Analyse-it

Using Excel filter to analyse a subset of the dataset

Thu, 14 Aug 2008 12:40:19 GMT

While answering a customer e-mail earlier this week, it occurred to us that some Analyse-it users don’t know how to filter data with Analyse-it. Filtering lets you restrict analysis to just a subset of your dataset, for example females only, or females over 40 years old, and is invaluable for exploratory data analysis.

How does Analyse determine what to analyse?

To let you analyse a subset of your dataset, Analyse-it only extracts the visible data from the Excel worksheet. Analyse-it ignores observations on hidden rows, and does not include them in analysis.

You can manually hide rows on the worksheet to exclude them from analysis. Simply highlight the worksheet rows to hide, right-click over the selection, then choose Hide from the pop-up menu.

More often though you want to filter to specific criteria, such as Females only, or females over age 40. Rather than hide the rows containing those cases, it’s easier to use Excel’s built-in filter. Called AutoFilter until Excel 2007, filter lets you choose the values to filter to, such as Male or Female, or use a simple boolean expression, such as values greater-than 40. Excel then hides the rows that don’t match the criteria – excluding them from analysis by Analyse-it.

Using filter to analyse a subset

You can use filter with any Analyse-it dataset in list format. List format simply means each variable is in a separate column. The top-most cell contains the variable name, and the observations follow in the column below. See the screenshot below for an example.

You can enable filter on any list dataset:

Click a cell in the dataset.
Click Filter on the Analyse-it toolbar to enable filter on the dataset. Click Filter again if you want to remove the filter, and show all the data in the dataset.
Click the drop-down button to the right of the variable you want to filter. You can choose individual values to filter to, or use a boolean condition.

For example, to show just females, click the drop-down button next to Sex, tick the checkbox alongside Female, and then click OK.

To filter to cases over 40 years old, click the drop-down button next to Age, then choose Number Filters, Greater Than, enter 40, and then click OK.
Any subsequent analysis with Analyse-it will analyse only the visible rows.

You can use filter on any variable in the dataset – not just the variables you’re analysing. In the above example, you could filter the dataset to females over 40 years old and then use the Mann-Whitney test to compare salary for those with Higher and Standard education.

The only limitation, imposed by Excel, is you can only use filter on one dataset at a time on each worksheet. This isn’t a problem if you use separate worksheets for each dataset.

Using filter to choose the groups to test

You can use filter to choose the groups to analyse in two-sample tests such as the Independent t-test, F-test, or Mann-Whitney test. Each test expects your dataset to contain two groups. When there are three or more, and you want to analyse just two groups, you can use filter so Analyse-it only finds the two groups in which you’re interested.

As an example, imagine you’ve measured a sample of widget weights from three production lines: A, B & C. Let’s assume the operator on production line B weighed the wrong widgets, making the data useless for comparison with A & C. Rather than abandon the analysis you might decide to compare the weights from lines A & C only:

Select a cell in the dataset
Click Filter on the Analyse-it toolbar to enable filter on the dataset.
Click the drop-down button next to Production line, tick A, C, clear B, and then click OK.
Choose Compare Groups > Mann-Whitney from the Analyse-it toolbar, and then choose to analyse Weight by Factory. Analyse-it recognises you want to compare just the two production lines, A vs. C, as you can see in the Alternative Hypothesis below:
Because of the filter, Analyse-it will only compare the weights for production line A vs. C, ignoring the data hidden for line B.

In Analyse-it 3 we’ve choosing the groups to analyse easier. Instead of using filter you can choose the groups to analyse when you run the test. Simply click the Filter button alongside the factor, then choose the groups to analyse:

What happens when updating a report?

When we developed Analyse-it, the question arose of what to do when updating a report. Should the same subset of data be analysed, using the same filter criteria to select what's analysed? Or should the dataset be analysed as it currently stands, using whatever filter is in effect at the time.

At the time we had no choice. Microsoft Excel didn’t provide programmatic access to the filter currently in effect. There was no way to see if the data was filtered, never mind how to get or set the filter in effect. We had no choice than to make Analyse-it work with the subset of data currently visible.

All versions of Analyse-it up to now have worked the same. When you click Refresh report, Analyse-it repeats the analysis with the data visible in the dataset. The benefit is you can analyse a subset of data, change the filter, and then update the analysis to see the effect. The downside is most users expect update to repeat analysis with the same data – taking account of new or changed data – but still using the same filter criteria.

The latest versions of Microsoft Excel do let add-ins like Analyse-it access the current filter settings. In Analyse-it 3, we’re planning to save the filter in effect when you run a test, and then let you choose if you want to re-apply it when updating a report. We’ve yet to resolve some issues, but generally think this approach is best. What do you think?

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

Testing the assumption of normality

Thu, 07 Aug 2008 15:02:50 GMT

The most used distribution in statistical analysis is the normal distribution. Sometimes called the Gaussian distribution, after Carl Friedrich Gauss, 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.

When to check sample distribution

It’s vital you ensure the assumptions of a parametric test are met before use.

If you’re unsure of the underlying distribution of the sample, you should check it.

Only when you know the sample under test comes from a population with normal distribution – meaning the sample will also have normal distribution – should you consider skipping the normality check.

Many variables in nature naturally follow the normal distribution, for example, biological variables such as blood pressure, serum cholesterol, height and weight. You could choose to skip the normality check these in cases, though it’s always wise to check the sample distribution.

How to check the sample distribution

You can use a statistical test and or statistical plots to check the sample distribution is normal. Analyse-it includes three statistical tests for testing normality:

Kolmogorov-Smirnov test
An EDF-type test based on the largest vertical distance between the normal cumulative distribution function (CDF) and the sample cumulative frequency distribution (commonly called the ECDF – empirical cumulative distribution function).

It has poor power to detect non-normality compared to the tests below. D’Agostino and Stephens say the Kolmogorov-Smirnov test is now really only of historical interest.
Anderson-Darling test
An EDF-type test similar to the Kolmogorov-Smirnov test, except it uses the sum of the weighted squared vertical distances between the normal cumulative distribution function and the sample cumulative frequency distribution. More weight is applied at the tails, so the test is better able to detect non-normality in the tails of the distribution.
Shapiro-Wilk test
A regression-type test that uses the correlation of sample order statistics (the sample values arranged in ascending order) with those of a normal distribution.

It’s the most powerful normality test available and is able to detect small departures from normality.

The only limitation is it’s not suitable for very large sample sizes. Analyse-it uses the latest algorithm and supports use on samples up to 5,000 observations, but some software limits use to 2,000, or as few as 50, observations.

While normality tests are useful, they aren’t infallible.

You shouldn’t rely on a normality test to exclusively to judge normality. You should look at the Normal Q-Q plot, or Frequency histogram with normal overlay, to double-check the distribution is roughly Normal. The plots will also tell you why a sample fails the normality test, for example due to skew, bimodality, or heavy tails.

Small and large samples can also cause problems for the normality tests.

With small sample sizes of 10 or fewer observations it’s unlikely the normality test will detect non-normality. If you know the population distribution is normal you should still use a parametric test, as it’s more powerful, but if you’re unsure a non-parametric alternative is usually more conservative.

Conversely, for large samples, for example 1000 observations or more, the normality test might conclude a small deviation from normality is significant. You should look at the normal QQ plot to see if the deviation from normality really is significant.

Many parametric tests, such as the t-test and ANOVA, use the mean of the sample so some non-normality can be tolerated (due to the Central Limit Theorem). How large a sample you need depends on how skewed the sample distribution is – the more skewed the data, the larger the sample size should be – so it’s not possible to give hard and fast rules. You should first check the degree of non-normality and, only after (careful!) consideration, decide if you can safely use the test.

Using Analyse-it to check normality

Analyse-it provides the normality tests, Normal Q-Q plot and Frequency histogram mentioned above. All are included on the single sample summary statistics (that’s a tongue twister!) report.

To display detailed summary statistics, plots, and the normality test for a sample:

Choose Describe > Summary from the Analyse-it toolbar.
Select the Variable to test.
Select the normality test to use from the Normality Test drop-down selector. When you choose a normality test, Analyse-it assumes you are checking normality and will show Normal Q-Q plot.
Tick Overlay Normal distribution, below Histogram, to show the ideal normal distribution superimposed over the histogram bars. You can then judge the histogram bar-heights against the ideal Normal distribution.
Click OK.

In the forthcoming Analyse-it 3.0 we’ve also made the normality tests available separately, directly from the Describe (to be renamed Distribution) menu.

How to interpret the normality test

Since the normality tests included in Analyse-it are all hypothesis tests, they test a null against alternative hypothesis. For each test, the null hypothesis states the sample has a normal distribution, against alternative hypothesis that it is non-normal.

The p-value tells you the probability of incorrectly rejecting the null hypothesis.

When it’s significant (usually when less-than 0.10 or less than 0.05) you should reject the null hypothesis and conclude the sample is not normally distributed.

When it is not significant (greater-than 0.10 or 0.05), there isn’t enough evidence to reject the null hypothesis and you can only assume the sample is normally distributed. However, as noted above, you should always double-check the distribution is normal using the Normal Q-Q plot and Frequency histogram.

On a technical note: Since we developed Analyse-it over 10 years ago, a few users have asked about the p-values calculated by Analyse-it. When calculating the p-value, Analyse-it assumes the mean and standard deviation of the population are unknown and instead estimates them from the sample. Some software packages don’t make this assumption, and go on to calculate incorrect p-values.

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

The Analyse-it "Dataset" concept

Wed, 30 Jul 2008 12:42:04 GMT

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.

Why can’t I select the cells containing the data to analysed?

We introduced the dataset concept so Analyse-it could automatically pick-up the data and variables from your Excel worksheet. As we found with Astute, the Analysis Toolpak, and other Excel add-ins, forcing you to select cells containing the data to be analysed can be problematic:

Selecting cells can be tedious.
When you need to run a few statistical tests you have to select the same cells, again and again. When there are lots of cells to select you have to wait while Excel auto-scrolls the off-screen cells into view so you can select them.
Selecting cells can be error-prone.
It’s easy to select the wrong cells, too many cells, or too few cells. You have to carefully select cells, otherwise the statistics calculated won't be valid.
Selecting a subset of the data often means re-organising and sorting the data.
Most Excel add-ins require the data be in a rectangular contiguous range of cells. You might have to filter then sort it, or maybe even re-arrange it manually, to bring data together into a selectable rectangular block of cells.
Adding or removing cases and observations isn't easy.
You have to run the statistical test again and re-select the cells again, now to include new data you’ve added, or exclude data you’ve removed.

Statistics software is supposed to simplify what’s already a complex and error-prone subject area. Forcing you to select ranges of cells just seemed, to us, to introduce more potential for errors.

What are the benefits of the Analyse-it way?

In Analyse-it we tried to solve all these problems. We wanted the software to do more of the work, eliminating the need to select or re-organise your data. You should be able to:

Select variables by name, not by selecting cells.
Statistics and charts can then refer to variables by name, instead of cryptic meaning-less cell references. Which is clearer? “1-way ANOVA of Yield by Fertiliser”, or “1-way ANOVA of Sheet1!$A$2:$A$350, Sheet1!$C$2:C$350”?
Add, remove, or correct observations or cases in the dataset, and then easily re-run the analysis.
The software should automatically recognise when data is added, removed, or changed. You should be able to update any statistics and charts to reflect the new data, without running the test and selecting data again.
Slice and dice the data, to filter and limit analysis to a subset of data.
Excel’s built-in AutoFilter provides a quick and easy way to filter data, providing a simple way to analyse a subset of the dataset without you having to re-arrange data on the worksheet.

How does Analyse-it recognise the data for analysis?

These requirements mean Analyse-it has to know exactly how your data is arranged on an Excel worksheet -- which cells contain data for analysis, and which cells contains the variable names.

This is when the real problem with spreadsheets (or benefit, if you like) becomes apparent. Spreadsheets enforce no structure to the data you place in the cells. You can use cells on a worksheet how you like, arranging data anywhere on the sheet, horizontally or vertically, with or without labels for each row/column.

To keep things simple, we chose to support a few layouts commonly used to organise data in Excel. We call these “datasets” and all are immediately recognisable. Over time we've found most users tend to naturally organise their data in the way Analyse-it expects it – experienced Excel users more so. For more information datasets, and how to arrange your data for Analyse-it, see the online help.

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

Handbook of Parametric & Non-parametric Statistical procedures

Tue, 15 Jul 2008 14:17:20 GMT

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 Statistical test advisor, 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.

For beginners the book starts at the very beginning, introducing summary statistics such as the mean, median, then moving on to explain concepts such as measurement scales, central tendency, variability, normal distribution, hypothesis testing, parametric and non-parametric statistics. The text is concise, but is clear, easy to read, and easy to understand -- ideal for anyone needing a refresher course on statistics.

Over 130 statistical tests for univariate and bivariate data are then covered, including ANOVA, Chi-Square, t- test, Mann-Whitney, Wilcoxon Signed Ranks, and many more. The historical background, purpose, use, assumptions and interpretation are explained, with examples to demonstrate real-world use of the test in business, education, life and environment science applications. Again, the text is very easy to read, and the extensive use of examples really helps to demonstrate how the tests are used in practical situations.

For experts the book includes formulas (without resorting to proofs) and explains how to calculate each test and statistic. References to further reading are included but aren’t needed as this book covers each procedure so thoroughly.

Of all the books we've read, this is hands-down, the definitive guide for univariate and bivariate statistical analysis. We haven't found another book so comprehensive and complete, yet easy to read and understand. The author really does succeed in his aim: to provide a comprehensive reference for readers at all levels.

The fourth edition of this book was published last year, with over 500 new pages, now covering topics such as multivariate analysis, clinical trials and survival analysis.

You can read more about the book and buy it online at Amazon (UK) and Amazon (US).

Contents (from the 3rd edition):
Introduction	1
Outline of Inferential Statistical Tests and Measures of Correlation/Association	107
Guidelines and Decision Tables for Selecting the Appropriate Statistical Procedure	113
Inferential Statistical Tests Employed with a Single Sample	119
The Single-Sample z Test	121
The Single-Sample t Test	135
The Single-Sample Chi-Square Test for a Population Variance	161
The Single-Sample Test for Evaluating Population Skewness	173
The Single-Sample Test for Evaluating Population Kurtosis	181
The Wilcoxon Signed-Ranks Test	189
The Kolmogorov-Smirnov Goodness-of-Fit Test for a Single Sample	203
The Chi-Square Goodness-of-Fit Test	219
The Binomial Sign Test for a Single Sample	245
The Single-Sample Runs Test (and Other Tests of Randomness)	337
Inferential Statistical Tests Employed with Two Independent Samples (and Related Measures of Association/Correlation)	373
The t Test for Two Independent Samples	375
The Mann-Whitney U Test	423
The Kolmogorov-Smirnov Test for Two Independent Samples	453
The Siegel-Tukey Test for Equal Variability	465
The Moses Test for Equal Variability	479
The Chi-Square Test for r x c Tables (Test 16a: The Chi-Square Test for Homogeneity; Test 16b: The Chi-Square Test of Independence (employed with a single sample))	493
Inferential Statistical Tests Employed with Two Dependent Samples (and Related Measures of Association/Correlation)	573
The t Test for Two Dependent Samples	575
The Wilcoxon Matched-Pairs Signed-Ranks Test	609
The Binomial Sign Test for Two Dependent Samples	621
The McNemar Test	633
Inferential Statistical Tests Employed with Two or More Independent Samples (and Related Measures of Association/Correlation)	665
The Single-Factor Between-Subjects Analysis of Variance	667
The Kruskal-Wallis One-Way Analysis of Variance by Ranks	757
The Van der Waerden Normal-Scores Test for k Independent Samples	781
Inferential Statistical Tests Employed with Two or More Dependent Samples (and Related Measures of Association/Correlation)	795
The Single-Factor Within-Subjects Analysis of Variance	797
The Friedman Two-Way Analysis of Variance by Ranks	845
The Cochran Q Test	867
Inferential Statistical Test Employed with Factorial Design (and Related Measures of Association/Correlation)	885
The Between-Subjects Factorial Analysis of Variance	887
Measures of Association/Correlation	943
The Pearson Product-Moment Correlation Coefficient	945
Spearman's Rank-Order Correlation Coefficient	1061
Kendall's Tau	1079
Kendall's Coefficient of Concordance	1093
Goodman and Kruskal's Gamma	1109
Appendix: Tables	1123
Index	1175

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

Choosing the correct statistical test

Tue, 08 Jul 2008 09:55:50 GMT

Following our recent announcement that Analyse-it 3 is now in development, the first new feature we’d like to tell you about is the Statistical Test Advisor.

Many Analyse-it users readily admit their statistics knowledge is a little rusty, usually because it’s 10 or more since their last statistics course. Should I use the t-test, Mann-Whitney, or Wilcoxon test? The names of the tests aren’t exactly helpful, nor do they give you any clue of the assumptions that must be met to use the test.

That’s why we’ve devised the Statistical Test Advisor.

It’s a simple interactive wizard that asks what you want to do, what data you’ve observed, checks which pre-test assumptions can be met, then tells you the best statistical test to use. Using the advisor you can be confident you’re using the correct statistical test -- or even use it to check if your statistics knowledge really is as rusty as you think!

Try it for yourself now:

http://www.analyse-it.com/support/advisor/test-advisor.aspx

Bear in mind this is only a simple prototype at the moment. Eventually the advisor will integrate into Analyse-it, leading you to help and tutorials showing you how to use the recommended test and interpret the statistics. You’ll also notice the advisor recommends tests that will be new to Analyse-it 3.

So now it’s over to you. What do you think to the test advisor? Will you find this useful? Are the questions logical? Is the terminology clear or do we need to explain the terms used? Finally, have we missed anything you think should be included?

Let us know what you think, good or bad. Post your comments below.

11 comments | Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

You’ve spelt “analyse” wrong!

Tue, 01 Jul 2008 16:08:34 GMT

Depending on where you’re located, the way we spell “Analyse-it” may intrigue you. We chose the name in 1997 as it sounded active, a direction to analyse it! – similar to many product names of the time.

The name has served us well and hints as to what our business and product offers.

At the time we didn’t think such a simple name would cause so many headaches. Before you wonder, Analyse-it doesn’t mean anything offensive in other languages, but it can be spelt different ways:

If you’re from the US, the obvious spelling is “Analyze-it”. If English isn’t your first language, “Analise-it” or “Analize-it” seems to be the natural spelling. Then there’s the hyphen – with or without? Combined there are 16 variations.

The variations have sometimes caused customers to send e-mail to the wrong company, or made finding our web-site difficult. We’ve also had the occasional e-mail suggesting we can’t spell! One of the not-so polite e-mails went like this:

You’ve spelt “analyse” wrong! “Licence” is wrong too! How can I trust your company and products if you can’t even use spell check!

Ouch! The spelling is a clue to our English heritage (see our location). We’re based in England in the United Kingdom in Europe, and that meant we chose to use the English spelling – analyse, licence – rather than the arguably more logical American-English spelling – analyze, license.

Thankfully most search engines now suggest our web-site regardless of which variation you use. More importantly, we’ve acquired many of the alternative-spelled domain names such as www.analyze-it.com, www.analyzeit.com, with and without the hyphen. Owning many of the variations on the domain name means we now get mis-addressed e-mail (and browsers) – and all the spam for these 8-10 domains!

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

How to get help using Analyse-it

Thu, 26 Jun 2008 15:04:55 GMT

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.

Getting started tutorial

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.

To watch the tutorial:

Browse to http://www.analyse-it.com/userguide/gettingstarted.aspx
The tutorial will start to play. It requires Adobe Flash.
In full it's 10 minutes, but you can jump to any section -- hover over the “Jump to section” link at the top-left of the tutorial, then choose the feature you want to see.

Help when you’re using Analyse-it

The application help provided with Analyse-it is a complete reference covering all aspects of Analyse-it: how to install Analyse-it, start it, layout datasets, manage reports, and how to use the statistical tests. You can either browse the contents to learn about Analyse-it, or use the index or search to quickly find the right topic. Index and search were only recently added to the help, in Analyse-it 2.10, so if you’re not using the latest version download it now.

To open the help:

Start Analyse-it
Click the Help button on the Analyse-it toolbar (see below):
Analyse-it opens the help. Click the Contents, Index, or Search tab (see below) depending on how you want to navigate the help:

Help on the statistical test you’re using

Most questions arise when using a statistical test -- what are the assumptions for this test, how do I use the test, and how do I interpret the statistics? Help topics for every statistical test in Analyse-it are included, explaining what the test does, what the assumptions are, common tasks using the test, and references to further reading on how the test implemented and the statistics calculated.

To get help for the test you’re using:

Start Analyse-it
Choose the test you want to use from the Analyse-it toolbar, or click Edit on the Analyse-it toolbar if you have an Analyse-it report worksheet active.
Click the Help button on the test dialog box shown (see below):
Analyse-it opens the help topic for the test.

Learning by example

Sometimes it’s easier to learn from an example -- actually seeing how it's done is easier than reading about it. If you’re stuck and want to see an example dataset and report that you can then use as a template, take a look at the examples. We’ve included sample datasets and reports for almost every statistical test in Analyse-it.

To access the examples:

Start Analyse-it
On the Analyse-it toolbar click the Further Resources button then choose Examples (see below):
Analyse-it opens the examples folder, containing the example Excel workbooks.

In future we plan to blog about how to use specific features and statistical tests in Analyse-it. Your comments on these topics will help us ensure we concentrate our time and efforts on problem areas. Post a comment now and tell us what you think of the help and, more importantly, where we can improve it.

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

Analyse-it 3.0 now in development

Thu, 19 Jun 2008 13:35:26 GMT

One of the primary reasons we launched the blog is to let you know what we’re currently working on, and give you to opportunity to feedback and influence the development.

At the moment we're spending most of our time developing version 3.0 of the Analyse-it Standard edition. The improvements are based on what you've asked us to include, and through insights gleaned from recent customer surveys. Improvements and new features will include:

Test advisor to help choose the most appropriate statistical test – handy, if your statistics knowledge is a bit rusty.
New and improved statistical tests – including logistic regression, improved ANOVA with post-hoc tests, new regression fits.
Simpler dataset setup – without the need to set measurement scales before running a test.
Transform variables, and easily choose groups to analyse, when running a test.
Clearer hypotheses and automatic interpretation of p-values.
Improved help with step-by-step instructions for each test.

Version 3.0 will be a major upgrade and will be released later this year. As per our upgrade policy, you'll get the upgrade free if you purchased your licence within 2-years of the release date – so if you're thinking of buying a licence now you will get the upgrade free. If you don't qualify for the free upgrade you'll be able to buy the upgrade for a small cost, necessary to cover development costs. We'll announce final pricing and upgrade pricing nearer release.

In the next few weeks we’ll post more about the new features, including screenshots, so you get a chance to comment on individual features. We'll then incorporate any requests and suggestions into the final version before release.

Start now by posting a comment to let us know what you’d like to see included in Analyse-it 3.0.

8 comments | Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

Analyse-it 2.11 released

Mon, 16 Jun 2008 12:20:22 GMT

Last Friday we released the latest update to Analyse-it, version 2.11. It’s a minor update providing minor fixes to issues recently reported by customers.

The update is available free to everyone, including users still using Analyse-it version 1.

In fact if you’re still using Analyse-it version 1 you should get the update right away. You’ll be impressed with the improvements to both the application and help, and if you’re using Excel 2007 you’ll love the slick Analyse-it RibbonBar .

If you’re unsure which version of Analyse-it you’re using, see our FAQ: How to find which version of Analyse-it you’re using.

How do I download and install the update?

Analyse-it automatically checks for updates every 15 days on start-up, and will tell you if an update is available to download. Firewalls can get in the way though, so if you haven’t got a notification yet, or want to download 2.11 right away, you can download at:

http://www.analyse-it.com/support/download.aspx

When you’ve downloaded the update simply install over your existing version of Analyse-it. There’s no need to uninstall the old version and you won’t need your product key to reactivate Analyse-it -- unless you’re upgrading from Analyse-it version in which case you’ll need to request a product key to activate Analyse-it 2.11.

Where can I find details of what’s changed?

We know many of you work in regulated environments and need to re-validate changes made in each Analyse-it update. To see exactly what’s changed since the version you validated, so you can just re-validate the affected statistical tests, please see the change log at:

http://www.analyse-it.com/support/changes.aspx

Subscribe to get the latest updates

In future we’ll announce all major and minor updates on the blog. If you’re not already a subscriber, subscribe to the blog then you’ll be notified of new versions as soon as they’re released.

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed

The Analyse-it blog is live!

Wed, 11 Jun 2008 16:23:19 GMT

Thanks for stopping-by to read the inaugural post to the Analyse-it blog.

Why launch a blog?

We’ve launched the blog to keep you up-to-date on what’s new at Analyse-it, what we’re working on, and to notify you when updates and new products are released. We’ll also blog about using Analyse-it, statistical analysis, and interpretation.

The best part about the blog is it’s a conversation -- a conversation between you, our visitors and customers, and us. After reading a post you can add your thoughts, suggestions, and comments. We’ll reply when necessary or other visitors can post their opinions to ignite the debate. Click the links at the end of each post to read comments or add your own.

How can I get the latest posts?

To stay up-to-date we recommend you subscribe so you get the latest posts as they're published, without having to check the web-site. You can subscribe in two ways

Subscribe to the RSS feed
New blog posts are delivered to your RSS reader as soon as they are published. Internet Explorer, Mozilla Firefox, Microsoft Outlook and Mozilla Thunderbird all let you subscribe to blogs via RSS feeds.
Subscribe by e-mail
New blog posts are sent by e-mail directly to your inbox, just like a newsletter. You can unsubscribe at anytime.

That’s about it for the first blog post. Please remember to subscribe, and please post a comment now to introduce yourself or let us know how you want the blog and Analyse-it to develop in future.

Post comment Add to: del.icio.us | digg it! | Stumble upon | Subscribe to RSS feed