Tag: statistics
How to Interpret Linear Regression Analysis Output | R Squared, F Statistics, and T Statistics
Once the researcher has successfully conducted linear regression analysis, the next step is to interpret the results. It is crucial for the researcher to possess sufficient knowledge to interpret the findings. The interpretation based on these results can be used to draw conclusions from the research.
If the regression coefficient is negative and significant, how should it be interpreted?
In some research findings, it is possible to observe negative and significant values for the estimated regression coefficient. Under such circumstances, how should this be interpreted? This is a question that often arises among researchers when they encounter results that indicate a negative coefficient estimate that is statistically significant.
How to Transform Natural Logarithm (Ln) in Cobb Douglas Regression Analysis using Excel
In production theory, the production function is defined as the technical relationship between inputs and outputs, where the output is a function of the inputs. The production function allows us to understand how input variables can explain the output variable. Inputs in the production process include capital, labor, and other production input variables.
How to Analyze Pearson Correlation Using Excel
Correlation analysis is one of the analytical techniques used to test the associative relationship between variables. In correlation analysis, testing can be conducted to answer whether the relationship between variables is significant and how strong and the sign of the relationship between the variables.
How to Differentiate between Nominal, Ordinal, Interval, and Ratio Data Measurement Scales in Research
In statistics, data measurement scales can be divided into four types: nominal, ordinal, interval, and ratio scales. Understanding the differences among these four measurement scales is crucial for researchers to grasp. This is because the choice of data analysis in research is heavily influenced by the measurement scale of the variables, whether they are nominal, ordinal, interval, or ratio scales.
Descriptive Statistical Analysis of Non-Parametric Variables (Nominal and Ordinal Scales)
Based on its methods, statistics can be divided into descriptive statistics and inferential statistics. Researchers can choose to use either of these methods or even combine both methods of data analysis.
Wilcoxon Test | Different test of two paired samples for non-parametric variables
Differences test is one of the most commonly used associative tests by researchers. Differences test can be conducted on both parametric and non-parametric variables. For parametric variables, the differences test can utilize the t-test assuming normally distributed data.
Mann-Whitney Test | Different test of two independent samples for non-parametric variables
In statistics, the association tests commonly conducted by researchers consist of tests of influence, relationship, and difference. Researchers often use the t-test to examine the mean difference between two sample groups. Typically, the measurement scale used in the t-test is the interval and ratio scales, which are normally distributed.