The Sum of Squares is the square of the difference between a value and the mean value. SS: SS (Sum of Squares) symbolizes the good to fit parameter. It can be calculated using the df=N-k-1 formula where N is the sample size, and k is the number of regression coefficients. It is the second part of the analysis result.ĭf: df expresses the Degrees of Freedom. Observations: The number of iterations in the data model.ĪNOVA means Analysis of Variance. It shows the average distance of data points from the Linear equation. A smaller number for the regression equation provides increased certainty in its accuracy and reliability. Standard Error: It shows a healthy fit of Regression Analysis. The adjusted R-squared is a metric that takes into account the number of independent variables included in the model. The regression analysis model is a good fit for the data, as almost 99% of the values fall within the predicted range.Īdjusted R Square: The value of R^2 is used in multiple variables Regression Analysis instead of R square. In our example, the value of 0.997 is pretty good. An R-squared value of more than 95% is generally regarded as a good fit for a regression model. It indicates how well the data model fits the Regression Analysis. R Square: It symbolizes the Coefficient of Determination. 0 indicates that there is no correlation at all between the variables.-1 indicates a strong negative correlation between the variables.1 indicates a strong positive correlation between the variables.The bigger positive the value, the stronger correlative the relationships are. Multiple R: Multiple R indicates the correlation between variables. We will try to explain the simple regression analysis result that we have performed. However, understanding the output may seem difficult if you do not know what the terminologies mean. Performing regression analysis is quite easy. How to Interpret Regression Analysis Result? Multiple linear regression analysis is done and the results are displayed.Input the corresponding values and click on OK.The only difference is in the input X range. Performing multiple linear regression analysis using Analysis ToolPak is essentially the same as simple linear regression analysis.Our dataset consists of the price of the car varies depending on the Maximum Speed, Peak Power, and Range. To perform multiple linear regression analysis, we have the following dataset. Simple linear regression analysis is done and the result of the regression analysis is shown.ģ.2 How to Perform Multiple Linear Regression Analysis in Excel?.
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