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Level Ladders
Level Ladders

... • Move between the general and the particular to test the logic of an argument • Interpret the results of an experiment using the language of probability; appreciate that random processes are unpredictable • Know that if the probability of an event occurring is p, then the probability of it not occu ...
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... without being stifled by detail. It can also carry out multiple operations simultaneously. Not only can it identify patterns in a few variables, it also can detect correlations in hundreds of variables. It is this feature of the network that is particularly suitable in analyzing relationships betwee ...
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Integrating the Computer and the Calculator in the Statistics

... Sample mean, x Sample standard deviation, s Sample size, n You must show all work (not just answers). If you use your calculator to find an answer, explain carefully what you did. Again, neatness counts and there is a grade reduction for lateness. ...
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A Survey of the Eighth National Conference on Artificial Intelligence

... the papers, whereas differentiated by illustrate it with examples from the proceedings. statistical tests of subject and methodmy hypotheses are ology, by what they described in Four study and how they Hypotheses and in Statistical Analyses. Argustudy it. Subfields in AI study intelligent ments agai ...
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Dynamic shaping of dopamine signals during probabilistic

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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.).
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