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Statistics
A collection of articles on descriptive and inferential statistics: Basic Statistics ( Definition of Statistics, Population and Sample, Variables and Data, Variable Measurement Scale ); Descriptive Statistics ( Definition of Descriptive Statistics, Size of Data Concentration, Size of Spread, Examples of Skewness and Kurtosis Calculations, Frequency Distribution ); Data Exploration ( Explorative data analysis, Stemplot, Knowing Box-Plot ); Correlation and Regression; Student 's t-test ( student t-test, 2-population t-test with homogeneous variance, 2-student t-test with heterogeneous variance, Paired t-test ); and some Non-Parametric tests (McNemar test, Wilcoxon test for paired data )
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- Written by Ade Setiawan
- Category: Correlation
Pearson correlation is a correlation measure used to measure the strength and direction of a linear relationship between two variables. Two variables are said to be correlated if a change in one variable is accompanied by a change in the other variable, either in the same direction or in the opposite direction. It must be remembered that a small (not significant) correlation coefficient does not mean that the two variables are not related . It is possible that two variables have a strong relationship but the value of the correlation coefficient is close to zero, for example in the case of a non-linear relationship .
Thus, the correlation coefficient only measures the strength of the linear relationship and not the non-linear relationship . It should also be remembered that the existence of a strong linear relationship between variables does not necessarily mean that there is a causal, causal relationship .
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- Details
- Written by Ade Setiawan
- Category: Student's t-Test
Student's t-test for one sample is rarely applied in agricultural research. However, in some cases, the t-test of a single sample can be used to compare the average of the characteristics studied with the comparator value or the standard value. For example, a single sample t-test can be used to compare the results of a measurement of the potential yield of a rice variance planted in an area (as a newcomer variance) with the average potential yield in its home country (as a hypothetical value).
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- Written by Ade Setiawan
- Category: Regression
Introduction
Definition of Simple Linear Regression
Linear regression is a statistical analysis technique used to predict the relationship between two or more variables. These variables can be divided into two types: dependent variables (Y) and independent variables (X). Simple linear regression refers to a model where there is only one independent variable, while multiple linear regression involves more than one independent variable.
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- Details
- Written by Ade Setiawan
- Category: Student's t-Test
In one sample t-test we only compare a population with a certain value, but in reality cases using this type of test are very rare. Researchers, particularly in agriculture, are more concerned with cases that require comparisons between two conditions or two population averages.
Before we do the analysis, we must first consider whether the two populations come from a normal distribution, are the variance of two populations equal? This will guide us in choosing the right method and formula for conducting a t-test analysis to compare the two population mean values.
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- Written by Ade Setiawan
- Category: Student's t-Test
To compare the mean value of the population with a certain value or with the mean value of other populations can be done with the z test . However, the z test can only be used if the data is normally distributed and the population variance is known .
In fact, it is rare that we can know the parameter values of a population with certainty, so we can only estimate the parameters of the population from the sample we take. Since we do not know what the population standard deviation, this value is estimated by the sample standard deviation, s, which is calculated from the sample. However, for small samples, s is not an accurate estimate for so it is no longer valid if we use it for the z test. For small sample sizes, we can approach it by using the student's t-test .
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- Details
- Written by Ade Setiawan
- Category: Student's t-Test
In one sample t-test we only compare a population with a certain value, but in reality cases using this type of test are very rare. Researchers, particularly in agriculture, are more concerned with cases that require comparisons between two conditions or two population averages.
Before we do the analysis, we must first consider whether the two populations come from a normal distribution, are the variances of the two populations homogeneous? This will guide us in choosing the right method and formula for conducting a t-test analysis to compare the two population mean values. In this article, we will describe the 2 population/sample t-test with heterogeneous variance .
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