If you don’t mind me asking, can your quantify the average difference between the amount of food you eat on clear/cloudy days than on rainy/stormy days?
I’m considering sanitizing the personal parts (comments I’ve made, on the data, any data I wouldn’t want public, etc.) and posting it when I make my post on it. I’m still not sure how I feel about that. But long story short, I don’t mind giving that to you at all.
This is a screenshot of the graphing worksheet I made for reporting (categorical) statistics. (By the way, I was misremembering when I wrote that, the significance is only there for clear days, not cloudy. And there’s really not enough data on rainy and drizzle days to say anything.) The p-value column displays the p-value of a one tailed T test with unequal variances between the first variable set (in this case stormy weather) and the indicated variable set. So I eat as much or less food (the null hypothesis) on a clear day as I do on a stormy day with probability 0.012164. I’m sure you can extrapolate the rest. What’s cool about this is all I have to do is change the variables in the red boxes and it’ll automatically report anything. The excel formulas are… long.
It should be noted though, that in my second iteration of my journal, I’m changing both the way I record food and the way I record weather. First of all, when I started this the first time, it somehow didn’t occur to me to record the weather. Which is kind of dumb of me, given all of the scientific literature. So I went back and looked it up retroactively for the first 3-ish months of data. Secondly, I only gave one description for the entire day, which was generally the most severe weather we had that day. So if it stormed for an hour, I recorded it as stormy, even though it may have been cloudy or clear most of the day. Now I record a morning weather and an evening weather, which hopefully should be much more precise. I still look at the same website to help myself, but it’s tempered with my own observations too. I also use that website to grab the average temperature for the day though.
Also, I’m now recording an average hunger level instead of “food eaten”. The “food eaten” label made me want to add, not average. If I ate a huge meal right before bed, and starved the entire day, my moods were primarily affected by the starvation, not the meal—that’s my primary motivation.
Edit: I forgot to explain the scale. For most of my quantitative measurements, 50 is supposed to be how I feel on an average day. So 50 should be eating a “normal” amount of food for me.
Also, I took this screenshot just to show how the red boxes work. You change them and the rest automatically updates. Ironically, exercise is the only qualitative variable that I don’t keep track of using the 50 = average system, most notably because average for me is not really doing anything.
If you don’t mind me asking, can your quantify the average difference between the amount of food you eat on clear/cloudy days than on rainy/stormy days?
I’m considering sanitizing the personal parts (comments I’ve made, on the data, any data I wouldn’t want public, etc.) and posting it when I make my post on it. I’m still not sure how I feel about that. But long story short, I don’t mind giving that to you at all.
This is a screenshot of the graphing worksheet I made for reporting (categorical) statistics. (By the way, I was misremembering when I wrote that, the significance is only there for clear days, not cloudy. And there’s really not enough data on rainy and drizzle days to say anything.) The p-value column displays the p-value of a one tailed T test with unequal variances between the first variable set (in this case stormy weather) and the indicated variable set. So I eat as much or less food (the null hypothesis) on a clear day as I do on a stormy day with probability 0.012164. I’m sure you can extrapolate the rest. What’s cool about this is all I have to do is change the variables in the red boxes and it’ll automatically report anything. The excel formulas are… long.
It should be noted though, that in my second iteration of my journal, I’m changing both the way I record food and the way I record weather. First of all, when I started this the first time, it somehow didn’t occur to me to record the weather. Which is kind of dumb of me, given all of the scientific literature. So I went back and looked it up retroactively for the first 3-ish months of data. Secondly, I only gave one description for the entire day, which was generally the most severe weather we had that day. So if it stormed for an hour, I recorded it as stormy, even though it may have been cloudy or clear most of the day. Now I record a morning weather and an evening weather, which hopefully should be much more precise. I still look at the same website to help myself, but it’s tempered with my own observations too. I also use that website to grab the average temperature for the day though.
Also, I’m now recording an average hunger level instead of “food eaten”. The “food eaten” label made me want to add, not average. If I ate a huge meal right before bed, and starved the entire day, my moods were primarily affected by the starvation, not the meal—that’s my primary motivation.
Edit: I forgot to explain the scale. For most of my quantitative measurements, 50 is supposed to be how I feel on an average day. So 50 should be eating a “normal” amount of food for me.
Also, I took this screenshot just to show how the red boxes work. You change them and the rest automatically updates. Ironically, exercise is the only qualitative variable that I don’t keep track of using the 50 = average system, most notably because average for me is not really doing anything.
I’d like to hear more about what results you’ve derived from analyzing the data, FWIW.