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I am a bit of a newbie at this. I am trying to fit a linear regression model on my data with the response variable being the population of heather, and the predictor variable being the types of treatment. Here is the summary:

## 
## Call:
## lm(formula = Heather ~ Treat, data = newdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.20533 -0.03983 -0.00339  0.00586  0.47767 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01578    0.02464   0.640    0.524    
## TreatC       0.55056    0.03485  15.797  < 2e-16 ***
## TreatH       0.14956    0.03485   4.291 5.76e-05 ***
## TreatHB     -0.01239    0.03485  -0.355    0.723    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1046 on 68 degrees of freedom
## Multiple R-squared:  0.8339, Adjusted R-squared:  0.8266 
## F-statistic: 113.8 on 3 and 68 DF,  p-value: < 2.2e-16

In the coefficients, there are only three treatments that are showing; C, H, and HB. But I have four treatments in total which are B, C, H, and HB. Where is TreatmentB?

JESEELing
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  • Please see the linked question and answer: although that question is about linear mixed models, the same explanation applies. – neilfws Sep 06 '22 at 00:30

0 Answers0