Bandingkan metode

	Interpolasi Spasial Kriging	Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)
Bidang	Analisis Spasial	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1963	2019
Pencetus	Georges Matheron (formalised geostatistics)	Wooldridge (textbook treatment); classical least squares
Tipe	Geostatistical spatial interpolation	Linear regression
Sumber perintis	Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Terkait	5	5
Ringkasan	Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms.	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).