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I recently created a seasonal dummy regression model in R given a dataset beginning Jan 2016 and ending May 2020. Given the results below there appears to be statistically significant seasonality in the second half of year which makes sense and confirms my hypothesis. I shared these initial results with a colleague before they left for the day and they replied to me asking me to run a joint test of all the monthly dummies equal to 1, and that they wanted to know if they impacted the growth. I'm guessing they might be asking about non stability in the trend but I'm confused about what they are asking. It sounds like they are simply asking me for an F test, which is included in my results below. More specifically, it seems like they are looking for me to run a joint hypothesis where all dummies are equal to 1. Theres another Cross Validated post that is similar to my post. I'm unsure how to proceed with the modeling the data doing a joint hypothesis where all month's dummies are equal to 1. Any recommendations/insight would be much appreciated. Thank you very much.

Series: value 
Model: TSLM 
Transformation: (.x) 

Residuals:
      Min        1Q    Median        3Q       Max 
-0.027701 -0.014466 -0.001116  0.013166  0.037516 

Coefficients:
                 Estimate Std. Error  t value Pr(>|t|)    
(Intercept)     14.0141457  0.0116511 1202.819  < 2e-16 ***
trend()         -0.0018201  0.0003069   -5.930 3.44e-06 ***
season()month2  -0.0039888  0.0142680   -0.280 0.782110    
season()month3  -0.0047131  0.0154564   -0.305 0.762943    
season()month4   0.0066519  0.0154351    0.431 0.670191    
season()month5   0.0234106  0.0154198    1.518 0.141505    
season()month6   0.0499464  0.0154106    3.241 0.003360 ** 
season()month7   0.0595418  0.0154076    3.864 0.000701 ***
season()month8   0.0590938  0.0154106    3.835 0.000757 ***
season()month9   0.0666339  0.0154198    4.321 0.000216 ***
season()month10  0.0439361  0.0154351    2.847 0.008703 ** 
season()month11  0.0299634  0.0154564    1.939 0.063923 .  
season()month12  0.0416095  0.0154838    2.687 0.012624 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.02017 on 25 degrees of freedom
Multiple R-squared: 0.7802, Adjusted R-squared: 0.6747
F-statistic: 7.395 on 12 and 25 DF, p-value: 1.4005e-05
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