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You'll see the good, and the bad - negative reviews are never deleted. Reviews can't be edited.The results of the submissions should be replicable. Applications consisting only of routine calculations are not of interest to the journal. Papers providing important original contributions to methodological statistics inspired in applications are considered for this section.

Papers dealing, directly or indirectly, with computational and technical elements are particularly encouraged. Innovative algorithmic developments are also of interest, as are the computer programs and the computational environments that implement them as a complement. The journal consists, preponderantly, of original research. Occasionally, review and short papers from experts are published, which may be accompanied by discussions.

Special issues and sections within important areas of research are occasionally published. The journal publishes as a supplement The Annals of Computational and Financial Econometrics.

Advertising - Careers - Feedback - Site map - Terms and Conditions - Privacy PolicyCookies are used by this site. Menu Search Search Search in: All Webpages Books Journals Home Journals Econometrics and Statistics ISSN: 2452-3062 Econometrics and Statistics Supports Open Access Editors: E. Colubi: View Editorial Board Submit Your Paper Enter your login details below. This free service is available to anyone who has published and whose publication is in Scopus. Publishing Campus Author Services Try out personalized alert features Econometrics and Statistics is the official journal of the networks Computational and Financial Econometrics and Computational and Methodological Statistics.

Read more Econometrics and Statistics is the official journal of the networks Computational and Financial Econometrics and Computational and Methodological Statistics. Ahlgren View All Articles Recent Articles Recently published articles from Econometrics and Statistics Most Cited Articles The most cited articles published since 2012, extracted from Scopus.

Recent Open Access Articles The latest Open Access articles published in Econometrics and Statistics. Advertising - Careers - Feedback - Site map - Terms and Conditions - Privacy Policy Cookies are used by this site. Probably the most often used descriptive statistic is the mean. The mean is a particularly informative measure of the "central tendency" of the variable if it is reported along with its confidence intervals.

As mentioned earlier, usually we are interested in statistics (such as the mean) from our sample only to the extent to which they can infer information about the population. The confidence intervals for the mean give us a range of values around the mean where we expect the "true" (population) mean is located (with a given level of certainty, see also Elementary Concepts). Note that the width of the confidence interval depends on the sample size and on the variation of data values.

The larger the sample size, the more reliable its mean. The larger the variation, the less reliable the mean (see also Elementary Concepts). The calculation of confidence intervals is based on the assumption that the variable is normally distributed in the population.

Shape of the Distribution, Normality. An important aspect of the "description" of a variable is the shape of its distribution, which tells you the frequency of values from different ranges of the variable.

Typically, a researcher is interested in how well the distribution can be approximated by the normal distribution (see the animation below for an example of this distribution) (see also Elementary Concepts). Simple descriptive statistics can provide some information relevant to this issue. For example, if the skewness (which measures the deviation of the distribution from symmetry) is clearly different from 0, then that distribution is asymmetrical, while normal distributions are perfectly symmetrical.

More precise information can be obtained by performing one of the tests of normality to determine the probability that the sample came from a normally distributed population of observations (e. However, none of these tests can entirely substitute for a visual examination of the data using a histogram (i. The graph allows you to evaluate the normality of the empirical distribution because it also shows the normal curve superimposed over the histogram.

It also allows you to examine various aspects of the distribution qualitatively. For example, the distribution could be bimodal (have 2 peaks). This might suggest that the sample is not homogeneous but possibly its elements came from two different populations, each more or less normally distributed. In such cases, in order to understand the nature of the variable in question, you should look for a way to quantitatively identify the two sub-samples.Management's and shareholders' interests seem to be at odds.

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Everything is explained in a really clear way. Films are great too and help in understanding issues. I very much enjoyed the Stats 1 and 2 classes, and I think your book and approach really are excellent. I have taken a variety of stats-type classes, including calculus-based probability, but the re-sampling gave me a much more intuitive understanding than any other approach.

I certainly feel that I got my money's worth from this course and this was almost entirely due to the prompt, thoughtful and expert comments of Brady and Andrzej. I liked the way this course is presented. In every assignment we needed to think logically to produce the required plots and that's what I liked.

Thanks for such a wonderful course. It was great to get a better sense of the Python language. The course gave a great overview of the data analysis and visualization tools. The lecture notes were clear and concise.

The instructor did a great job in covering many different topics in such a short course. This course definitely adds another dimension to my map visualization capabilities other than just relying on QGIS. I greatly appreciated learning about SEM from a foremost expert of the subject.

I now have a more thorough understanding of SEM from theoretical and practical perspectives. I found this a very good grounding the the statistical theory, but also there were good discussions about some of the practical issues. The introduction to the various software packages was also very helpful. Every professor has unique and great qualities in them and the unique and great quality that I found in Prof Anthony Babinec is that he can write pages of explanation for each and every line in the book.

He is an awesome professor. He explained each and every doubts we had to the perfection. I feel this class gave me a much better understanding of how to design experiments and enhanced my statistical vocabulary. One thing I liked about Mr. Babinec's approach was that his written explanations were precise.

Overall, I enjoyed the class and it was well worth the time spent. Instructor John Verzani provided timely and detailed responses to each and every question posted on the forum. His depth of knowledge in both statistics and R and his passion for teaching are the major contribution to the success of this class.

I took this class out of general curiosity and the fact that it used Python, which is increasingly attractive to me in building my own software tools. To my delighted surprise I found the course to be one of the very best (out of more than 20) I have taken at Statistics.

It proved a harmonious blend of theory (buttressed by relevant reading and videos by the instructor), focused homework exercises, and training in Python coding. As a result I learned much and enjoyed the process greatly. I was particularly impressed with the instructor's choice of homework assignments, which fascinated me, and with the importance he placed in students writing clear, even elegant, Python code.

This is my fifth course using Python at Statistics. Allan Leis is a seasoned professional, working in the field, and it was a luxury having him cast an experienced gaze on my rudimentary code. It's the kind of thing that would never happen in a MOOC and shows the value of a course such as this.Read the feature highlight summary below, and check out the Visual Studio 2017 version 15.

The CPU Usage tool (available during F5 Debugging in the Diagnostic Tools window and in the ALT-F2 Performance Profiler) now displays source line highlighting based on the CPU consumption of specific lines of code. This feature requires that source information be included in the generated PDB which is controlled by the project settings. Projects for which PDBs do not have source information will be unable to display either the line attribution or the source file. In addition to create, you can now delete tags, push tags, and create a new branch from tags.

Visual Studio Team Services users can now check out pull request branches, which makes it easier to review pull requests, test changes, and build your code. Last month we introduced the App Authentication Extension which makes it easy to configure your machine to use these protected settings so that you can develop and debug apps locally using your Visual Studio credentials.

With Visual Studio Version 15. Learn more about managing secrets in the cloud. With Visual Studio 2017 version 15.

In addition, the ImageWatch extension has been updated to work with Visual Studio 2017IntelliSense for Python code now no longer requires a completion database. Instead of waiting up to four hours after installing a new package, you can start using it immediately.

We have also added experimental support for managing Anaconda packages, new code snippets, and more customizable syntax highlighting. Read our blog post for full details on these improvements and how to enable our experimental features.

Real time test discovery is a new Visual Studio feature for managed projects that uses the Roslyn compiler to discover tests and populate the Test Explorer in real-time without requiring you to build your project. This feature was introduced behind a feature flag in version 15. This feature not only makes test discovery significantly faster, but it also keeps the Test Explorer in sync with code changes such as adding or removing tests.

To learn more, check out the Real Time Test Discovery blog post and Channel9 video. With this Preview, Visual Studio now supports configuring continuous delivery to Azure for Team Foundation Version Control (TFVC), Git SSH remotes, and Web Apps for containers. Read more about these features on this post about Continuous Delivery Tools for Visual Studio.

The WCF Web Service Reference connected service provider now supports updating an existing service reference.

This simplifies the process for regenerating the WCF client proxy code for an updated web service. This will regenerate the Reference. Please note, this feature is not supported for service references added to the project using a previous version of Visual Studio 2017. Remember that Visual Studio 2017 Previews can be installed with other versions of Visual Studio and other installs of Visual Studio 2017 without adversely affecting either your machine or your productivity.

Similarly, the Previews enable the Visual Studio Engineering team to validate usage, incorporate suggestions, and detect flaws earlier in the development process. We are highly responsive to feedback coming in through the Previews and look forward to hearing from you.

Please Install the Visual Studio 2017 Preview today, exercise your favorite workloads, and tell us what you think. You can report issues to us via the Report a Problem tool in Visual Studio or you can share a suggestion on UserVoice.

You can also engage with us and other Visual Studio developers through our Visual Studio conversation in the Gitter community (requires GitHub account).

Christine is on the Visual Studio release engineering team and is responsible for making Visual Studio releases available to our customers around the world. Back totop Download Visual Studio Subscribe Facebook Twitter YouTube Related Resources Visual Studio Product Website Visual Studio Developer Center Getting Started Resources Write, Navigate, Fix your Code Debug, Profile, Diagnose your Code Search this blog Search all blogs jQuery(document).

NET Framework announcement Apache Cordova ASP. By continuing to browse this site, you agree to this use. NET with Beth Massi ASP. NET by Scott Hanselman OData Team WPF Platform Development Apps for Windows Bing Edge Microsoft Azure Office 365 Development Web Data Development SQL Server SQL Server Data Tools Cosmos DB Revolutions R The Visual Studio Blog Visual Studio 2017 Version 15. Diagnostics The CPU Usage tool (available during F5 Debugging in the Diagnostic Tools window and in the ALT-F2 Performance Profiler) now displays source line highlighting based on the CPU consumption of specific lines of code.


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