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Concurrent deviation - Correlation & Regression, Business Mathematics & Statistics Video Lecture | SSC CGL Tier 2 - Study Material, Online Tests, Previous Year
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FAQs on Concurrent deviation - Correlation & Regression, Business Mathematics & Statistics Video Lecture - SSC CGL Tier 2 - Study Material, Online Tests, Previous Year
| 1. What is concurrent deviation in correlation and regression? |  |
| 2. How is concurrent deviation calculated in correlation and regression? | |
Ans. Concurrent deviation is calculated by subtracting the predicted value of the dependent variable from the actual value for each data point. These differences are then squared, summed, and divided by the number of data points to obtain the mean squared deviation. Taking the square root of the mean squared deviation gives the standard deviation of the concurrent deviation.
| 3. What does a high concurrent deviation indicate in correlation and regression? | |
Ans. A high concurrent deviation indicates that the regression line does not accurately represent the relationship between the independent and dependent variables. It suggests that the regression equation is not a good fit for the data, and the predicted values are significantly different from the actual values. This could be due to outliers, non-linear relationships, or other factors affecting the data.
| 4. How can concurrent deviation be minimized in correlation and regression? | |
Ans. Concurrent deviation can be minimized by improving the fit of the regression line to the data. This can be achieved by identifying and removing outliers, transforming variables to account for non-linear relationships, or using more advanced regression techniques. Additionally, increasing the sample size can help reduce the impact of random variation and improve the accuracy of the predicted values.
| 5. What are the limitations of using concurrent deviation in correlation and regression? | |
Ans. Concurrent deviation has some limitations in correlation and regression analysis. It assumes that the relationship between variables is linear and that the residuals have a constant variance. If these assumptions are violated, the concurrent deviation may not provide an accurate measure of the model's performance. Furthermore, concurrent deviation does not consider the direction of the deviation, only the magnitude, which may overlook important patterns in the data.
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