3.3 - Measures of Position
... Who is taller, a man 73 inches tall or a woman 68 inches tall? The obvious answer is that the man is taller. However, men are taller than women on the average. Let’s ask the question this way: Who is taller relative to their gender, a man 73 inches tall or a woman 68 inches tall? ...
... Who is taller, a man 73 inches tall or a woman 68 inches tall? The obvious answer is that the man is taller. However, men are taller than women on the average. Let’s ask the question this way: Who is taller relative to their gender, a man 73 inches tall or a woman 68 inches tall? ...
EquationsStudy
... Note that in this notation, it looks similar to the equations for the mean. Note that the n-1 exists to make sure that s is an unbiased estimator of σ. You don’t have to understand why, just the fact that s is unbiased is important. ...
... Note that in this notation, it looks similar to the equations for the mean. Note that the n-1 exists to make sure that s is an unbiased estimator of σ. You don’t have to understand why, just the fact that s is unbiased is important. ...
... is special track is a forum for discussing the latest approaches in computational linguistics related to cognitive semantics and to artificial intelligence. Its aim is also to exchange ideas concerning the way of building efficient systems for language analysis based on cognitive semantic models. e ...
Rogues and Suspects: How to Tackle Outliers
... the sample median, and then the median of these deviations. The MAD forms the basis of another possible outlier test: if an individual result differs in magnitude from the sample median by more than 5 times the MAD it is a candidate for rejection. The MAD is also used in robust statistics (see below ...
... the sample median, and then the median of these deviations. The MAD forms the basis of another possible outlier test: if an individual result differs in magnitude from the sample median by more than 5 times the MAD it is a candidate for rejection. The MAD is also used in robust statistics (see below ...
BasicStatisticalConcepts
... manufacturer, then a random variable X can be defined as the number of defective laptops in the lot. The possible values for X are any number from zero to 10. Continuous RV: A random variable that can take an uncountable range of values. For instance, if an experiment consists of measuring the amoun ...
... manufacturer, then a random variable X can be defined as the number of defective laptops in the lot. The possible values for X are any number from zero to 10. Continuous RV: A random variable that can take an uncountable range of values. For instance, if an experiment consists of measuring the amoun ...
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.).