## Wednesday, June 1, 2016

### Statistical data type

In statistics, groups of individual data points may be classified as belonging to any of various

Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.

Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating point computation. But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.

Other categorizations have been proposed. For example, Mosteller and Tukey (1977)

The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer" (Hand, 2004, p. 82).

**statistical data types**, e.g. categorical ("red", "blue", "green"), real number (1.68, -5, 1.7e+6), etc. The data type is a fundamental component of the semantic content of the variable, and controls which sorts of probability distributions can logically be used to describe the variable, the permissible operations on the variable, the type of regression analysis used to predict the variable, etc. The concept of data type is similar to the concept of level of measurement, but more specific: For example, count data require a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.

Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating point computation. But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.

Other categorizations have been proposed. For example, Mosteller and Tukey (1977)

^{[1]}distinguished grades, ranks, counted fractions, counts, amounts, and balances. Nelder (1990)^{[2]}described continuous counts, continuous ratios, count ratios, and categorical modes of data. See also Chrisman (1998),^{[3]}van den Berg (1991).^{[4]}The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer" (Hand, 2004, p. 82).

^{[5]}^{source: https://en.wikipedia.org/wiki/Statistical_data_type}### BLOG's are not popular

Currently a lot of people write BLOG's because it has become quite easy. Those writers also realize that the visitors aren't coming that easy anymore, because the social media bring much more diversity. So what happens? The BLOG-writers post links on their Social Media Websites like facebook and Tumbler, and also send out Tweets to their followers to draw attention.

That worked quite well for the last years, but now people seem to not follow those links anymore. The BLOG's form an undiscoverd archive of information that most Internet-users are avoiding. A new marketing tool will be brought out soon to solve this. Not sure what it will be.

That worked quite well for the last years, but now people seem to not follow those links anymore. The BLOG's form an undiscoverd archive of information that most Internet-users are avoiding. A new marketing tool will be brought out soon to solve this. Not sure what it will be.

Labels:
advertising,
Blog,
drawing attenton,
facebook,
Public,
Ruud Janssen,
Social Media,
Social Networker,
Tubmler,
Twitter,
Visitors

### StatCounter - Browsers

What decision can the browser stats help with?

The obvious question is have you tested your website in the browsers your visitors are using? A website can look great in one particular browser and not work in any other. It is always recommended to code websites using the standards maintained by W3C.

This ensures that not only will your website be compatible now, but it will also be compatible for future software!

Labels:
Browsers,
IUOMA,
Ruud Janssen,
Standard,
Statcounter

## Tuesday, February 2, 2016

### Social Networks take the traffic

With Google analytics I see that the visitors of my websites mainly go to the social networks. Blogs are hardly found and maintained anymore. The time factor is the reason. People don't have the time for a structured blog. They just publish their info on Social Networks and rely on those to carry the message to their surroundings. A scary thought because it doesn't always reach all one wants too....

Labels:
Blogs. Social Networks,
Ruud Janssen,
Scary,
Statistical facts

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