قارن الطرق

	تحليل السلاسل الزمنية المتقطعة (ITS)	انحدار المربعات الصغرى العادية (OLS)
المجال	الاستدلال السببي	الاقتصاد القياسي
العائلة	Regression model	Regression model
سنة النشأة	2002	2019
صاحب الطريقة	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	Wooldridge (textbook treatment); classical least squares
النوع	Quasi-experimental segmented regression	Linear regression
المصدر التأسيسي	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
الأسماء البديلة	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ذات صلة	5	5
الملخص	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).