catalogue of courses_statisticsexisting

7/29/2019 Catalogue of Courses_statisticsexisting

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Republic of the PhilippinesCARAGA STATE UNIVERSITY

P.O. Box 165, Ampayon, Butuan CityCARAGA Administrative Region XIII

Tel. No (085) 3423047 Fax No. (085) 3421079

DEPARTMENT OF MATHEMATICS

SUMMARY OF COURSE OFFERING

EXISTING

A. GENERAL EDUCATION SUBJECTS

Course No. Credit Course Title Prerequisites Course Description

Lec Lab Total

Stat 1 2 1 3 Elementary

Statistics

Descriptive Statistics,; Probability Theory;

Probability Distributions; Inferential

Statistics; and Regression analysis.

Stat 1.1 3 0 3 Statistics for

Teachers

Topics here include frequency

distributions of empirical data, calculations

of descriptive statistics and review ofcommonly occurring distributions

( binomial, Normal, etc.) needed for

understanding basic ideas of statisticalinference. It introduces central limit

theories, estimation and hypothesis testing.

It gives emphasis on problem solving

Stat 1.2 2 1 3 AdvancedStatistics

Stat 1.1 This course presents basic concepts in thedesign of experiments, analysis of variance

and linear regression. It has a large dataanalytic component and will include

applications and data analysis with

computations carried using carried outusing SPSS.

Stat.2 2 1 3 Applied

Statistics

Math 3 This course deals with descriptivestatistics, laws of probability, some

sampling distributions, some discrete and

continuous probability distributions, point

and interval estimations, testing ofhypotheses, simple linear regression and

correlation, and one-way and two-way

ANOVA

Stat 3 2 1 3 Surveys,

Questionnaires,

Data

Management

Stat 1/Stat 2 Research process; techniques of data

collections; principles of questionnairedesign; data coding and encoding; data

quality Control; presentation of research

findings.

Stat 10 4 0 4 Biostatistics Stat 1 A course that focuses on the application of

descriptive and inferential statistics. Itincludes instruction in mathematical

statistics, modeling, methodology, disease

and survival analysis, missing dataanalysis, biostatistics consulting, and other

applications on biological, physical , and

Social Sciences.


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Stat 12 3 0 3 Probability

and Statistics

Math 4/Math

3

Basic principles of statistics; presentationand analysis of data; averages; median,

mode; deviations; probability

distributions; normal curves andapplications; regression analysis and

correlation; application to engineering

problems.

Stat 13 3 0 3 Probability Math 2 This course deals with the following topics:

Counting Techniques- Permutations andCombinations; Probability; RandomVariable and Probability distribution;

Variance and Co-variance;Properties and

Means of Variances; ChebyshevsTheorem; Discrete Probability

Distributions, and Continuous Probability

distributions.

B. STATISTICS MAJOR SUBJECTS

Stat. 100 3 0 3 Statistical

Computing

Stat.2 This course deals with computer aided

statistical computing using statistical

softwares that are used in statistics,statistical software output manipulation,

tabular presentation of data analysis, and

interpretation of results

Stat. 110 3 0 3 Probability

Theory I

Math 11 This course deals with kinds of probability,

axiomatic probability; random variables,distribution functions, and expectations;

special parametric families of univariatedistributions; and joint and conditionaldistributions, stochastic independence, and

more expectation.

Stat. 115 5units

0 5

unitsStatistical

Inference

Stat. 110 This course deals with distributions of

functions of random variables, sampling

and sampling distributions, parametricpoint estimation, parametric interval

estimation, tests of hypotheses, and linear

models.

Stat. 120 3 0 3 Sampling

Techniques

Stat. 110 This course includes the following topics:an introduction to survey sampling,

response process, probability sampling,

sampling distribution for estimates, simplerandom sampling, (sample size

determination, ratio estimation,

nonresponse), cluster sampling

(subsampling, optimal, proportional and

equal allocations), systematic sampling(unequal-sized clusters), stratified cluster

sampling (weighting and ratio estimation),are sampling techniques, replication and

pseudo-replication (balanced repeated,

jackknife), and missing data analysis(weighting and imputation).

Stat. 125 2 1 3 Regression

Analysis w/

Stat 110 The course include the following topics:

scatterplots and regression; simple linear


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Lab regression; multiple regression; drawingconclusions; weights, lack of fits and more;

polynomials and factors; transformations;

regression diagnostics: residuals; outliersand influence; variable selection; nonlinear

regression; and logistic regression.

Stat. 130 3 0 3 Experimental

Designs

Stat. 110 This course will cover issues related to

particular experimental and quasi-experimental designs used in researches(e.g., psychological) as well as statistical

principles related to these designs,

including factorial designs, designs usingcovariates, quasi-experimental designs,

meta-analysis, and power. Statistical

analysis for each design will be discussedalong with issues related to the design.

Topics covered include fundamental

statistics; the statistical basis for

recognizing real effects in noisy data;statistical tests and reference distributions;

analysis of variance; construction,

application, and analysis of factorial andfractional factorial designs; screening

designs; response surface and optimization

methods.

Stat 133 2 1 3 Categorical

Data Analysis

Stat 125 The course includes the following topics:introduction, contingency tables,

generalized linear models, logistic

regression, building and applying logisticregression models, multicategory logitmodels, loglinear models for contingency

tables, models for matched pairs, modeling

correlated-clustered responses, and randomeffects: generalized linear mixed models.

Stat 135 2 1 3 Applied

Survival

Analysis

Stat 125 This course includes variable data

collection, identification of the scale of

continuous covariate, interpretation of afitted model, assessment of fit and model

assumptions, regression diagnostic,recurrent event models and competing risk.

It focuses primarily on practicalapplication, and it relies on a model-

building approach.

Stat 136 3 0 3 Linear

Statistical

Models

Stat 125 The content of this course will include thenature of a linear model, least squares and

maximum likelihood estimation, analysis

of residuals, the general linear model,

violation of assumptions (multicollinearity,heteroscedasticity, autocorrelation,

measurement error, specification error),

models with dummy variables, analysis ofvariance, and analysis of covariance.

Stat 139 2 1 3 Time Series Stat 125 The objective of this course is to provide a

solid introduction to the methods and

underlying theory of modern time seriesanalysis. The main focus will be on


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modelling (ARIMA models, state spacemodels) , frequency domain methods and

other topics (time permitting) and

forecasting second-order stationaryprocesses in the framework of the classical

linear ARIMA model. The course also

includes the discussion on nonlinearGARCH models which are popular for

financial time series. The focus will bemore on methodology and data analysis

with statistical software R than theory

Stat 142 3 0 3 Nonparametric

Statistics

Stat 125 The course includes the following topics:

review of elementary probability,Goodness-of-fit Tests, Tests for a single

location parameter, test for several location

parameters, Tests for scale parameters,Distribution tests, Measures of Association,

Tests for Randomness trends,

Nonparametric regression, and

Bootstrap/Permutation tests.

Stat 145 3 0 3 Multivariate

Analysis

Stat 125 The course includes the following topics:

applied multivariate methods, samplecorrelations, multivariate data plots, review

on eigenvalues and eigenvectors, principal

component analysis, factor analysis,discriminant analysis, logistic regression

methods, cluster analysis, mean vectors

and variance-covariance matrices,multivariate analysis of variance,

prediction models and multivariate

regression, and canonical correlation

analysis.

PREPARED BY: MATHEMATICS DEPARTMENT

NOTED BY:

JAYROLD P. ARCEDE Ph.D

CHAIR, MATH DEPT.

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