Goodness of Fit
Correlation0.5048
R-squared0.2549
RMSE0.4306


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
110.7254901960784310.274509803921569
210.7254901960784310.274509803921569
300.725490196078431-0.725490196078431
400.210526315789474-0.210526315789474
510.7254901960784310.274509803921569
610.7254901960784310.274509803921569
710.7254901960784310.274509803921569
800.725490196078431-0.725490196078431
900.725490196078431-0.725490196078431
1010.7254901960784310.274509803921569
1100.210526315789474-0.210526315789474
1200.25-0.25
1300.25-0.25
1400.725490196078431-0.725490196078431
1500.210526315789474-0.210526315789474
1610.7254901960784310.274509803921569
1710.7254901960784310.274509803921569
1810.7254901960784310.274509803921569
1900.725490196078431-0.725490196078431
2000.210526315789474-0.210526315789474
2110.7254901960784310.274509803921569
2210.250.75
2300.210526315789474-0.210526315789474
2410.2105263157894740.789473684210526
2510.7254901960784310.274509803921569
2610.2105263157894740.789473684210526
2710.7254901960784310.274509803921569
2800.210526315789474-0.210526315789474
2900.210526315789474-0.210526315789474
3010.7254901960784310.274509803921569
3110.2105263157894740.789473684210526
3210.7254901960784310.274509803921569
3300.210526315789474-0.210526315789474
3400.210526315789474-0.210526315789474
3500.210526315789474-0.210526315789474
3610.7254901960784310.274509803921569
3710.7254901960784310.274509803921569
3800.25-0.25
3900.725490196078431-0.725490196078431
4010.7254901960784310.274509803921569
4110.7254901960784310.274509803921569
4210.7254901960784310.274509803921569
4310.250.75
4410.7254901960784310.274509803921569
4500.210526315789474-0.210526315789474
4600.725490196078431-0.725490196078431
4700.25-0.25
4810.7254901960784310.274509803921569
4910.7254901960784310.274509803921569
5000.210526315789474-0.210526315789474
5100.725490196078431-0.725490196078431
5210.7254901960784310.274509803921569
5300.725490196078431-0.725490196078431
5400.210526315789474-0.210526315789474
5500.25-0.25
5600.25-0.25
5700.725490196078431-0.725490196078431
5800.725490196078431-0.725490196078431
5900.210526315789474-0.210526315789474
6000.210526315789474-0.210526315789474
6100.25-0.25
6210.7254901960784310.274509803921569
6310.7254901960784310.274509803921569
6410.2105263157894740.789473684210526
6500.210526315789474-0.210526315789474
6600.725490196078431-0.725490196078431
6700.725490196078431-0.725490196078431
6800.210526315789474-0.210526315789474
6910.7254901960784310.274509803921569
7010.7254901960784310.274509803921569
7100.25-0.25
7200.25-0.25
7300.25-0.25
7400.25-0.25
7510.7254901960784310.274509803921569
7610.2105263157894740.789473684210526
7700.210526315789474-0.210526315789474
7810.7254901960784310.274509803921569
7910.2105263157894740.789473684210526
8010.7254901960784310.274509803921569
8100.210526315789474-0.210526315789474
8200.210526315789474-0.210526315789474
8300.210526315789474-0.210526315789474
8410.2105263157894740.789473684210526
8500.210526315789474-0.210526315789474
8600.210526315789474-0.210526315789474
8710.2105263157894740.789473684210526
8810.7254901960784310.274509803921569
8900.210526315789474-0.210526315789474
9000.210526315789474-0.210526315789474
9110.250.75
9210.7254901960784310.274509803921569
9310.7254901960784310.274509803921569
9400.725490196078431-0.725490196078431
9510.7254901960784310.274509803921569
9610.7254901960784310.274509803921569
9710.7254901960784310.274509803921569
9810.7254901960784310.274509803921569
9900.210526315789474-0.210526315789474
10000.210526315789474-0.210526315789474
10110.250.75
10200.210526315789474-0.210526315789474
10300.210526315789474-0.210526315789474
10400.210526315789474-0.210526315789474
10500.25-0.25