what is the difference between predictive and criterion data and how is each used?
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Criterion-related validity refers to the degree to which a measurement can accurately predict specific criterion variables. ... Predictive validity refers to the extent to which scores on a measurement are able to accurately predict future performance on some other measure of the construct they represent.
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Two types of statistical methods are used in analyzing data: descriptive statistics and inferential statistics. Descriptive statistics are used to synopsize data from a sample exercising the mean or standard deviation. Inferential statistics are used when data is viewed as a subclass of a specific population.
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Data mining is more about digging data, discovering patterns and coming up with theories to get to inferences. But the methods of statistical analysis can be applied only on data that is cleansed. Statistics is more about confirmation and applying the various theories.
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Predictive analytics uses many techniques – data mining, statistics, modeling, machine learning, and artificial intelligence. In pharmaceuticals, predictive modeling can help identify new potential drugs with a higher probability of being successfully developed and approved.
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Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.
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The primary difference between classification and tabulation is that the process of classifying data int groups is known as classification of data, whereas tabulation is the act of presenting data in tabular form, for better interpretation.
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Another notable difference between the two is that Big data employs complex technological tools like parallel computing and other automation tools to handle the “big data”. Data analytics use predictive and statistical modelling with relatively simple tools.
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The main difference between attribute data and spatial data is that the attribute data describes the characteristics of a geographical feature while spatial data describes the absolute and relative location of geographic features.
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Attribute data is the simplest kind. You sample some number of items and you classify each item as either having some attribute, like being defective, or not. ... Variables data contains more information than attribute data per data-point.
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Decision tree is one of the predictive modelling approaches used in statistics , data mining and machine learning . ... It is one of the most widely used and practical methods for supervised learning. Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks.
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As nouns the difference between criterion and condition is that criterion is a standard or test by which individual things or people may be compared and judged while condition is a logical clause or phrase that a conditional statement uses the phrase can either be true or false.
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KEY DIFFERENCE Data is a raw and unorganized fact that is required to be processed to make it meaningful whereas Information is a set of data that is processed in a meaningful way according to the given requirement.
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Quantitative forecasting methods are based on using calculations to make forecasts. These calculations require past data. They are more objective. I think that qualitative methods are better because we are in a rapidly changing environment where many times the past is not a good predictor of the future.
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Data analytics is more specific and concentrated than data science. Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling. ... On the other hand, data analytics involves a few different branches of broader statistics and analysis.
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Demand estimation and forecasting. ... The answer is that estimation attempts to quantify the links between the level of demand and the variables which determine it. Forecasting, on the other hand, attempts to predict the overall level of future demand rather than looking at specific linkages.
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KEY DIFFERENCE Data does not have any specific purpose whereas Information carries a meaning that has been assigned by interpreting data. ... Data can be structured, tabular data, graph, data tree whereas Information is language, ideas, and thoughts based on the given data.
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Statistical methods are required to ensure that data are interpreted correctly and that apparent relationships are meaningful (or “significant”) and not simply chance occurrences. A “statistic” is a numerical value that describes some property of a data set.