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Concurrent Deviation - Correlation & Regression, Business Mathematics & Statistics Video Lecture | Business Mathematics and Statistics - B Com

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FAQs on Concurrent Deviation - Correlation & Regression, Business Mathematics & Statistics Video Lecture - Business Mathematics and Statistics - B Com

1. What is concurrent deviation in correlation and regression analysis?
Ans. Concurrent deviation refers to the difference between the actual value and the predicted value of a dependent variable in regression analysis. It measures the extent to which the predicted values deviate from the actual values, providing insights into the accuracy of the regression model.
2. How is concurrent deviation calculated in correlation and regression analysis?
Ans. Concurrent deviation is calculated by subtracting the predicted value of the dependent variable from the actual value. The resulting value represents the deviation or error for each observation. These deviations are then squared, summed, and averaged to calculate the mean squared error (MSE), which quantifies the overall accuracy of the regression model.
3. How can concurrent deviation help in evaluating the performance of a regression model?
Ans. Concurrent deviation plays a crucial role in assessing the performance of a regression model. By measuring the difference between the predicted and actual values, it provides a measure of how well the model fits the data. A lower concurrent deviation indicates a better fit, suggesting that the model accurately predicts the dependent variable.
4. Can concurrent deviation be negative in correlation and regression analysis?
Ans. Yes, concurrent deviation can be negative in correlation and regression analysis. Negative deviations occur when the predicted value is higher than the actual value. Positive deviations, on the other hand, occur when the predicted value is lower than the actual value. The overall sum of positive and negative deviations determines the accuracy of the regression model.
5. How can concurrent deviation be used to identify outliers in correlation and regression analysis?
Ans. Concurrent deviation can be used to identify outliers by examining the magnitude of the deviations. Observations with large positive or negative deviations are often considered outliers, as they significantly deviate from the predicted values. By identifying and analyzing these outliers, researchers can gain insights into influential data points that may impact the regression model's performance.
115 videos|142 docs
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