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Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Ανάλυση Διακοπτόμενης Χρονοσειράς (ITS)× | Παλινδρόμηση Ελαχίστων Τετραγώνων (OLS)× | |
|---|---|---|
| Πεδίο≠ | Αιτιακή Συμπερασματολογία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 2002 | 2019 |
| Δημιουργός≠ | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) | Wooldridge (textbook treatment); classical least squares |
| Τύπος≠ | Quasi-experimental segmented regression | Linear regression |
| Θεμελιώδης πηγή≠ | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Εναλλακτικές ονομασίες≠ | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. | 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). |
| ScholarGateΣύνολο δεδομένων ↗ |
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