Missing data management and statistical measurement of socio-economic status: application of big data

Journal of Big Data

Table 1 Result for the estimates of a missing value by Lagrange interpolation, linear interpolation and linear regression estimation: in case of one missing value

Year	2000	2001	2002	2003	2004	2005	2006
Y(t)_Actual	41.18	36.69	35.50	38.25	40.05	47.21	48.81
Lagrange interpolation	41.18	85.34	30.58	39.45	42.07	46.31	46.92
Linear interpolation	41.18	38.34	37.47	37.78	42.73	44.43	47.14
Linear spline interpolation	41.18	42.75	42.42	41.51	40.99	40.12	39.84
Lagrange error	0.00	− 48.65	4.92	− 1.20	− 2.02	0.90	1.89
Linear interpolation error	0.00	− 1.65	− 1.96	0.47	− 2.67	2.77	1.67
Linear regression error	0.00	− 6.06	− 6.91	− 3.27	− 0.94	7.09	8.97

Year	2007	2008	2009	2010	2011	2012	2013
Y(t)_Actual	47.07	47.97	35.37	38.44	38.79	36.89	33.22
Lagrange interpolation	49.51	41.77	44.78	30.86	52.39	− 31.64	33.22
Linear interpolation	48.39	41.22	43.21	37.08	37.67	36.00	33.22
Linear spline interpolation	39.76	39.38	40.37	39.85	39.60	39.87	33.22
Lagrange error	− 2.45	6.20	− 9.40	7.59	− 13.61	5.25	0.00
Linear interpolation error	− 1.32	6.75	− 7.84	1.36	1.12	0.89	0.00
Linear regression error	7.31	8.59	− 5.00	− 1.40	− 0.81	− 2.98	0.00