Category: Statistics
Understanding the Difference between Parametric and Non-Parametric Statistics
The selection of data analysis methods is an important step that researchers must prepare. The correct analytical method will obtain the proper conclusion.
How to Create and Analyze Variables using a Likert Scale
In statistics, research variables can be measured in parametric and non-parametric variables. Variables measured by interval scale and ratio scale are grouped in parametric variables.
How to Find Variance and Standard Deviation in Excel
Variance and standard deviation are components of statistical values that are most often found in descriptive analysis. When we perform statistical software analysis, we will find the value of variance and standard deviation if we enable descriptive statistics.
How does high variance affect hypothesis testing in linear regression?
Will a high variance value affect the statistical test on linear regression? Many questions related to this topic were Kanda Data obtained. On this occasion, Kanda Data will discuss the impact of the variance value on hypothesis testing on linear regression.
How to Write and Test Statistical Hypotheses in Simple Linear Regression
We need to develop hypotheses when conducting research. A hypothesis is a provisional assumption or statement of the research. The hypothesis needs to be proven, whether true or false, through the research process.
How to Reverse Natural Logarithm to Initial Data in Excel
Researchers often use the natural logarithm form (Ln) transformation. In the previous article, I have written about How to Transform Data into Natural Logarithm (Ln) in Excel.
How to Transform Data into Natural Logarithm (Ln) in Excel
You may have seen research data transformed into other forms in analyzing research data. The data transformation is determined by changing the measurement scale of the original data into another form.
Create Residual and Y Predicted in Excel
Residual and predicted Y have an important role in linear regression analysis using the Ordinary Least Square (OLS) method. Several OLS linear regression method assumptions were conducted to test the residuals, including normally distributed residuals and constant residual variance.