Update: The trouble-shooter can now make the necessary changes for you, so if you're experiencing this problem and are unsure what to do simply run the trouble-shooter to fix the problem.
Microsoft has recently released updates to both Windows (the April 1803 update) and Microsoft Office 2016 to provide support for multiple monitor high DPI (dots-per-inch) displays.
In the early days of Microsoft Windows, monitors were assumed to have 96 DPI and all applications worked on that assumption: with a user interface fixed on that assumption. In the last 15 years, monitors with higher DPI have started to appear with the benefit that on-screen text and graphics look much smoother because there are so many more dots per inch. That caused problems for many applications which were fixed to assume 96 DPI, causing their user interface to scale improperly on high-DPI monitors. Applications like Analyse-it supported high DPI monitors and adjusted their user interface appropriately... until now.
These days many of us have 2 or more monitors hooked up to a PC as it's a much quicker and more efficient way to work. Often these monitors are different sizes and often have different DPI, and the recent changes to Windows and Microsoft Office are designed to address varying DPIs. Instead of applications scaling their user-interface to one system-wide DPI setting, they now scale according to the monitor application is displayed on. If you have the latest updates to Windows and Office, and drag the Microsoft Excel main window between monitors with different DPIs you will see the application user interface rescale properly for the monitor.
This is a major new improvement and under the hood there are some major changes to Microsoft Windows to support it. Unfortunately, as with all major changes to Windows, it breaks some existing applications, especially add-ins like Analyse-it that are at the mercy of what the host application (Microsoft Excel in this case) decides to do with respect to user interface scaling. In the case of Analyse-it, when the new multiple monitor DPI awareness feature is enabled in Microsoft Excel 2016 the Analyse-it user interface does not appear properly in the task panes.
We are now working on a fix for this issue, but in the meantime, if you are experiencing this problem follow these steps to temporarily disable multiple monitor DPI awareness in Excel:
We will update this blog post when we have a fix available.
Prediction intervals on Deming regression are a major new feature in the Analyse-it Method Validation Edition version 4.90, just released.
A prediction interval is an interval that has a given probability of including a future observation(s). They are very useful in method validation for testing the commutability of reference materials or processed samples with patient samples. Two CLSI protocols, and both use prediction intervals.
Often we collect a sample of data not to make statements about that particular sample but to generalize our statements to say something about the population. Estimation is the process of making inferences about an unknown population parameter from a random sample drawn from the population of interest. An estimator is a method for arriving at an estimate of the value of an unknown parameter. Often there are many competing estimators for the population parameter that differ based on the underlying statistical theory.
As we mentioned last week in the , in this release we took the opportunity to revamp the documentation.
The revamp involved rewriting many topics to make the content clearer, adding new task-oriented topics, including refresher topics on common statistical concepts, and improving the indexing and links between topics so you can more easily navigate the help system.
The new task-oriented topics give you step-by-step instructions on completing common tasks. For example you will now find topics on how to , , , and even simple tasks like . We have also fully documented the supported dataset layouts for each type of analysis so you can see how to arrange your data for Analyse-it. The links in each topic help you more easily find related topics, for example links to topics on how to interpret the statistics, links to explain the pros and cons of the available statistical tests, links to topics for common tasks, and a link showing you how to arrange the dataset.
Last week we released version 4.80 of Analyse-it.
The new release includes multi-way , , and in the Standard edition, and since every licence includes the Standard edition, these features are available to all users. We also took the opportunity to revamp the and develop a . We’ll go into more details on the improvements in the next few weeks.
If you have you can download and install the update now, see . If maintenance on your license has expired you can renew it to get this update and forthcoming updates, see .
Today we released version 3.80 of the Analyse-it Standard edition.
The new release includes Principal Component Analysis (PCA), an extension to the multivariate analysis already available in Analyse-it. It also includes probably the most advanced implementation of biplots available in any commercial package.
New features include:
The tutorial walks you through a guided example looking at how to use correlation and principal component analysis to discover the underlying relationships in data about New York Neighbourhoods. It demonstrates the amazing new features and helps you understand how to use them. You can either follow the tutorial yourself, at your own pace, or .
If you you will no doubt already know about the recent improvements in the Analyse-it Method Validation edition and the release of our first video tutorial. If not, now is a good time to since we post short announcements and feature previews on Facebook, and use the blog only for news about major releases.
The latest changes and improvements to the Analyse-it Method Validation edition include:
What is a sample quantile or percentile? Take the 0.25 quantile (also known as the 25th percentile, or 1st quartile) -- it defines the value (let’s call it x) for a random variable, such that the probability that a random observation of the variable is less than x is 0.25 (25% chance).
A simple question, with a simple definition? The problem is calculating quantiles. The formulas are simple enough, but a take a quick look on Wikipedia and you’ll see there are at least 9 alternative methods . Consequently, statistical packages use different formulas to calculate quantiles. And we're sometimes asked why the quantiles calculated by Analyse-it sometimes don’t agree with Excel, SAS, or R.
Yesterday we improved the help in the and added a statistical reference guide. The guide tells you about the statistical procedures in Analyse-it, with help on using and understanding the plots and statistics. It’s a work in progress, and we intend to improve it further with your comments and feedback, but it’s important to understand the role of the guide.
Firstly, the guide is not intended to be a statistics textbook. While it covers key concepts in statistical analysis, it is no substitute for learning statistics from a good teacher or textbook.
In clearly titling this blog post, we’ve probably already revealed the answer, but... Can you spot the difference between the two rows of values in the Excel spreadsheet shown below?
Sorry, it’s a trick question, because (visually) there is no difference. The difference is how the values are stored by Microsoft Excel. The value 57 in the cell on second row is actually stored as a text string, not a number.
Today we’re delighted to publish the second case study into the use of Analyse-it.
The case study features a national clinical laboratory in the USA that offers more than 2,000 tests and combinations to major commercial and government laboratories. They use Analyse-it to determine analytical performance of automated immunoassays for some of the industry’s leading in-vitro diagnostic device makers -- including Abbott Diagnostics, Bayer Diagnostics, Beckman Coulter and Roche Diagnostics.
In a previous post, , we explained the tests provided in Analyse-it to determine if a sample has normal distribution. In that post, we mentioned that although hypothesis tests are useful you should not solely rely on them. You should always look at the histogram and, maybe more importantly, the normal plot.
The beauty of the normal plot is that it is designed specifically for judging normality. The plot is very easy to interpret and lets you see where the sample deviates from normality.
A customer contacted us last week to ask how to refer to cells on an Analyse-it report worksheet, from a formula on another worksheet. The customer often used Analyse-it's refresh feature, to repeat the statistical analysis and update the statistics, and direct references to cells on the report were being lost on refresh.
As an example, suppose you have used Analyse-it linear regression to calculate the linear relationship between installation cost and the number of employees required, distance to the site, and the cost of machine being installed. Analyse-it would calculate the effect of each variable on the final cost, technically known as regression coefficients, which you can then use to predict installation costs for jobs in future.
Today we’re delighted to publish the first case study into the use of Analyse-it.
Marco Balerna Ph.D., a Clinical Chemist at the in Switzerland, used Analyse-it when replacing the clinical chemistry and immunological analysers in EOC’s laboratories.
Since the EOC provides clinical chemistry services to five large hospitals and three small clinics in the region, it was essential the transition to the new analysers went smoothly. Marco used Analyse-it to ensure the analyser’s performance met the manufacturer’s claims, to ensure the reporting of patient results was not affected, and to comply with the regulations of the EOC’s accreditation.
Although the charts in Analyse-it are large so they’re easy to read when printed, sometimes you need to print a chart to fill the full page. You can do so easily, without resizing the chart, in just a few steps:
Chart size is only limited by the page size your printer supports.
Identifying what was analysed, when, and by who, is the first step in understanding any Analyse-it report. The top rows of each Analyse-it report provide you with this information. The statistical test used, dataset and variables analysed, user who analysed, and the date and time last analysed, are included (see below). When you print the report the header is repeated at the top of printed page.
In May this year, we surveyed users of the Analyse-it Method Evaluation edition to gain insight into how we can improve Analyse-it in future. Thank you to all those who responded.
In the responses, one issue became clear: the unfiled reports feature causes confusion.
When you run an analysis, Analyse-it creates a new worksheet containing the statistics and charts for that analysis (what we call a report). Analyse-it places the report in a temporary workbook called . From there you can then decide what you want to do with the analysis: keep it, print it, e-mail it, or discard it. If you want to keep it you click the (see below), and Analyse-it moves the report into the same workbook as your dataset.
The most used distribution in statistical analysis is the normal distribution. Sometimes called the Gaussian distribution, after , the normal distribution is the basis of much parametric statistical analysis.
Parametric statistical tests often assume the sample under test is from a population with normal distribution. By making this assumption about the data, parametric tests are more powerful than their equivalent non-parametric counterparts and can detect differences with smaller sample sizes, or detect smaller differences with the same sample size.
For new and occasional Analyse-it users, datasets can sometimes seem confusing. Today we’ll explain why we devised the 'dataset' concept, a concept now copied by some other Excel add-ins.
We introduced the dataset concept so Analyse-it could automatically pick-up the data and variables from your Excel worksheet. As we found with , the Analysis Toolpak, and other Excel add-ins, forcing you to select cells containing the data to be analysed can be problematic:
A few readers have e-mailed to ask for more information about the book by David J. Sheskin we alluded to in the comment reply re: the , last week.
The book is the Handbook of Parametric & Non-parametric Statistical procedures, by David J. Sheskin, ISBN: 1584888148.
We have the third edition of the book which runs to over 1,200 pages -- a phenomenal piece of work for a single (obviously very dedicated) author. While it’s not a book you would sit down and read cover-to-cover, it is a very readable reference guide, covering all the parametric and non-parametric statistical procedures included in Analyse-it.
Most of you know where to find the help and examples provided with Analyse-it, but if not, today we’d like to explain what’s available. If you're stuck we're always happy to help, and usually respond within a few hours, but it's always faster for you to check if the help answers your question first.
If you’re new to Analyse-it, or want a quick refresher, the best place to start is the Getting Started tutorial. It’s completely automated, no typing is required, so all you have to do is sit back and watch. In just 10 minutes it will demonstrate how to setup a dataset, how to filter the dataset, how to run a statistical test, and how to edit, refresh, and print the reports.