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Sampling Distribution of Difference Between Means Sampling Distribution of Difference Between Means Author(s) David M. Lane Prerequisites Sampling Distributions, Sampling Distribution of the Mean, Variance Sum Law I Learning Objectives State the mean and variance of the sampling distribution of the difference between means Compute the standard error of the difference between means Compute the probability of a difference between means being above a specified value The sampling distribution of the difference between means can be thought of as the distribution that would result if we repeated the following three steps over and over again: (1) sample n1 scores from Population 1 and n2 scores from Population 2, (2) compute the means of the two samples (M1 and M2), and (3) compute the difference between means, M1 - M2. As you might expect, the mean of the sampling distribution of the difference between means is: which says that the mean of the distribution of differences between sample means is equal to the difference between population means.

Pattern Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and <canvas> visualization. The module is free, well-document and bundled with 50+ examples and 350+ unit tests. Download Installation Pattern is written for Python 2.5+ (no support for Python 3 yet). To install Pattern so that the module is available in all Python scripts, from the command line do: > cd pattern-2.6 > python install If you have pip, you can automatically download and install from the PyPi repository: If none of the above works, you can make Python aware of the module in three ways: Quick overview pattern.web pattern.en The pattern.en module is a natural language processing (NLP) toolkit for English. pattern.vector Case studies

Generalized linear model In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Intuition[edit] Ordinary linear regression predicts the expected value of a given unknown quantity (the response variable, a random variable) as a linear combination of a set of observed values (predictors). However, these assumptions are inappropriate for many types of response variables. Similarly, a model that predicts a probability of making a yes/no choice (a Bernoulli variable) is even less suitable as a linear-response model, since probabilities are bounded on both ends (they must be between 0 and 1). Overview[edit] Model components[edit] 1. as

Home | The IATI Standard Alpha version Please note that the Datastore is currently in its first release. Therefore, data queries may sometimes result in unexpected results. We appreciate your understanding. What is the IATI Datastore? The IATI Datastore is an online service that gathers all data published to the IATI standard into a single queryable source. How does it work? Data that is recorded on the IATI Registry, and is valid against the standard, is pulled into the Datastore on a nightly basis. Who is it for? The store is a service for analysts, data journalists, infomediaries and developers. Why a store? This repository is called a store, not a database, because it cannot be used as a single dataset. How to access the Datastore¶ An API is available that enables people to construct queries.For those wishing to just access the data in CSV format, an online form is available to assist with queries Are there any limitations on the Datastore?

Linear regression In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression, where multiple correlated dependent variables are predicted,[citation needed] rather than a single scalar variable.) In linear regression, data are modeled using linear predictor functions, and unknown model parameters are estimated from the data. Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. Linear regression has many practical uses. If the goal is prediction, or forecasting, or reduction, linear regression can be used to fit a predictive model to an observed data set of y and X values. where Example.

An Interactive Infographic Maps The Future Of Emerging Technology Can speculation about the future of technology serve as a measuring stick for what we create today? That’s the idea behind Envisioning Technology's massive infographic (PDF), which maps the future of emerging technologies on a loose timeline between now and 2040. Click to enlarge. On it you’ll find predictions about everything from artificial intelligence and robotics to geoengineering and energy. Mouse over the entries for blurbs describing them and links to more information; you won’t find much more than a Wikipedia page explanation, but that’s plenty helpful for the uninitiated. In 30 years, it will also be a great reference for where we thought we might end up. You can download a PDF for free, or--should you want to track our progress toward artificial photosynthesis and space-based solar power by X-ing out accomplishments on your wall--purchase a poster version here.

Ordinary least squares Okun's law in macroeconomics states that in an economy the GDP growth should depend linearly on the changes in the unemployment rate. Here the ordinary least squares method is used to construct the regression line describing this law. In statistics, ordinary least squares (OLS) or linear least squares is a method for estimating the unknown parameters in a linear regression model. This method minimizes the sum of squared vertical distances between the observed responses in the dataset and the responses predicted by the linear approximation. The resulting estimator can be expressed by a simple formula, especially in the case of a single regressor on the right-hand side. The OLS estimator is consistent when the regressors are exogenous and there is no perfect multicollinearity, and optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated. Linear model[edit] Suppose the data consists of n observations { y i, x i }ni=1. Assumptions[edit]

Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is in Which are the most hyped technologies today? Check out Gartner's latest 2015 Hype Cycle Report. Autonomous cars & IoT stay at the peak while big data is losing its prominence. Smart Dust is a new cool technology for the next decade! By Bhavya Geethika. Gartner, the leading market and technology research firm, has published its 2015 Hype Cycle Report of Emerging technologies. Fig. 1: Gartner 2015 Hype Cycle. For comparison, here is Fig. 2: Gartner 2014 Hype Cycle. What's the Hype Cycle about? As technology advances, we all get over-excited about new buzz-words & trends in technology and then disappointed when expectations of results go down. Five regions of Gartner's Hype cycle: Innovation Trigger(potential technology breakthrough kicks off), Peak of Inflated Expectations(Success stories through early publicity), Trough of Disillusionment( waning interest), Slope of Enlightenment (2nd & 3rd generation products appear) and Plateau of Productivity (Mainstream adoption starts). Related:

Generalized least squares Method outline[edit] In a typical linear regression model we observe data on n statistical units. The response values are placed in a vector Y = (y1, ..., yn)′, and the predictor values are placed in the design matrix X = [[xij]], where xij is the value of the jth predictor variable for the ith unit. Here β is a vector of unknown “regression coefficients” that must be estimated from the data. Suppose b is a candidate estimate for β. Since the objective is a quadratic form in b, the estimator has an explicit formula: Properties[edit] GLS is equivalent to applying ordinary least squares to a linearly transformed version of the data. Weighted least squares[1][edit] A special case of GLS called weighted least squares (WLS) occurs when all the off-diagonal entries of Ω are 0. Feasible generalized least squares[edit] In practice, the method cannot be applied since the covariance of the errors is generally unknown. The ordinary least squares (OLS) estimator is calculated as usual by are constructed.