We send out daily e-mails to customers suggesting products at different times: `09:30`, `12:00`, `19:30`. A customer can either click on a product or not. I want to know the following: *Is there a significant difference in clicks depending on at what time an email is sent to the customer?*

The hypothesis is set up as follows:

\begin{align}
\mathcal{H}_N &= \textrm{There is no difference in number of clicks between time groups} \\
\mathcal{H}_A &= \textrm{There is a difference in number of clicks between time groups}
\end{align}

The data set I have is the following

    > summary(df)
     Click      Time      
     0:277551   0930:93799  
     1:3236     1200:93446  
                1930:93542  

Where `0=no click` and `1=click`. My first guess was a one-way ANOVA but then I have to make the assumption that my dependent variable `Click` is continous and normally distributed, which is not the case. 

What would be an appropriate test for the scenario I've described? If I only had two timegroups I'd use test of two proportions as suggested [here][1]. Is there any test of 3 proportions?

**EDIT 1:** Data set as per Ben Bolkers suggestion. But here I have only 3 rows and not 6 as he suggests. I'm misunderstanding what he means.

[![enter image description here][2]][2] 

**EDIT 2:** Fitting glm as dipetkov suggested gives the following result, using the raw data set in the form

    Click Time
    -----------
    0     0930
    1     0930
    1     1200
    0     0930
    0     1930
    ...

    Call:
    glm(formula = Click ~ Time - 1, family = binomial, data = df)
    
    Deviance Residuals: 
        Min       1Q   Median       3Q      Max  
    -0.1595  -0.1595  -0.1520  -0.1450   3.0200  
    
    Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
    Time0930 -4.54982    0.03210  -141.8   <2e-16 ***
    Time1200 -4.45538    0.03070  -145.1   <2e-16 ***
    Time1930 -4.35849    0.02927  -148.9   <2e-16 ***
    ---
    Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    
    (Dispersion parameter for binomial family taken to be 1)
    
        Null deviance: 389253  on 280787  degrees of freedom
    Residual deviance:  35301  on 280784  degrees of freedom
    AIC: 35307
    
    Number of Fisher Scoring iterations: 7
    
All the groups seem to be significant. How do I find which one of them leads to most clicks?


  [1]: https://stats.stackexchange.com/questions/435729/statistical-significance-of-a-b-test-for-binary-outcome
  [2]: https://i.sstatic.net/1iICk.png