Verzani SimpleR
... are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course. These notes were written to take advantage of R version 1.5.0 or later. For pedagogic ...
... are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course. These notes were written to take advantage of R version 1.5.0 or later. For pedagogic ...
simpleR – Using R for Introductory Statistics - CSI Math
... textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course. These notes were written to take advantage of R version 1.5.0 or later. For pedagogical reasons the equals sign, =, is used as an assignment operator ...
... textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course. These notes were written to take advantage of R version 1.5.0 or later. For pedagogical reasons the equals sign, =, is used as an assignment operator ...
An Overview of Some Recent Developments in Bayesian Problem
... Bayesian network to model existing vehicle capabilities and the uncertainty regarding the state of those capabilities. It selects from the available alternatives the best response to the unanticipated event with the aim of maximizing the overall achievement of mission objectives. Bayesian Ship Self ...
... Bayesian network to model existing vehicle capabilities and the uncertainty regarding the state of those capabilities. It selects from the available alternatives the best response to the unanticipated event with the aim of maximizing the overall achievement of mission objectives. Bayesian Ship Self ...
A Bayes Optimal Approach for Partitioning the Values of Categorical
... attributes. Many induction algorithms rely on discrete attributes and need to discretize continuous attributes or to group the values of categorical attributes when they are too numerous. While the discretization problem has been studied extensively in the past, the grouping problem has not been exp ...
... attributes. Many induction algorithms rely on discrete attributes and need to discretize continuous attributes or to group the values of categorical attributes when they are too numerous. While the discretization problem has been studied extensively in the past, the grouping problem has not been exp ...
Linear Methods for Classification - Penn State Department of Statistics
... The feature vector X = (X1 , X2 , ..., Xp ), where each variable Xj is quantitative. The response variable G is categorical. G ∈ G = {1, 2, ..., K } Form a predictor G (x) to predict G based on X . Email spam: G has only two values, say 1 denoting a useful email and 2 denoting a junk email. X is a 5 ...
... The feature vector X = (X1 , X2 , ..., Xp ), where each variable Xj is quantitative. The response variable G is categorical. G ∈ G = {1, 2, ..., K } Form a predictor G (x) to predict G based on X . Email spam: G has only two values, say 1 denoting a useful email and 2 denoting a junk email. X is a 5 ...
Lecture_23
... How should we think about the randomness in time series data? • The outcome of economic variables (e.g. GNP, Dow Jones) is uncertain; they should therefore be modeled as random variables • But time series are specific sequences of random variables. We call this a "stochastic process“ • Randomness do ...
... How should we think about the randomness in time series data? • The outcome of economic variables (e.g. GNP, Dow Jones) is uncertain; they should therefore be modeled as random variables • But time series are specific sequences of random variables. We call this a "stochastic process“ • Randomness do ...
A Comprehensive Overview of Clustering
... K-Means Clustering K-means is one of the simplest unsupervised learning algorithm that is used to generate specific number of disjoint and non-hierarchical clusters based on attributes[4]. It is a numerical, nondeterministic and iterative method to find the clusters. The purpose is to classify data. ...
... K-Means Clustering K-means is one of the simplest unsupervised learning algorithm that is used to generate specific number of disjoint and non-hierarchical clusters based on attributes[4]. It is a numerical, nondeterministic and iterative method to find the clusters. The purpose is to classify data. ...
Time series
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).