Multiple Linear Regression - Estimated Regression Equation

Universiteit[t] = + 411.193846153847 + 0.543269230769229Hogeschool[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)	411.193846153847	0	1745759452927018	0	0
Hogeschool	0.543269230769229	0	1108343673279120	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	1.22842569809785e+30
F-TEST (DF numerator)	1
F-TEST (DF denominator)	11
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.23903317614699e-14
Sum Squared Residuals	1.68872353275218e-27

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	674.31	674.31	-3.8972326979948e-14
2	674.31	674.31	3.33203334061951e-15
3	674.31	674.31	4.45503670491605e-15
4	674.31	674.31	4.45503670491605e-15
5	674.31	674.31	4.45503670491605e-15
6	674.31	674.31	4.45503670491605e-15
7	674.31	674.31	4.45503670491605e-15
8	674.31	674.31	4.45503670491605e-15
9	674.31	674.31	4.45503670491605e-15
10	674.31	674.31	4.45503670491605e-15
11	665.27	665.27	2.71355825444384e-29
12	665.27	665.27	2.71355825444384e-29
13	665.27	665.27	2.71355825444384e-29