![]() The ANOVA table can be used to test the research hypothesis. Next, we move to the 2nd table, namely the ANOVA table. The remaining 9.71% is explained by other variables not included in the equation model. You can interpret that the variation of the rice consumption variable of 90.29% can be explained by the variation of the income and population variables. The value of R Square can be seen that the value is 0.902888. From this information, R Square and Adjusted R Square can be used to estimate the model’s goodness. There is 5 information displayed in the summary output, namely Multiple R, R Square, Adjusted R Square, standard error, and observations. The resulting output summary table is as shown below: The output displayed in excel is divided into three tables, namely summary output, ANOVA, and coefficient table. The stages in detail can be seen in the image below: Multiple Linear Regression Analysis Output Interpretation Then you click OK to bring up the output of the analysis. You can also generate residual values and normal probability plots (optional). There are 3 options for storing the analysis output: the same sheet, creating a new sheet, and a new workbook. Next, you activate “Labels” and “Confidence Level” For the Confidence level, I choose a p-value of 5% (0.05). How do input label variables and data? you can block all the labels and data in the excel sheet. You then input the variable label and independent variable data into the “Input X Range:” box. ![]() The next step is to input the variable label and all dependent variable data into the “Input Y Range:” box. The “Data Analysis” window will then appear, then you select regression as shown below: ![]() To perform multiple linear regression analysis using excel, you click “Data” and “Data Analysis” in the upper right corner. To enable “Data Analysis” in excel, you can follow the tutorial I wrote in the article entitled: “ How to Activate and Load the Data Analysis Toolpak in Excel.” How to Analyze Multiple Linear Regression in Excel If you don’t find the thing in question, you need to activate the toolpak first in excel. But this time, I will use the data analysis tools provided in excel.ĭata analysis tools in excel can be seen in the “Data” menu, and then you will find “Data Analysis” in the upper right corner of your excel. In several previous articles, I have written about tutorials on calculating simple linear regression and multiple linear regression manually using excel. The data used for the exercise can be seen in the table below: Data Analysis Tools in Excel Furthermore, the row is filled with data from each observation result. Next, you can create 4 columns which are then filled with the name of the country and the name of the variable (1 dependent variable and 2 independent variables). Data can be directly inputted into excel. The next stage after the specification of the regression equation is to input data. The specifications for multiple linear regression equations can be arranged as follows: Data Input Data were analyzed using the results of variable observations in 15 countries. ![]() Rice consumption is measured by million tons, Income by million per capita, and population by million people. This exercise aims to know how income and population influence rice consumption. The variables I use consist of: (a) rice consumption as the dependent variable (b) Income as the 1st independent variable and (c) Population as the 2nd independent variable. I use an example of a case of multiple linear regression with two independent variables. To make it easier to understand how to analyze data and interpret it, I will give an example of a case that can be used for exercise. Excel can also help us analyze multiple linear regression quickly as we use statistical software, such as SPSS, SAS, STATA, etc. On this occasion, I will analyze multiple linear regression using excel. In a previous article, I have written an article on analyzing multiple linear regression using SPSS. Multiple linear regression using at least two independent variables.ĭue to many researchers, lecturers, and students who use multiple linear regression analysis, I will review how to analyze and interpret the output. We can use multiple linear regression analysis to estimate the effect of the independent variable on the dependent variable.
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