পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Durbin-Watson Autocorrelation Test× | সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন× | |
|---|---|---|
| ক্ষেত্র | অর্থমিতি | অর্থমিতি |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 1950 | 2019 |
| প্রবর্তক≠ | James Durbin & Geoffrey Watson | Wooldridge (textbook treatment); classical least squares |
| ধরন≠ | Test for first-order residual autocorrelation | Linear regression |
| মৌলিক উৎস≠ | Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| অপর নাম≠ | DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| সম্পর্কিত≠ | 4 | 5 |
| সারসংক্ষেপ≠ | The Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateডেটাসেট ↗ |
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