
Call:
lm(formula = Output ~ Treatment, data = filter(df, bins == "2024-01-01 13:00:00"))

Residuals:
    Min      1Q  Median      3Q     Max 
-46.726   0.000   0.000   4.804  17.321 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        385.000      6.830  56.367 7.49e-14 ***
Treatmentfeedback   38.973      9.659   4.035  0.00238 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 16.73 on 10 degrees of freedom
Multiple R-squared:  0.6195,	Adjusted R-squared:  0.5814 
F-statistic: 16.28 on 1 and 10 DF,  p-value: 0.002381

Analysis of Variance Table

Response: Output
          Df Sum Sq Mean Sq F value   Pr(>F)   
Treatment  1 4556.7  4556.7  16.279 0.002381 **
Residuals 10 2799.1   279.9                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = Output ~ Treatment, data = filter(df, bins == "2024-01-01 01:00:00"))

Residuals:
    Min      1Q  Median      3Q     Max 
-49.505   0.000   0.000   4.414  39.671 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        385.000      9.217  41.772 1.48e-12 ***
Treatmentfeedback -124.861     13.035  -9.579 2.35e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 22.58 on 10 degrees of freedom
Multiple R-squared:  0.9017,	Adjusted R-squared:  0.8919 
F-statistic: 91.76 on 1 and 10 DF,  p-value: 2.354e-06

Analysis of Variance Table

Response: Output
          Df Sum Sq Mean Sq F value    Pr(>F)    
Treatment  1  46771   46771  91.763 2.354e-06 ***
Residuals 10   5097     510                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
