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Multiple Linear Regression

How to Find Y Predicted, Residual, and Sum of Squares in Multiple Linear Regression

By Kanda Data / Date Apr 08.2022

In the previous article, we have determined the value of the predicted Y value, residual value, and sum of squares in simple linear regression. As researchers, we must understand how to find it in multiple linear regression. So, in this article, I will convey a tutorial on how to find the predicted Y value, residual value, and sum of squares in multiple linear regression.

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Statistics

Nominal, ordinal, interval, and ratio scales | Types of Data Measurement

By Kanda Data / Date Apr 05.2022

Types of data measurement scales are fundamental to understand well for researchers. Types of data measurement scales in statistics consist of nominal, ordinal, interval, and ratio scales. This article will discuss the different types of data measurement scales.

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Multiple Linear Regression

How to Determine R Square (Coefficient of determination) in Multiple Linear Regression

By Kanda Data / Date Apr 01.2022

R Square (coefficient of determination) can be used to test the goodness of fit of a regression model. The value of R Square shows how big the independent variable’s ability to explain the dependent variable is. Because of the high benefit of the R Square value, various statistical software outputs will usually display the R Square value.

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Multiple Linear Regression

How to Calculate bo, b1, and b2 Coefficient Manually in Multiple Linear Regression

By Kanda Data / Date Mar 29.2022

It is essential to understand the calculation of the estimated Coefficient of multiple linear regression. Completing these calculations requires an understanding of how to calculate using a mathematical equation formula. But for most people, the manual calculation method is quite difficult.

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Assumptions of Linear Regression

Multicollinearity Test using Variance Inflation Factor (VIF) in SPSS

By Kanda Data / Date Mar 25.2022

Multicollinearity detection is one of the assumption tests that must be performed on multiple linear regression. This assumption test was conducted to obtain the best linear unbiased estimator.

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Excel Tutorial for Statistics

How to Activate and Load the Data Analysis Toolpak in Excel

By Kanda Data / Date Mar 22.2022

Until now, Excel has been an office application with many users. Excel can help manage data and process it to produce the required information. But did you know that excel can also perform statistical data processing?

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Assumptions of Linear Regression

How to Test the Multicollinearity in Multiple Linear Regression

By Kanda Data / Date Mar 18.2022

When choosing multiple linear regression analysis, we include at least two independent variables into the model. To obtain the best linear unbiased estimator, we must test the assumptions. One of the assumptions that need to be tested is the multicollinearity test.

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Simple Linear Regression

How to Find Variance, Standard Error, and T-Value in Simple Linear Regression

By Kanda Data / Date Mar 15.2022

Calculating the value of variance, standard error, and t-value is the last stage in simple linear regression analysis. The variance value can be calculated if the estimate of the variance of u has been calculated. The value of estimate of the variance of u cannot be calculated if it has not calculated the value of the sum of residual squared.

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Categories

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  • Assumptions of Linear Regression
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  • Regression Tutorial using Excel
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