
Call:
lm(formula = Output ~ Treatment, data = df.sum)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2734  0.0000  0.0000  0.3014  1.8465 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        24.9480     0.4542  54.927  9.7e-14 ***
Treatmentfeedback  -2.7828     0.6423  -4.332  0.00148 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.113 on 10 degrees of freedom
Multiple R-squared:  0.6524,	Adjusted R-squared:  0.6176 
F-statistic: 18.77 on 1 and 10 DF,  p-value: 0.001484

Analysis of Variance Table

Response: Output
          Df Sum Sq Mean Sq F value   Pr(>F)   
Treatment  1 23.232 23.2315  18.768 0.001484 **
Residuals 10 12.378  1.2378                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = Output ~ Treatment, data = df.sum)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2734  0.0000  0.0000  0.3014  1.8465 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        24.9480     0.4542  54.927  9.7e-14 ***
Treatmentfeedback  -2.7828     0.6423  -4.332  0.00148 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.113 on 10 degrees of freedom
Multiple R-squared:  0.6524,	Adjusted R-squared:  0.6176 
F-statistic: 18.77 on 1 and 10 DF,  p-value: 0.001484

Analysis of Variance Table

Response: Output
          Df Sum Sq Mean Sq F value   Pr(>F)   
Treatment  1 23.232 23.2315  18.768 0.001484 **
Residuals 10 12.378  1.2378                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = Output ~ Treatment, data = df.sum)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2734  0.0000  0.0000  0.3014  1.8465 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        24.9480     0.4542  54.927  9.7e-14 ***
Treatmentfeedback  -2.7828     0.6423  -4.332  0.00148 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.113 on 10 degrees of freedom
Multiple R-squared:  0.6524,	Adjusted R-squared:  0.6176 
F-statistic: 18.77 on 1 and 10 DF,  p-value: 0.001484

Analysis of Variance Table

Response: Output
          Df Sum Sq Mean Sq F value   Pr(>F)   
Treatment  1 23.232 23.2315  18.768 0.001484 **
Residuals 10 12.378  1.2378                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = Output ~ Treatment, data = df.sum)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2734  0.0000  0.0000  0.3014  1.8465 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        24.9480     0.4542  54.927  9.7e-14 ***
Treatmentfeedback  -2.7828     0.6423  -4.332  0.00148 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.113 on 10 degrees of freedom
Multiple R-squared:  0.6524,	Adjusted R-squared:  0.6176 
F-statistic: 18.77 on 1 and 10 DF,  p-value: 0.001484

Analysis of Variance Table

Response: Output
          Df Sum Sq Mean Sq F value   Pr(>F)   
Treatment  1 23.232 23.2315  18.768 0.001484 **
Residuals 10 12.378  1.2378                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = Output ~ Treatment, data = df.sum)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2734  0.0000  0.0000  0.3014  1.8465 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        24.9480     0.4542  54.927  9.7e-14 ***
Treatmentfeedback  -2.7828     0.6423  -4.332  0.00148 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.113 on 10 degrees of freedom
Multiple R-squared:  0.6524,	Adjusted R-squared:  0.6176 
F-statistic: 18.77 on 1 and 10 DF,  p-value: 0.001484

Analysis of Variance Table

Response: Output
          Df Sum Sq Mean Sq F value   Pr(>F)   
Treatment  1 23.232 23.2315  18.768 0.001484 **
Residuals 10 12.378  1.2378                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
