC dgreer@uttyler.edu Office BRB 1115 (903) 565-5766

More information

training programme in pharmaceutical medicine Clinical Data Management and Analysis

training programme in pharmaceutical medicine Clinical Data Management and Analysis training programme in pharmaceutical medicine Clinical Data Management and Analysis 19-21 may 2011 Clinical Data Management and Analysis 19 21 MAY 2011 LocaL: University of Aveiro, Campus Universitário

More information

List of Examples. Examples 319

List of Examples. Examples 319 Examples 319 List of Examples DiMaggio and Mantle. 6 Weed seeds. 6, 23, 37, 38 Vole reproduction. 7, 24, 37 Wooly bear caterpillar cocoons. 7 Homophone confusion and Alzheimer s disease. 8 Gear tooth strength.

More information

Server Load Prediction

Server Load Prediction Server Load Prediction Suthee Chaidaroon (unsuthee@stanford.edu) Joon Yeong Kim (kim64@stanford.edu) Jonghan Seo (jonghan@stanford.edu) Abstract Estimating server load average is one of the methods that

More information

Alabama Department of Postsecondary Education

Alabama Department of Postsecondary Education Date Adopted 1998 Dates reviewed 2007, 2011, 2013 Dates revised 2004, 2008, 2011, 2013, 2015 Alabama Department of Postsecondary Education Representing Alabama s Public Two-Year College System Jefferson

More information

Course Agenda. First Day. 4 th February - Monday 14.30-19.00. 14:30-15.30 Students Registration Polo Didattico Laterino

Course Agenda. First Day. 4 th February - Monday 14.30-19.00. 14:30-15.30 Students Registration Polo Didattico Laterino Course Agenda First Day 4 th February - Monday 14.30-19.00 14:30-15.30 Students Registration Main Entrance Registration Desk 15.30-17.00 Opening Works Teacher presentation Brief Students presentation Course

More information

Comparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3)

Comparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3) Comparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3) What is EngineRoom? A Microsoft Excel add in A suite of powerful, simple to use Lean and Six Sigma data analysis tools Built for

More information

03 The full syllabus. 03 The full syllabus continued. For more information visit www.cimaglobal.com PAPER C03 FUNDAMENTALS OF BUSINESS MATHEMATICS

03 The full syllabus. 03 The full syllabus continued. For more information visit www.cimaglobal.com PAPER C03 FUNDAMENTALS OF BUSINESS MATHEMATICS 0 The full syllabus 0 The full syllabus continued PAPER C0 FUNDAMENTALS OF BUSINESS MATHEMATICS Syllabus overview This paper primarily deals with the tools and techniques to understand the mathematics

More information

Learning outcomes. Knowledge and understanding. Competence and skills

Learning outcomes. Knowledge and understanding. Competence and skills Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

More information