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Regression and Correlation

14.01 Reporter: zea 6 Responses

Regression and Correlation>>>Regression in the statistics is one method to determine the influence of a variable level of the other variables. The first is called the various terms: the variable description, explanatory variable, independent variables, or independently, the variables X (as is often depicted in the graph as absis, or X axis).

The second variable is the variable that is affected, dependent variables, the variables are bound, or variable Y. The two variables can be a random variable (random), but the variables that influence should always be a random variable. 

regression analysis is one of the most popular and knowledgeable . Almost all parts of science that requires the analysis may be known this analysis. 

This analysis was first used by Karl Pearson, a proponent mathematic and eugenic ideas, to analyze the nature of the relationship between parents and their children. 


B.Correlation
In the theory of probability and statistics, correlation, also called correlation coefficient, is a value that indicates the strength and direction of the relationship between the two linear(random variable). 

Correlation coefficients ranged from a value 

-1<><+1 

One type of correlation is the most popular correlation coefficient Pearson product-moment, obtained by dividing the two variables covariance with multiple ancillary. Though Pearson has a name, this method was first introduced by Francis Galton.

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Non Probablity Sampling Methods

16.07 Reporter: riezea 3 Responses

Actually there are two sampling methods, namely probability sampling and non-probability sampling. However, we will study the second. 

Non-Probability Sampling Method is a method of selecting a sample of the population does not use the rule-rule probability (opportunities). Non-Probability Sampling methods used when Probability Sampling methods can not be used, particularly in relation to the reduction of cost, time, energy and the problems that arise in the framework of making the sample. 

Including the probabilita Sampling method is as follows: 
1. Convinience Sampling 
2. Judgment Sampling 
3. Quota Sampling 
4. Snowball Sampling 

Let's take a look one by one
 
1. Convenience  sampling

Basic Concept The sample was taken based on the availability of the elements and the ease to get The sample was chosen because at the time and the right place This is usually used in the initial phase of research 
Advantages: a. Cheaper and faster (cost effective and time) b. Suitable for pilot study Disadvantages: a. The result is not reliable b. Can not be used when the population can not be defined 
Sample Applications : Research on consumer perceptions of the service. Research carried out for a sunday. Samples are taken of 100 people. Consumers will be selected as the sample is the first of 100 people found in the store during the period of research. 

2. Judgment sampling 

Basic Concept Judgment sampling is a sampling based on criteria that have been determined Sampling criteria, namely, two, Expert sampling The selection of representative sample based on the opinions of experts who so and the number of samples taken depends on the opinions of experts concerned. 

Purposively sampling Sample selection based on personal research on the researchers who claim that the sample selected truly representative. Researchers must have adequate knowledge. 
Advantages: a.When probability sampling can not be used at all 
b.when sample is very small (<20) 

I hope you can understand it and get useful information. There may be words that are strange and the new note by you, tomorrow there is more about statistics and its continue .  Statistic for our life, trust it Do not forget to leave your comment, url or e-mail


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Validity in Research

13.08 Reporter: zea 0 Responses


Validity in Research... In research conducted, the test is usually done on the validity and reliability study early to measure an instrument of research before going to the actual research. Here I will explain a little about the validity Validity shows the extent to which a gauge that measures what you want measured. Suppose we want to measure the body high, we may not measure it wearing body weight.

This is not valid because it is said to measure with high measure tool that is the higher body. A valid instrument that will have a high validity, the instrument (the questionnaire), which means less valid have a low validity. To test the validity level, researchers usually try instruments on testing instruments. 

Things to consider in determining the subject of many trials, including: 

1. availability will be subject to the target; 

2. unit of analysis is taken; 

3. ability of researchers in the case of time and funds; 

4. level of difficulty in the implementation. 

The number of trials is relatively subject, there is no rule that, only about 25-40 are a number of which already allows the implementation and analysis. There are differences of opinion on the subject which the trial is taken. When the subject should enable the pilot was taken from a population that will not be later research. 

But if not possible can take outside the terms of population with characteristics of the population taken as a pilot project for the same object or almost the same as the characteristics of the population will be examined. In addition there are two opinions pro and contra on the use of testing data. Opinion that states that if the pro trials using subjects drawn from the population, and the (valid), it may be added as research data. 

However, the contra opinion states, the data collected in the trial should not be mixed with the research data. In general there are two types of validity, namely: 

1. EKSTERNAL VALIDITAS 

This means the validity achieved when the resulting data from instruments in accordance with the data or other information about the research is variable. Example value Mathematics test results corelated to elementary school students with the highest student evaluation reports were distributed. Student evaluation reports were distributed so the value is used as a measure. 

2. INTERNAL VALIDITAS 

This means if there is validity to achieve conformity among parts of the instrument with the instrument as a whole. Validity-grain, an instrument that is said to have high validity when the grain is formed instruments not deviate from the instrument -Validity of factors, namely an instrument said to have a high validity if the factors that are part of the instruments does not deviate from the instrument.

In general, the steps made in the validity of the test is as follows: Calculate the correlation of each particle (items) with a total score (corrected item-total correlation). Comparing the value of the table with the correlation with the level of significance r รก degree and free N-2. Decision making If the results of r> r table, the item is valid If the results r Yes, that's the saying that the validity, I remember once again that the validity of which is measuring tool to measure what you want in the measure. 

you can comment, link or e-mail so we can share together,,thanks


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The BASIC of SAMPLING

22.11 Reporter: riezea 0 Responses

now we will study about sampling. sampling is one of the study, the knowledge learned in statistics . Sampling is used in research that would become an important part on it, For those who already don't know what sampling is just keep read and let's we return to basic first.

a. Basic sampling

Sampling in some terms that are frequently used and related to the estimation, as follows:

1. Elements: the unit used to get information.
2. Observation units: the unit where the information obtained either directly or
not directly.
3. Sampling Unit: the unit used in the basic sampling
4. Unit list: the list that is used as a sampling basis.
5. Sample framework: the collection of all units in the population as a basis
for sampling


b. Sampling method

Sampling method frequently used two types of Probability Sampling and Non-Probability Sampling.

1. Probability Sampling
Probability Sampling is a method of selecting a sample of the population using the rule-rule probability
Example: SRS, systematic, Stratified, Cluster, PPS, Multistage, Multiphase

2. Non-Probability Sampling
Non-Probability Sampling is a method of selecting a sample of the population does not use the rule-rule probability
Example: Convinience, judgment, Quota, Snowball

In the sampling method includes how to withdraw the sample is divide by three, namely:
1. Simple Random Sampling (SRS)
2. Probability Proportional to Size (PPS)
3. Systematic (systematic)

it's enough for the introducing sampling. Next day, I will continue with the next chapter. So, do not miss it

(please leave your comment, link or e-mail)

thanks









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Welcome AT STIS

12.10 Reporter: riezea 0 Responses
Welcome At STIS 
High School Science Statistics (STIS) is a university service IV-D program, which is managed by the Central Statistics Agency (BPS) since 1958, called for young people Indonesia general secondary school graduates to be trained statistician.

STIS holding a vision into institutions of higher education service is working to develop and disseminate knowledge, especially in the field of statistics and statistical computing to educate cadres who have academic / professional. Thus, graduates of the STIS is capable of planning and conducting research, doing the analysis in the field of socio-economic development and to plan and develop information systems. 

The curriculum is made in accordance with the development of knowledge economy, population, social, and information technology. The process and methods of learning are emphasized in the development of skills in the field of statistics and statistical computing. STIS has two departments: Department of Statistics (Economic and Social-residence) and the Department of Statistical Computing. 

Statistics experts generate economic statistics and social statistics experts-residence, and the Statistics Department of Computing experts computing and information systems. Lecturers come from graduates of universities in and outside the country level with the S2 and S3. STIS graduates get a degree Bachelor Applied Science (SST).




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Descriptive Statistics

06.00 Reporter: zea 0 Responses

Descriptive Statistics..Descriptive statistics is part of the science of statistics, which only process, presents the data without making a decision for the population. In other words, only to see the general description of the data obtained. Iqbal Hasan (2004:185) explains: Analysis is a form of descriptive analysis of research data to generalize the results of research based on one sample. Descriptive analysis was conducted with the test hypothetical descriptive. 

Whether the results of the analysis is hypothetical research can generalization or not. If the hypothetical zero (H0) is received, the results of research can generalization. This descriptive analysis using one or more variable, but are independent, by the curry is not analysis or comparison-shaped relationship. 

Iqbal Hasan (2001:7) explains: descriptive statistics or statistical deductive is part of a statistical study of data gathering and presentation of data so that young to understand. Descriptive statistic only related to decipher it or give particulars of the data or a situation or phenomenon. With descriptive statistical functions explain the situation, symptoms, or problems. 

Withdrawal of statistical conclusions on the descriptive (if any) is only aimed at the collection of the data. Based on the scope bahasannya descriptive statistics include: 

1. Distribution and its part-frequency portion, such as: a. Graphic distibusi (histogram, frequency polygon, and ogif) b. The size of the center value (average, median, mode, kuartil and so forth); c. Size stupid (range, average deviation, variation, standard deviation, and sebagianya); d. Slant and fineness kurva 

2. Index number 

3. Times series / progression of time or periodic 

4. Simple correlation and regression 

Bambang Suryoatmono (2004:18) states  Descriptive statistics is the use of data on a group to explain or interesting conclusion about the groups that Size Location: mode, mean, median, etc. Size variability: varians, standard deviation, range, etc. Form Size: skewness, kurtosis, box plot.

Pangestu Subagyo (2003:1) states: The definition is descriptive statistics as the statistics about the data collection, presentation, determining the values of statistics, making diagram or something about the image, here the data presented in the form of a more easily understood or read. Sudjana (1996:7) explains: Phase statistics which depict or just try analyse groups provided without creating or interesting conclusions about the population or a larger group called descriptive statistics.

statistics is always use for many things. as the statisticians, holding to statistic principium is  ca not bargain at all.

let's make statistics for the better life. 


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