It gives information about the levels of variability within your regression model. ANOVAĪNOVA stands for Analysis of Variance. Standard Error is another goodness-of-fit measure that shows the precision of your regression analysis. In other words, 96% of the dependent variables (y-values) are explained by the independent variables (x-values).Īdjusted R Square is the modified version of R square that adjusts for predictors that are not significant to the regression model. In our example, the value of R square is 0.96, which is an excellent fit. It shows how many points fall on the regression line. R Square signifies the Coefficient of Determination, which shows the goodness of fit. -1 means a strong negative relationship.The larger the absolute value, the stronger is the relationship. The Multiple R is the Correlation Coefficient that measures the strength of a linear relationship between two variables. The summary output tells you how well the calculated linear regression equation fits your data source. We will divide the output into four major parts for our understanding. Let us now understand the meaning of each of the terms in the output. Select the Input Y Range as the number of masks sold and Input X Range as COVID cases. Go to the Data tab > Analysis group > Data analysis.
The Independent Variable is the factor that might influence the dependent variable.Ĭonsider the following data where we have a number of COVID cases and masks sold in a particular month. The Dependent Variable is the factor you are trying to predict. There are two basic terms that you need to be familiar with: In Excel, we use regression analysis to estimate the relationships between two or more variables.
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