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When you create a new workspace in Azure Machine Learning, a number of sample datasets and gratis test fur binare optionen template are included by default. Many of these sample gratis test fur binare optionen template are used by the sample models in the Azure AI Gallery. Others are included as examples of various types of data typically used gratis test fur binare optionen template machine learning.

Some of these datasets are available in Azure Blob storage. For these datasets, the following table provides a direct link. You can use these datasets in your experiments by using the Import Data module. The rest of these sample datasets are available in your workspace under Saved Datasets. You can find this in the module palette to the left of the experiment canvas in Machine Learning Studio. You can use any of these datasets in your own experiment by dragging it to your experiment canvas. You can try Azure Machine Learning for free.

No credit card or Azure subscription is required. Classify people using demographics to gratis test fur binare optionen template whether a person earns over 50K a year. This dataset contains one row for each U. Automobile price data Raw Information about automobiles by make and model, including the price, features such as the number of cylinders and MPG, as well as an insurance risk score.

The risk score is initially associated with auto price. It is then adjusted for actual risk in a process known to actuaries as symboling. Predict the risk score by features, using regression or multivariate classification.

The dataset has one row for each hour of each day in andfor a total of 17, rows. The range of hourly bike rentals is from 1 to The code for converting the image is provided in the Color quantization using K-Means clustering model detail page. Donor data includes the months since last donationand frequency, or the total number of donations, time since last donation, and amount of blood donated.

The goal is to predict via classification whether the donor donated blood in Marchwhere 1 indicates a donor during the target period, and 0 a non-donor.

Combines diagnostic information with features from laboratory analysis of about tissue samples. Classify the type of cancer, based on 9 attributes, some of which are linear and some are categorical. University of California, School of Information and Computer Science Breast Cancer Features The dataset contains information for K suspicious regions candidates of X-ray images, each described by features. The features are proprietary and their meaning is not revealed by the dataset creators Siemens Healthcare.

Breast Cancer Info The dataset contains additional information for each suspicious region of X-ray image. Each example provides information for example, label, patient ID, coordinates of patch relative to the whole gratis test fur binare optionen template about the corresponding row number in the Breast Cancer Features dataset. Each patient has a number of examples.

For patients who have a cancer, some examples are positive and some are negative. For patients who don't have gratis test fur binare optionen template cancer, all examples are negative. The dataset has K examples. The dataset is biased, 0. The dataset was made available by Siemens Healthcare. The dataset contains 50K customers from the French Telecom company Orange.

Each customer has gratis test fur binare optionen template features, of which are numeric and 40 are categorical. The features are very sparse. Energy-Efficiency Regression data A collection of simulated energy profiles, based on 12 different building shapes.

The buildings are differentiated by eight features. This includes glazing area, the glazing area distribution, and orientation. Use either regression or classification to predict the energy-efficiency rating based as one of two real valued responses. For multi-class classification, is round the response variable to the nearest integer. Department of Transportation On-Time. The dataset covers the time period April-October Before uploading to Azure Machine Learning Studio, the dataset was processed as follows: The dataset was filtered to cover only the 70 busiest airports in the continental US Canceled flights were labeled as delayed by more than 15 minutes Diverted flights were filtered out The following columns were selected: Forest fires data Contains weather data, such as temperature and humidity indices and wind speed.

The data is taken from an area of northeast Portugal, combined with records of forest fires. This is a difficult regression task, where the aim is to predict the burned area of forest fires. The dataset classifies people, described by a set of attributes, as low or high credit risks. Each example represents a person. There are 20 features, both numerical and categorical, and a binary label the credit risk value. The cost of misclassifying a low risk example as high is 1, whereas the cost of misclassifying a high risk example as low is 5.

Gratis test fur binare optionen template are 17K movies in the dataset. The dataset was introduced in the paper "S. De Pessemier and L.

The dataset is relatively small, containing 50 examples each of petal measurements from three iris varieties. Predict the iris type from the measurements. The dataset has K ratings for movies, extracted from well-structured tweets on Twitter. Each instance represents a tweet and is a tuple: The dataset was made available by A. Tikk for Recommender Systems Challenge Gratis test fur binare optionen template data for various automobiles This dataset is a slightly modified version of the dataset provided by the StatLib library of Carnegie Mellon University.

The dataset was used in the American Statistical Association Exposition. The data lists fuel consumption for various automobiles in miles per gallon. It also includes information such as the number of cylinders, engine displacement, horsepower, total weight, and acceleration. Predict fuel economy based on three multivalued discrete attributes and five continuous attributes. StatLib, Carnegie Mellon University, The dataset was filtered to focus on female patients of Pima Indian heritage. The data includes medical data such as glucose and insulin levels, as well as lifestyle factors.

Predict whether the subject has diabetes binary classification. University of California, School of Information and Computer Science Restaurant customer data A set of metadata about customers, including demographics and preferences. Use this dataset, in combination with the other two restaurant datasets, to train and test a recommender system. Restaurant feature data A set of metadata about restaurants and their features, such as food type, dining style, and location.

Restaurant ratings Contains ratings given by users to restaurants on a scale from 0 to 2. Steel Annealing multi-class dataset This dataset contains a gratis test fur binare optionen template of records from steel annealing trials. It contains the physical attributes width, thickness, type coil, sheet, etc. Predict any of two numeric class attributes; hardness or strength. Gratis test fur binare optionen template might also analyze correlations among attributes. Steel grades follow a set standard, defined by SAE and other organizations.

You are looking for a specific 'grade' the class variable and want to understand the values needed. University of California, School of Information and Computer Science A useful guide to steel grades can be found here: Gratis test fur binare optionen template intent of the simulation was to improve the accuracy of ground-based atmospheric Cherenkov gamma telescopes.

This is done by using statistical methods to differentiate between the desired signal Cherenkov radiation showers and background noise hadronic showers initiated by cosmic rays in the upper atmosphere. The data has been pre-processed to create an elongated cluster with the long axis is oriented towards the camera center. The characteristics of this ellipse often called Hillas parameters are among the image parameters that can be used for discrimination.

Predict whether image of a shower represents signal or background noise. Simple classification accuracy is not meaningful for this data, since classifying a background event as signal is worse than classifying a signal event as background.

For comparison of different classifiers, the ROC graph should be used. The probability of accepting a background event as signal must be below one of the following thresholds: Also, note that the number of background events h, for hadronic showers is underestimated. In real measurements, the h or noise class represents the majority of events. The weather data covers observations made from airport weather stations, covering the time period April-October Weather station IDs were mapped to corresponding airport IDs Weather stations not associated with the 70 busiest airports were gratis test fur binare optionen template out The Date column was split into separate Year, Month, and Day columns The following columns were selected: Extract text content for each specific company Remove ranking binare optionen broker vergleich formatting Remove non-alphanumeric characters Convert all text to lowercase Known company categories were added Note that for some companies an article could not be found, so the number of records is less than Each row represents a customer.

The dataset contains nine features about user demographics gratis test fur binare optionen template past behavior, and three label columns visit, conversion, and spend. Visit is a binary column that indicates that a customer visited after the marketing campaign. Conversion indicates a customer purchased something.

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Each procedure is easy-to-use and is carefully validated for accuracy. Use the links below to jump to a two proportions topic. For each sample size estimation procedure, only a brief summary of the procedure is given.

For more details about a particular procedure, we recommend you download and install the free trial of the software. For most of the sample size estimation procedures in PASS for two proportions, the user may choose to solve for sample size, power, or the population effect size in some manner. In the case of confidence intervals, one could solve for sample size or the distance to the confidence limit. In a typical two proportion test procedure where the goal is to estimate the sample size, the user enters power, alpha, and the desired population proportions.

The procedure is run and the output shows a summary of the entries as well as the sample size estimate. A summary statement is given, as well as references to the articles from which the formulas for the result were obtained. For many of the parameters e. When this is done, estimates are made for every combination of entered values. A numeric summary of these is results is produced as well as easy-to-read sample size or power curve graphs.

This page provides a brief description of the tools that are available in PASS for power and sample size analysis of two proportions. If you would like to examine the formulas and technical details relating to a specific PASS procedure, we recommend you download and install the free trial of the software, open the desired proportions procedure, and click on the help button in the top right corner to view the complete documentation of the procedure.

There you will find summaries, formulas, references, discussions, technical details, examples, and validation against published articles for the procedure. From this window the desired procedure is selected from the menus, the category tree on the left, or with a procedure search.

The procedure opens and the desired entries are made. When you click the Calculate button the results are produced.

You can easily navigate to any part of the output with the navigation pane on the left. This procedure computes power and sample size for hypothesis tests of the difference, ratio, or odds ratio of two independent proportions. The test statistics analyzed by this procedure assume that the difference between the two proportions is zero or their ratio is one under the null hypothesis.

This procedure computes and compares the power achieved by each of several test statistics that have been proposed. The power calculations assume that random samples are drawn from two separate populations. This procedure provides sample size and power calculations for one- or two-sided hypothesis tests of the difference between two independent proportions using the effect size.

The details of procedure are given in Cohen This routine calculates the group sample sizes necessary to achieve a specified interval width of the difference, ratio, or odds ratio of two independent proportions. Calculations may be made for several different confidence interval formulas for differences, ratios, and odds ratios. These procedures provide power analysis and sample size calculation for non-inferiority tests in two-sample designs in which the outcome is binary.

There are four non-inferiority procedures for two proportions. These procedures are identical except for the type of parameterization. The parameterization can be in terms of proportions, differences in proportions, ratios of proportions, and odds ratios.

Users may choose from among several popular test statistics commonly used for running the hypothesis test. These procedures provide power analysis and sample size calculation for equivalence tests in two-sample designs in which the outcome is binary. There are four equivalence procedures for two proportions. These procedures provide power analysis and sample size calculation for superiority by a margin tests in two-sample designs in which the outcome is binary.

There are four superiority procedures for two proportions. There are two procedures for two proportions in a repeated measures design. The parameterization can be in terms of proportions or odds ratios. These procedures calculate the power for testing the time-averaged difference TAD between two proportions in a repeated measures design.

A repeated measures design is one in which subjects are observed repeatedly over time. Measurements may be taken at pre-determined intervals e. This type of time-averaged difference analysis is often used when the outcome to be measured varies with time. For example, suppose that you want to compare two treatment groups based on a certain binary response variable such as the presence or absence of a disease.

The disease status may change over time, depending on various factors unrelated to the treatment. The precision of the experiment is increased by taking multiple measurements from each individual and comparing the time-averaged difference in proportions between the two groups.

Care must be taken in the analysis because of the correlation that is introduced when several measurements are taken from the same individual. The covariance structure may take on several forms depending on the nature of the experiment and the subjects involved.

This procedure allows you to calculate sample sizes and power using four different covariance patterns: These procedures can be used to calculate sample size and power for tests of pairwise contrasts in a mixed models analysis of repeated measures data.

Mixed models analysis of repeated measures data is also employed to provide more flexibility in covariance specification and a greater degree of robustness in the presence of missing data, provided that the data can be assumed to be missing at random. There are a number of group sequential procedures in PASS for the comparison of two proportions. One analytic procedure is available as well as simulation procedures for each of the following test types:.

Either the T-test or the Mann-Whitney-Wilcoxon test may be examined. For one-sided tests, significance and futility boundaries can be produced. The spacing of the looks can be equal or custom specified. The distributions of each of the groups under the null and alternative hypotheses can be specified directly using over ten distributions including normal, exponential, Gamma, Uniform, Beta, and Cauchy.

Futility boundaries can be binding or non-binding. This procedure computes conditional and predicted power for the case when a test is used to test whether the event probabilities of two populations are different. In sequential designs, one or more intermediate analyses of the emerging data are conducted to evaluate whether the experiment should be continued.

This may be done to conserve resources or to allow a data monitoring board to evaluate safety and efficacy when subjects are entered in a staggered fashion over a long period of time.

Conditional power a frequentist concept is the probability that the final result will be significant, given the data obtained up to the time of the interim look. Predictive power a Bayesian concept is the result of averaging the conditional power over the posterior distribution of effect size. Both of these methods fall under the heading of stochastic curtailment techniques. In a stratified design, the subjects are selected from two or more strata which are formed from important covariates such as gender, income level, or marital status.

The number of subjects in each of the two groups in each strata is set fixed by the design. A separate 2-by-2 table is formed for each stratum.

Although response rates may vary among strata, hypotheses about the overall odds ratio can be tested the Cochran-Mantel-Haenszel test. This procedure allows you to determine power and sample size for such a study. A number of procedures are available in PASS for the comparing two proportions in a cluster-randomized design. Below is a list of procedure categories of this type:. A cluster group randomized design is one in which whole units, or clusters, of subjects are randomized to the groups rather than the individual subjects in those clusters.

However, the conclusions of the study concern individual subjects rather than the clusters. Examples of clusters are families, school classes, neighborhoods, and hospital wards. Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually.

For example, it may be difficult for an instructor to use two methods of teaching individuals in the same class. The price of randomizing by clusters is a loss of efficiency—the number of subjects needed to obtain a certain level of precision in a cluster-randomized trial is usually much larger than the number needed when the subjects are randomized individually.

Hence, the basic two-sample methods of sample size estimation cannot be used. A stepped-wedge cluster-randomized design is similar to a cross-over design in that each cluster receives both the treatment and control over time. In a stepped-wedge design, however, the clusters switch or cross-over in one direction only usually from the control group to the treatment group. Once a cluster is randomized to the treatment group, it continues to receive the treatment for the duration of the study.

In a typical stepped-wedge design the all clusters are assigned to the control group at the first time point and then individual clusters are progressively randomized to the treatment group over time. The stepped-wedge design is particularly useful for cases where it is logistically impractical to apply a particular treatment to half of the clusters at the same time. This procedure computes power and sample size for tests for the difference between two proportions in cross-sectional stepped-wedge cluster-randomized designs.

In cross-sectional designs, different subjects are measured within each cluster at each point in time. No one subject is measured more than once. This is not to be confused with cohort studies i. The methods in this procedure should not be used for cohort or repeated measures designs. This procedure permits sample size and power analysis for studies where the analysis is to done based on the McNemar test. A 2-by-M case-control study investigates a risk factor relevant to the development of a disease.

A population of case patients with a disease and control patients without the disease is considered. Some of these patients have had exposure to a risk factor of interest. A random sample of N case patients is selected. Patients are stratified by the levels of a confounding variable such as age, gender, etc. For each selected case patient, a random sample of M matched control patients is drawn from the same strata group.

An estimate of the odds ratio, OR, of developing the disease in exposed and unexposed patients who have equal values of the confounding variable is desired. This odds ratio is assumed to be constant across all levels of the confounding variables. This procedure permits the user to solve for power, or for the number of cases, or for the number of controls per case.

Cluster-randomized designs are those in which whole clusters of subjects classes, hospitals, communities, etc. This sample size and power procedure is used for the case where the subject responses are binary proportion outcome.