1. No texto sobre Ontologia [Model Driven ] s fls. 47 o autor afirma
Another important issue here is the distinction between ontological
knowledge and all other types of knowledge, illustrated in Table 1-1 [see
TelEduc Table 1-1]. An ontology represents t
1. Considere o caso de estudo s fls. 59/60 e slide 26. Comente a questo de timestamp, confivel e no-confivel, dentro de um exemplo que voc construa.
Time-stamp by itself not is trusted because the magnetic media can be altered easily
a. Escreva um texto (1 pgina) sobre pontos em comum e
distintos entre Standards e Specifications. Cite verses no
Set of construction rules that tells you how to
represent a required set of information.
1. Como so os identificadores da Eduroam e da Federao CAFe - RNP ?
Eduroam (Education Roamin)
In eduroam, user's authentication request is sent within the RADIUS
protocol packets, which are routed to user's home institution on the basis
of the User-Name a
1) Considere a questo da indexao da informao no contexto de Big Data. V s pginas 10-11
do texto de referncia e comente os pontos de 1-a-10 sobre ndices.
Indexes should be accepted as another device for driving down the complexity of Big Data
In Chap. 8, we discussed testing a hypothesis regarding the relationship between
a binary explanatory variable (referred to as the factor) and a numerical response
variable using two-sample t-tests. We also
Roger Day 2012
Combining evidence across studies; overviews and
meta-analysis; intro to MCMC
Meta-analysis; intro to MCMC
Does treatment with magnesium protect patients after myocardial infarction from dying from
These data are
Statistical Inference for the Relationship
Between Two Variables
In the previous two chapters, we discussed estimation and hypothesis testing regarding the population mean of a random variable. For instance, using sample data,
For further volumes:
Biostatistics with R
An Introduction to Statistics
Through Biological Data
Prof. Babak Shahbaba
Department of St
In Chap. 4, we discussed Bayes theorem and mentioned that it is the basis of the
Bayesian Statistics. In this chapter, we discuss Bayesian inference regarding the
population proportion as an example for the a
This data set is based on an epidemiological survey of 2484 people to investigate snoring as a risk factor for heart attack. The data set is discussed in Categorical Data Analysis by Agresti (2002). The first variable is snoring_severity as reported by th
Analysis of Categorical Variables
In Chap. 7, we talked about hypothesis testing regarding population proportions.
There, we used the central limit theorem (for large enough sample sizes) to obtain
an approximate normal distri
Linear regression models discussed previously are used to predict the unknown values of the response variable. In these models, the response variable has a central
role; the model building process is guided by expla
Analysis of Variance (ANOVA)
In Chap. 8, we discussed how two-sample t -tests can be used to evaluate hypotheses regarding the difference between the means of two groups. We mentioned that
we typically use this approach to inves
Random Variables and Probability Distributions
5.1 Random Variables
In the previous chapter, we discussed random events and their probabilities. We used
the possible genotypes of a bi-allelic gene A as an example. We dened its sample
space, S =
In the previous chapter, we focused on estimating parameters such as the population
mean and variance. In this chapter, we rely on estimators, their sampling distributions, and their specic values from observe
More Basics of Probability
p.1 of 8
Marginal and conditional probabilities; independence; conditional independence
Recall the data from last time:
Converting to random variables:
6.1 Parameter Estimation
In the previous chapter, we discussed using random variables to represent characteristics of a population (e.g., BMI, disease status). Furthermore, we discussed some
commonly used probability distributions for
4.1 Probability as a Measure of Uncertainty
In the previous chapters, we used plots and summary statistics to learn about the
distribution of variables and to investigate their relationships. In the birthweight example, from a sample
Installing R and R-Commander
This appendix gives detailed instructions for installing R and R-Commander.
A.1 Installing R
Go to http:/www.r-project.org/.
Click on the download R link.
Then select a location closest to you.
3.1 Visualizing and Summarizing Relationships Between
In the previous chapter, we focused on using graphs and summary statistics to explore the distribution of individual variables. This chapter is dedicated to
1.1 Statistical Methods in the Context of Scientic Studies
This book discusses statistical methods from the application point of view. More
specically, we focus on biostatistical methods, which involve applying statistical
Page 1 of 6
The least squares principle.
We have data with
a PREDICTOR (covariate, feature, attribute, independent variable), and
a TARGET (outcome, dependent variable).
Example: See GAGurine.R.
The simple linear regression m
ANOVA, logistic regression, discriminant analysis, survival analysis p. 1 of 5
ANOVA: Analysis of variance
In supervised learning (regression analysis), when a predictor is a CATEGORY, we can replace it
by a series of indicator functions, then