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Free Statistical Software

Free Statistical Software
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Google Analytics Trick: See The Full Referring URL If you use Google Analytics to track your websites stats, you have probably realized that Google doesn’t let you see the full URL of the sites referring traffic to you. That’s a basic feature that somehow is not included in GA but fortunately for us, there is a simple trick you can implement to display the full URL of the referring site. Before I explain you this trick, let me give full credit to Ruben Yau for actually coming up with it. As stated above, Google Analytics doesn’t let you see from what specific page your visitors are coming. For example, instead of showing this as the traffic referrer: it will just show you this: That’s not very useful information… The solution to view the full URL of the referring site is to implement a simple hack that uses filters to place the referrer into the User Defined Report. It should look like this: Now, if you look at your Traffic Sources, you usually see this:

aluMATTER | Aluminium | Recovery | Recovery: Overview The metal after cold working has raised its internal energy and is therefore in a metastable, high energy state. It will try and lower its internal energy. In most cases it needs some external agent to help it to lower its internal energy. Recovery is the earliest onset of re-arrangement of crystal defects in the cold worked microstructure at elevated temperature, where some restoration of the original structure and properties may occur through annihilation of point defects and dislocations and, spatial re-arrangement of dislocations. In the case of deformed metals, point defects and dislocations are created during deformation. In this module, we shall study in detail the microstructural changes that take place during recovery of Al alloys. Learning Outcomes for this Section After completing this section, you should be able to: Pre-Requisites Before starting, it is important that you are familiar with the following terms: climb; cross-slip; dislocation; glide;

Data Preprocessing Tools Advance Macintosh Data Recovery Software and macintosh file retrieval tool for deleted or formatted apple macintosh hard drives. Mac Recovery Software is the most advanced Mac File Recovery application that recovers data from formatted, deleted or corrupted Mac partitions or External mac hard drives. Best Mac Data Recovery Tools is risk-free mac data recovery utility that recovers all important data lost accidental format, virus, file/directory deletion, or even a sabotage. Software fixes damaged mac hard disk and restore mac files within minutes. Platform: Windows Publisher: Date: Size: 1730 KB ADRC Data Recovery Tools v1.0 contains a collection of DIY data recovery tools that supports a wide variety of drives (fixed drives or removable drives) and file systems (FAT12, FAT16, FAT32 and NTFS) for Windows 95/ 98, Windows ME, Windows NT, Windows 2000, Windows XP and Windows 2003 server.The software incorporates extremely simple GUI with novice users in mind.

Research Blog OpenSolver for Excel Do Faster Data Manipulation using These 7 R Packages Introduction Data Manipulation is an inevitable phase of predictive modeling. A robust predictive model can’t be just be built using machine learning algorithms. But, with an approach to understand the business problem, the underlying data, performing required data manipulations and then extracting business insights. Among these several phases of model building, most of the time is usually spent in understanding underlying data and performing required manipulations. What is Data Manipulation ? If you are still confused with this ‘term’, let me explain it to you. Actually, the data collection process can have many loopholes. At times, this stage is also known as data wrangling or data cleaning. Different Ways to Manipulate / Treat Data: There is no right or wrong way in manipulating data, as long as you understand the data and have taken the necessary actions by the end of the exercise. Usually, beginners on R find themselves comfortable manipulating data using inbuilt base R functions. #or

LingPipe Home How Can We Help You? Get the latest version: Free and Paid Licenses/DownloadsLearn how to use LingPipe: Tutorials Get expert help using LingPipe: Services Join us on Facebook What is LingPipe? LingPipe is tool kit for processing text using computational linguistics. Find the names of people, organizations or locations in newsAutomatically classify Twitter search results into categoriesSuggest correct spellings of queries To get a better idea of the range of possible LingPipe uses, visit our tutorials and sandbox. Architecture LingPipe's architecture is designed to be efficient, scalable, reusable, and robust. Java API with source code and unit tests; multi-lingual, multi-domain, multi-genre models; training with new data for new tasks; n-best output with statistical confidence estimates; online training (learn-a-little, tag-a-little); thread-safe models and decoders for concurrent-read exclusive-write (CREW) synchronization; and character encoding-sensitive I/O.

Metallurgical Solutions Introduction to Principal Component Analysis (PCA) - Laura Diane Hamilton Principal Component Analysis (PCA) is a dimensionality-reduction technique that is often used to transform a high-dimensional dataset into a smaller-dimensional subspace prior to running a machine learning algorithm on the data. When should you use PCA? It is often helpful to use a dimensionality-reduction technique such as PCA prior to performing machine learning because: Reducing the dimensionality of the dataset reduces the size of the space on which k-nearest-neighbors (kNN) must calculate distance, which improve the performance of kNN. Reducing the dimensionality of the dataset reduces the number of degrees of freedom of the hypothesis, which reduces the risk of overfitting. What does PCA do? Principal Component Analysis does just what it advertises; it finds the principal components of the dataset. Can you ELI5? Let’s say your original dataset has two variables, x1 and x2: Now, we want to identify the first principal component that has explains the highest amount of variance.

How Your Data Can Predict The Future Define and solve a problem by using Solver Solver is part of a suite of commands sometimes called what-if analysis (what-if analysis: A process of changing the values in cells to see how those changes affect the outcome of formulas on the worksheet. For example, varying the interest rate that is used in an amortization table to determine the amount of the payments.) tools. With Solver, you can find an optimal (maximum or minimum) value for a formula (formula: A sequence of values, cell references, names, functions, or operators in a cell that together produce a new value. A formula always begins with an equal sign (=).) in one cell — called the objective cell — subject to constraints, or limits, on the values of other formula cells on a worksheet. Solver works with a group of cells, called decision variables or simply variable cells, that participate in computing the formulas in the objective and constraint cells. In this article Overview Use Solver to determine the maximum or minimum value of one cell by changing other cells.

Downloadable Sample SPSS Data Files Downloadable Sample SPSS Data Files Data QualityEnsure that required fields contain data.Ensure that the required homicide (09A, 09B, 09C) offense segment data fields are complete.Ensure that the required homicide (09A, 09B, 09C) victim segment data fields are complete.Ensure that offenses coded as occurring at midnight are correctEnsure that victim variables are reported where required and are correct when reported but not required. Standardizing the Display of IBR Data: An Examination of NIBRS ElementsTime of Juvenile Firearm ViolenceTime of Day of Personal Robberies by Type of LocationIncidents on School Property by HourTemporal Distribution of Sexual Assault Within Victim Age CategoriesLocation of Juvenile and Adult Property Crime VictimizationsRobberies by LocationFrequency Distribution for Victim-Offender Relationship by Offender and Older Age Groups and Location Analysis ExamplesFBI's Analysis of RobberyFBI's Analysis of Motor Vehicle Theft Using Survival Model