Basic data

Name: Descriptive and inferential research methods
Dates: November 09 from 2017 to March 31 from 2018

Presentation


Duration:

210 hours: 160 classroom hours and 50 hours of autonomous work

Timetable:

  • Group (MINITAB YR): Thursday and Friday from 11H00 to 13h00 and Saturdays from 8h00 to 13h00

Modality:

blended

Certificate:

Expert in Descriptive and inferential research methods

Background:

This course is aimed at teachers, researchers and professionals interested in the subject, both in the technical and social areas, whose interest in research requires statistical validation and corroboration of data through hypothesis testing, regression and experimental designs. The objective is to understand the statistical methods of the area, the statistical tests they use, and how those tests would benefit the different scientific fields of each researcher. This is an introductory course to the various subjects.

The course will be developed with the use of commercial software MINITAB and free language R.

Objectives:

The main objectives are:

  • Understand the basic and inferential descriptive statistics.
  • Understand the statistical assumptions necessary when designing a research design.
  • Understand the generalities and the application of hypothesis tests.
  • Understand the generalities and develop multivariate regression models
  • Understand the generalities and development of various experimental designs.
Contents:

Each module will be theoretical-practical so the participant requires software and a laptop of their own. There will be individual readings, and developments of individual and group exercises. The modules will be 24 contact hours of classes, plus 6 hours of tutorials if the student requires, in the hours that each teacher establishes. In addition to this, participants require 10 hours of autonomous work to solve exercises, workshops and readings.

Previous requirements:

This course recommends previously having basic knowledge of statistics and algebra. It will not cover aspects of abstract mathematics and will not cover the development of tests with high level symbolic complexity.

Expected results:

At the end of the course, the participant will be able to have the appropriate criteria to understand models of basic statistical tests and interpret the information that is collected from some articles, in addition to promoting their own tests and statistical experiments.

ModuleDescriptionFace-to-faceAutonomousTotal
Software Introduction

Minitab and Language R

10010
1 Module: Descriptive StatisticsDescriptive, random variables distribution probability24 + 6 (Tutoring)1040
2 Module: Inferential StatisticsSampling, inferences and hypothesis testing24 + 6 (Tutoring)1040
3 Module: Simple and Multiple RegressionSimple and multiple regression24 + 6 (Tutoring)1040
4 module: 1 factor, 2 and more factors, factorial, fractional, mixtures and surfacesExperiments of 1 factor, 2 and more factors, factorial, fractional, mixtures and surfaces24 + 6 (Tutoring)1040
5 Module: Non-parametric Statistics Non-parametric24 + 6 (Tutoring)1040

Content detail by module

Introduction Module

Subject

Content

Hours

Minitab

5

Statistics and Basic Graphics

1

Confidence Intervals

1

Hypothesis Tests

1

Normal Distributions

1

Tests of goodness of fit

1

R

5

Generalities and management of variables

3

Statistics and Basic Graphics

2

Form 1

DESCRIPTIVE STATISTICS (40HS)

Subject

Content

Hours

Introduction

What is Statistics? Why study it? Types of statistics. Population and sample. Types of variables.

2

Descriptive statistics

14

Obtaining and Organization of the data. Parameters and statistics. Frequency distributions. Histogram

2

Measures of central tendency: Mean, median, mode for ungrouped and grouped data.

4

Dispersion measures: Variance, standard deviation, coefficient of variation.

4

Relative position measures: Quartiles, deciles and percentiles.

4

Probability and probability distributions

8

Elementary Probability. Sample space. Events Probabilities of an event.

1

Additive Rules. Conditional Probability. Multiplicative rules

3

Random variables. Discrete and continuous probability distributions.

4

Form 2

INFERENTIAL STATISTICS (40HS)

Subject

Content

Hours

General concepts

4

Sampling and census. Random and stratified sampling.

2

Distribution of sample means. Central Limit Theorem.

2

Estimation and hypothesis testing

20

Point and interval estimates (confidence intervals). Estimation of the average of a population. Estimation error Estimation of the proportion of a population. Sample sizes.

3

Procedure to test a hypothesis. Null and alternative hypothesis, Values-P. Tests of one and two tails for medias.1hs

3

Hypothesis tests for a sample (Test "t").

3

Hypothesis tests for two samples (Test "t").

3

Variance analysis. ANOVA One Way.

8

Form 3

SIMPLE AND MULTIPLE REGRESSION (40hs)

Subject

Content

Hours

Linear regression of 1 predictor

14

Generalities

2

Square minimum method

1

Residuals

1

Straight adjustments

1

Normal errors in regression models

1

Inferences for regression and correlation

1

Prediction of new observations

1

Residual analysis and statistical tests

2

Tests for normality

2

Tests for homoscedasticity

2

Multiple Regression

10

Generalities

2

General linear model

2

Regression ANOVA

2

Estimation of means and new observations

1

Multicollinearity

1

Use of quantitative variables

1

Selection of model criteria

1

Form 4

Experiments of 1 factor, 2 and more factors, factorial, fractional, mixtures and surfaces (40 HS)

Subject

Content

Hours

Introduction to Experiment Design

9

Generalities

1

Basic definitions

1

Elements of inference Review

1

Random designs

1

ANOVA for DOE

2

Graphic checking methods

1

Qualitative Data

1

Statistical tests for waste treatment

1

Factorial and Fractional Experiments

8

Analysis of variance of two factors

2

The principles for the construction of factorial and fractional designs

1

2k Factorial Designs. Calculation of the effects. Confusing factors Resolution of a fractional factorial design

1

Contrasts and measures of effects on treatments

2

Evaluation of a model

2

Optimization and Desirability and Testing

7

Transformation and procedure Box-Cox

2

Optimization and desirability

2

Methodology of the response surface.

2

Design of Mixtures. Factors involved in a mix design.

1

Form 5

NON PARAMETRIC STATISTICS (40 HS)

Subject

Content

Hours

Introduction

1

Why use nonparametric statistics?

1

Nonparametric tests

15

Sign test.

3

Categorical association (Chi-square)

3

A sample. Try Wilcoxon.

3

Two samples Test Wilcoxon / Mann-Whitney.

2

More than two samples Try Kruskal-Wallis.

2

Test Kolmogorov-Smirnov for goodness of distribution adjustments.

2

Resampling and boostrap

5

Resampling and boostrap

5

Non-parametric correlation

5

Spearman correlation

5

Form of Participation and Approval

Each participant can enroll in any module individually, if the module is approved, a certificate will be delivered with the institutional endorsement by the approved module. The approval of each module requires a minimum evaluation of the 70% and attend at least the 80% of the face-to-face classes.

Participants who participate and pass at least 160 hours, after an exam, will be awarded an Expert in Statistics certificate

Teaching plant:

Module

Software Introduction

Ing. Jonnatan F. Aviles PhD.

Module 1

Agrim. Daniela Ballari PhD.

Module 2

Agrim. Daniela Ballari PhD.

Module 3

Ing. Jonnatan F. Aviles PhD.

Module 4

Ing. Jonnatan F. Aviles PhD.

Module 5

Agrim. Daniela Ballari PhD.

Target:

Teachers, researchers and professionals interested in the subject

Cost for the full course:

  • External: $ 940,00
  • UDA staff: $ 375,00

Cost per module:

  • External: $ 190,00
  • UDA staff: $ 75,00
  • The payment must be made in the Treasury of the University of Azuay.

Payment options:

  • Cash
  • With credit card, up to three months without interest.
  • Request the Administrative Financial Dean to pay in three installments through a discount in the payment role

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Certificates:

More information:

Ing. Miriam Briones García: formacion.continua@uazuay.edu.ec



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