Data analysis with only dummy variables I would like to find out whether there is a significant relationship between No-show rates and the part of the week (weekday/weekend), this in order to be able to suggest whether restaurants should allow or not allow reservations on certain days of the week. 
Am I right to think a logistic regression would be suitable for this? As you can see from the preview of my data I only have dummy variables available, and one continuous, which is making me confused about which type of regressions are possible. Or should I not use a regression at all? My main confusion I think lies in that I do not know what the outcome of my analysis should be, a yes/no answer, a p-value, i don't know :(

 A: From your question, it sounds like this is your main issue: 

...in order to be able to suggest whether restaurants should allow or not allow reservations on certain days of the week.

To address this question, you'll want to restrict your analyses to just the people who made a reservation (folks who come in without a reservation don't contribute to your understanding of whether no-shows are more likely on certain days). You're right that you can use logistic regression for this, with whether or not the reservation no-showed (0 or 1) as the outcome (or perhaps cancellations or no-shows, as per Matt Barstead's comment), and whether or not it's a weekend and size of the reservation as predictors. I also recommend you include an interaction between weekend and size, to test whether the relationship between size of reservation and likelihood of a no-show is different on weekends vs. weekdays (or whether smaller vs. larger parties have a bigger difference between weekend and weekday no-show rates, depending on how you look at it). As always, plotting your results will make interpretation easier. Here's a relevant tutorial I wrote up a couple years ago: https://blogs.uoregon.edu/rclub/2016/04/05/plotting-your-logistic-regression-models/
If you want to examine the relationship between no-showing and weekend separately from the relationship between no-showing and reservation size, you can run two separate logistic regression models. Alternatively, you can run the no-showing and weekend model as a cross-tab analysis, if you prefer, possibly using Fisher's exact test to test the hypothesis that weekend and no-showing are unrelated. 
A: To begin with you provided a list of transactions. I would aggregate the transaction data to daily values. Time series data is a collection of transactions , e.g. daily totals. At this level you have a number of time series a) total # of reservatons by day  b) total # of cancellations c) total # of no shows. d) composite of b and c .  You may have to model different classes of "size" i.e. 0-2 , 3-5 , 5-10 , over 10 as this might be an important classifier . I surmise that size is the # of people in the reservation who will be seated ? I would not analyse percentages but I would model b , c and d as a function of a  thus generating three equations and three sets of forecasts. Analysis of the coefficients could lead to insight. Note that a useful model might detect day-of-the-week effects , weekly effects , monthly effects , pre and post holiday effects , long-weekend effects , level shift effects , local time trends etc while dealing with anomalous values. An example of this kind can be found at http://autobox.com/cms/index.php/afs-university/intro-to-forecasting/doc_download/53-capabilities-presentation starting at slide 49 may be of some help to you. 
If you search for " user:3382 :daily effects" you will find more hints (124 posts) on this subject.
