The results of the data transformation into the natural logarithm form that I have finished can see in the table below: In the same way, you transform it for the X 1 and X 2 variables. Next, you copy-paste the formula up to the last data of the Y variable. In more detail, you can see in the image below:īased on the calculations using the excel formula, the Ln result of 12.00 is 2.48. In the first step, you create 3 additional columns in Excel and write the labels with LnY, LnX 1, and LnX 2, respectively. The data that will be used for exercise can be seen in the table below: The variables used to consist of one dependent variable (rice consumption) and two independent variables (income and population). To make the exercise easier, I will use an example of a research case entitled: Effect of income and population on rice consumption. In this article, I will determine the natural logarithm transformation using Excel. How to Transform Data into Natural Logarithm (Ln) in Excelĭata transformation can be conducted using various statistical software. One of the advantages of using the natural logarithm (Ln) is that Ln can minimize one of the deviations in the form of a non-constant variance (heteroscedasticity). On this occasion, you will discuss How to Transform Data into Natural Logarithm (Ln) in Excel. There are several types of data transformations, including root transformations, logarithms, arcsin, square, inverse, cubic, etc. Therefore, we need to understand how to transform data. Why does the assumption test need to be carried out as required? The assumption test was conducted to obtain the best linear unbiased estimator.
The main purpose of this data transformation is to change the measurement scale of the original data into another form so that the data can fulfill the required requirements. Transforming data into other forms is one method that is quite popular when you encounter problems in the assumption test that I conveyed earlier. However, sometimes you have to face the fact that some assumption tests may not qualify. You certainly hope that, based on the data you get from the research results, you can fulfill all the required assumption tests. The assumption test consists of non-heteroscedasticity, normality, multicollinearity, linearity, and autocorrelation (for time-series data). You are certainly familiar with the assumption test for those of you who have conducted linear regression analysis using the ordinary least square method. The data transformation is determined by changing the measurement scale of the original data into another form. You may have seen research data transformed into other forms in analyzing research data.
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