Monday, December 11

Statistical And Statistic

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       Almost in every field, whether Government, Education, economy, industry, trade, corporate or otherwise, any officer or at least the leadership, have ever been, is or will face problems, among others, expressed by the numbers. From the collection of these figures he tried to make conclusions that are considered or reasonably expected to give an idea or an explanation of the problem. One of these efforts has been done since many years is to prepare or present those numbers in a form of a list or table that often people say that it is a statistic.

       Before we propose, explain, conclude, or make a statement about something the problem, first we need to do research on persolan are we to conclude that, for the statement that we make reasonable and reliable. After the material was obtained, collected and then processed and analyzed or analysis of based on the conclusion that this was recently made.
So basically the understanding that knowledge-related statistics by collecting the materials or information, processing and penganalisaannya, drawing conclusions and making a reasonable decision based on the analysis performed.

      As said above, to conclude something persolan required material or information which is collected in part or whole of the matter under investigation. Usually, material or information obtained otherwise in the figures. Material or information so that the truth must be reliable or unreliable, is also called the statistical data or often abbreviated just by word Data. Truth or reliability of the data is really a thing to note before further assessments are made.

DATA by Source
As previously explained, that to make a statement or conclusion is required that data collection is needed to make these conclusions, the data used in the statistics can be classified or were classified into several types of data. By DR. SUDJANA, MA, MSc in economics books and Commerce STATISTICAL for the fifth edition, dDitinjau of how the presence and use there are two general classifications. The first is called the internal data, data is collected by an agency on agency activities and the results used for the body for it too. For example in a company; production data, sales. In a country; data on population, national income data, etc.. The second extern data is data that is not contained in the agency’s activities, the data can be obtained from magazines, newspapers, specialized agencies that collect data or of other entities that may be required data is available.
According to the source data extern divided into two kinds, namely data extern extern primary and secondary data. Extern primary data is data that is collected by an agency and published by the same entity, person or entity which requires that data obtained directly from the agency, while the secondary extern data is data that was reported by a body, while the agency does not collect its own direct, but obtained from another party.

According to its type can be classified into two, namely data Qualitative and Quantitative data. Qualitative characteristics or called qualitative data, will be obtained if we keep records of elementary units kedala several categories, such as whether or damaged goods, excellent quality, good, adequate or less and so forth. If we do the recording in this way it is said that we have the attribute. So in this case is not menncatat characteristics of elementary units in the form of quantitative data values changing in character, but the record in the form of classification. While quantitative data have an understanding of the characteristics of the observations expressed in terms of numbers, with values shaped the changing data or be variable. Further data are variable this can be subdivided into two groups, with variable data is discrete (Discrete Data) and data with continuous variables (Continuous Data).
Discrete data is data obtained by way of counting, such as number of employees disebuah company data. Continuous data is data that has value in an interval just as the measurement results. For example length, area, content, weight and time.
Judging from the scale of measurement to be used, can be classified four types of research data, namely: (1) nominal data, (2) ordinal data, (3) data intervals, and (4) ratio data.

a. Nominal Data
There are field data to reflect the different variety of things based on the categories, do not indicate a high-low sequence of criteria in the position. Nominal scale is the lowest level of quantification method. Example: each member of the football team, sex (male, female), religion, educational background and so forth. It’s all just a category in the group, did not constitute the highest level to lowest.
b. Ordinal Data
Is the data field stating the number and level differences. Can also be a sequence of position classification that can be expressed “greater than or smaller than”. Ordinal data expressed in terms of relative position or order status within a group: 1st, 2nd, 3rd, 4th, and so on. Size ordinal dinayatakan in absolute price. Can you see examples of ordinal scales are described as follows:
c. Interval Data
Is a field data based on units of measurement are the same, indicating the size of a particular trait or characteristic. Interval scale does not have an absolute zero price. For example, differences in distance characteristics of the students who achieve a score of 90 and 91, assumed to be equal to the difference in distance characteristics possessed by students who achieve a score of 70 and 71. Interval scale shows the amount of the actual characteristics.
d. Ratio Data
Is a data field having the same interval with the interval scale, but there is still another feature is that, the ratio scale has absolute zero price, for example: zero point on the scale of centimeters, show no long or tall at all. Another feature of the scale again this ratio, ie the ratio scale has the quality of real numbers (real) that can be added, subtracted, multiplied, divided by the ratio expressed in the relationship. Example: 10 grams equal to two times five grams, three grams is half of six grams, and so on.


Population, constitute the entire subject. If you want to examine all the elements that exist within the study area, then the research is the study population or population studies. Example: All elementary school teachers who registered following the D2 equivalency program. So the collection of data in the study population, includes everything that exists in the population. Thus, the subject includes all that is in the population, which largely (according Arikunto, 1997), described as follows
Research imposed on objects in the population, the results are analyzed, it was concluded, the conclusion applies to the entire population.

The research sample that is a study of a portion of the population. The sample here is part or a representative of the population studied. Research conducted and the results are generalizable sample apply to the population.
Generalization means lifting the conclusions from the results of applied research on population samples. Example: Primary teachers in District Kemangkon (samples) in 2002 are still many who do not follow D2 equivalency program. This conclusion is actually not only apply in the District Kemangkon, but may be applied to elementary school teachers in Purbalingga (population) in previous years.
For more details, consider the following diagram.


The study sample conducted if a state subject in the population is really homogeneous. If the subjects in the population is not homogeneous then the conclusion should not be generalized to apply to the population.
In some studies, the data may be obtained from each individual that forms or is present in the population which will conclude its properties, for example, we may note the salaries of all employees throughout Indonesia, efforts to obtain such data, ie if each individual contained in the population studied is called a census.
In this census can not be done, because the existence of a factor that causes the constraint to do the census, the study is usually done by sampling, ie a study of a small portion of the population that can be accounted for and to represent the population in terms of all characteristics of the population should be able to reflected.

Statistical parameters are kerakteristik from measurements of an object. Size statistical parameters calculated from sample data or population. Statistical parameters as used in the statistical analysis is the average, variance or standard deviation, and correlation.
The average is a value that can represent the magnitude of the observed object. The average can be interpreted also as a measure that dominates data from all data. In the computation, the average can be determined by the midpoint, and its value is calculated by arithmetic average, median, and mode. The third measure has the properties of tersediri which shifts depending on the type of data dissemination. If the spread of data has a symmetric frequency distribution of the average, then the value of the three middle values are the same.
Varian is a measurement of variation around the mean. Varian is given by a value that indicates the level of variability of data differences. Because the average value often can not provide enough precise information about the parameters of the average as the midpoint, it is necessary to measure the level of variability in the data.
Correlation is a value that states the relationship between variables. If two variables have a correlation, then the two random variables are not independent. Size closely the relationship between two variables is indicated by the correlation coefficient. By knowing the correlation coefficient, it can be seen the level of the relationship between one variable with another variable.


DR. SUDJANA, M.A, MSc (1987)
Arikunto, (1997).

Nasution, (1996). Statistics for Economic and Commerce, Tarsito Bandung
Research Procedures, Rineka Copyright, Yogyakarta
Research methods, Earth Literacy, Jakarta.


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