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

Residuals:
      Min        1Q    Median        3Q       Max 
-0.030917 -0.019388 -0.003238  0.015260  0.042916 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.623084   0.009529  65.389  1.7e-14 ***
Treatmentfeedback -0.001745   0.013476  -0.129      0.9    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.02334 on 10 degrees of freedom
Multiple R-squared:  0.001674,	Adjusted R-squared:  -0.09816 
F-statistic: 0.01676 on 1 and 10 DF,  p-value: 0.8995

Analysis of Variance Table

Response: FqFm
          Df    Sum Sq   Mean Sq F value Pr(>F)
Treatment  1 0.0000091 9.130e-06  0.0168 0.8995
Residuals 10 0.0054480 5.448e-04               

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

Residuals:
      Min        1Q    Median        3Q       Max 
-0.028271 -0.005789 -0.001887  0.006518  0.031979 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       0.467226   0.006073   76.94 3.36e-15 ***
Treatmentfeedback 0.050154   0.008588    5.84 0.000164 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.01488 on 10 degrees of freedom
Multiple R-squared:  0.7733,	Adjusted R-squared:  0.7506 
F-statistic:  34.1 on 1 and 10 DF,  p-value: 0.0001638

Analysis of Variance Table

Response: FqFm
          Df    Sum Sq   Mean Sq F value    Pr(>F)    
Treatment  1 0.0075462 0.0075462  34.104 0.0001638 ***
Residuals 10 0.0022127 0.0002213                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
