Month: October 2022
Determining Variance, Standard Error, and T-Statistics in Multiple Linear Regression using Excel
The T-statistics in multiple linear regression analysis can be used for statistical hypothesis testing in research. Comparing T-statistics with the T table or p-value can be used to accept statistical hypotheses. T-statistics value can decide whether to accept or reject the null hypothesis based.
How to Find ANOVA (Analysis of Variance) Table Manually in Multiple Linear Regression
Researchers must comprehend how to calculate the Analysis of variance (ANOVA) table in multiple linear regression. Table ANOVA can be used to analyze the simultaneous effects of the independent to dependent variables. The previous post I wrote, “Finding Coefficients bo, b1, and R Squared Manually in Multiple Linear Regression,” continues in this one.
Finding Coefficients bo, b1, b2, and R Squared Manually in Multiple Linear Regression
Researchers can choose to use multiple linear regression if the independent variables are at least 2 variables. On this occasion, Kanda Data will write a tutorial on manually calculating the coefficients bo, b1, b2, and the coefficient of determination (R Squared) in multiple linear regression.
Calculating Variance, Standard Error, and T-Statistics in Simple Linear Regression
Statistical hypothesis testing in a study can use the T-statistics in linear regression analysis. The criteria for the acceptance of statistical hypotheses can use a comparison between the T-statistics and the T table or the p-value. Based on the value of T-statistics, a decision can be concluded whether to accept or reject the null hypothesis.
How to Calculate ANOVA Table Manually in Simple Linear Regression
In simple linear regression, the calculation of the Analysis of variance (ANOVA) table is important for researchers to understand. ANOVA table can be used to determine how the influence of the independent variable on the dependent variable simultaneously.
Calculate Coefficients bo, b1, and R Squared Manually in Simple Linear Regression
Simple linear regression can be used to analyze the effect of one variable on another variable. The regression analysis consists of the dependent variable and the independent variable. Simple linear regression only consists of one dependent variable and one independent variable. If in the regression equation the number of independent variables is two or more than two variables, it is no longer called simple linear regression.