# Lecture note: Biostatistic #2: Types of Data

This is lecture note for 2nd meeting, Tuesday, Oct 12th 2011. Today we study about Biostatistics and techniques in this kind of knowledge. like the previous post, statistic is the activity involving obtain data, analyze data, and present data.

Statistic is the summary of  information (data) in a meaningful fashion, and its appropriate presentation.

Statistic is the population if a plausible model explaining the mechanism that generates the data, with the ultimate goal to extrapolate and predict data under circumstance beyond the current experiment.

Biostatistic is the segment of statistic that deals with data arising from biological process or medical experiments

We start to know about descriptive statistic, the professor said that this is the point in descriptive statistic

• A means of organizing and summarizing observations
• They provide us with an overview of the general futures of data, this point like the quotes above ( predict and forecast).
• Tables, graphs, and numerical summary measures. Tables and graph is uses for make us easy to understand about the summary that we made.
This lecture is giving us the understanding of many kind of data that it used in statistic as general and especially in biostatistic. this is the summary of Data. As “data” we consider the result of experiment, A rough classification is as follows:
1. Nominal data, number of text representing unordered categories ( e.g. 0 = male, 1 = female ). the value fall into unordered categories classes ( sex, species, blood type), No ordering e.g. it makes no sense to sate that M > F.  we are only allowed to to examine if a nominal data is equal to some particular value or to count the number of occurrences of each value.
2. Ordinal Data: numbers or text representing categories where order counts ( e.g. 1 =fatal injury, 2 = severe injury , 4 = moderate injury ). The value fall into categories or classes with order, ordered with different values is not important, preferences scores.
3. Discrete Data: Counts of things of continuous values measured with whole numbers, both ordering and magnitude are important.
4. Continuous data: Numerical data where any conceivable value is, in theory, attainable ( e.g. height, weight, time, temperature, concentration of chemicals, infinite number of values between any to points. Continuous variables become discrete one if you measure them with discrete units.
5. Ranked Data, this ranked data give us this simple understanding: the differences between 1st and 2nd is not the same as that between 3rd and 4th. Ordinal, Discrete, Continuous Data can be ranked.

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