Multiple Linear Regression - Estimated Regression Equation

armoede [t] = + 468.154293413601 -2.3899644164659Wiskunde[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	468.154293413601	64.66784	7.2394	1e-06	0
Wiskunde	-2.3899644164659	0.375755	-6.3604	3e-06	2e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.81803163118994
R-squared	0.669175749627274
Adjusted R-squared	0.652634537108638
F-TEST (value)	40.4550602849303
F-TEST (DF numerator)	1
F-TEST (DF denominator)	20
p-value	3.31051492308276e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	15.7712823814165
Sum Squared Residuals	4974.66695908759

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	91.7	70.4642145136747	21.2357854863253
2	90.2	86.7159725456431	3.4840274543569
3	86	87.9109547538761	-1.91095475387605
4	83.9	96.5148266531533	-12.6148266531533
5	80.4	75.4831397882533	4.9168602117467
6	76.5	62.3383354976908	14.1616645023092
7	76	96.0368337698602	-20.0368337698601
8	75.8	73.332171813434	2.46782818656595
9	75.6	48.9545347654818	26.6454652345182
10	75	42.5016308410239	32.4983691589761
11	74.7	68.7912394221488	5.90876057785119
12	63.2	45.1305916991363	18.0694083008637
13	52.9	51.8224920652409	1.07750793475909
14	48.5	48.4765418821886	0.0234581178113746
15	39.1	59.9483710812249	-20.8483710812249
16	38.4	49.6715240904216	-11.2715240904216
17	4.3	31.2687980836341	-26.9687980836341
18	30.3	37.2437091247988	-6.94370912479883
19	30.3	40.8286557494977	-10.5286557494977
20	29.6	34.6147482666863	-5.01474826668635
21	26.5	31.2687980836341	-4.76879808363407
22	13.8	23.3819155092966	-9.58191550929662