Welcome to the cannabis data science meetup group.
The day before Thanksgiving.
So it's harvest season.
So we'll be talking about
cannabis sales and harvests and all that.
So there's a lot of ground to cover.
We've got some new faces today.
So if we want to just do a quick 30 second intro around.
So Sabahullah, you wouldn't mind
beginning with you and just giving
a quick introduction about yourself.
Okay, sure. Hi everyone.
My name is Sabahullah Zabi.
I'm also called Zabi Buddha.
And currently I'm joining you from Toronto.
Yeah. And first of all,
happy Thanksgiving for everyone.
And the thing is like I am a potential data scientist.
I have taken bootcamp for three months with Lighthouse.
So I kind of know how to do machine learning and do modeling,
analyze data, but I have not joined
yet the data science field yet.
So in the real world.
Awesome. Yeah.
Heather, would you mind introducing yourself to the group?
Sure. My name is Heather.
Joined cannabis data science a couple of months ago.
Been attending on a regular basis.
I don't come from the background of economics,
but it is something that's helping me to understand the data better.
So I have experience in the lab and also
a love of cannabis.
So with those two things combined, here I am.
Awesome. And exactly.
And so we have people from all different backgrounds,
my background is in economics.
As Heather said, she's spent time in the lab.
She's got a laboratory background.
So whatever you bring to the table just adds value to the group.
So Marjana, you wouldn't mind introducing yourself real quick, would you?
Hey, everyone. Can everyone hear me?
Yes.
OK. I didn't know how to use my pod's mic, so I'm glad it's working.
So yeah, I joined Keenan on Saturday for the stats portion.
And I actually worked in a lab too, just like Heather,
for 10 years and decided that was not for me.
So during the pandemic, I made a switch to software engineering.
I went to a boot camp too, like you, Zabi.
I went to a coding boot camp actually for software engineering.
And right now I work as a tech consultant.
I also like cannabis and think there is great potential for it in the market.
So here I am. Nice to see everyone.
Awesome to see you, Marjana.
And that's interesting that you have a laboratory background as well.
So every now and again, we'll talk about cannabis testing.
So we'd always love your perspective on the lab space.
Sure.
To Parat, if I'm pronouncing that correctly.
And correct me if I'm not.
But would you mind introducing yourself?
Beira?
Yeah. OK. Hi.
I wasn't sure it was me. Yeah, Farah is fine.
Hi. I'm a software engineer.
I am currently in England, south of England.
I've been a software engineer for four years.
And I'm very interested in data science.
Cannabis is quite new to me.
It's because there's legal issues and then other stuff that kind of make people think
that it's not OK.
And I'm like, hold on for a second.
Maybe it is OK.
And my recent experiences with it have been really interesting.
So I really wanted to learn more because I also think that it has a lot more uses than
the government wants us to know.
Awesome.
And we'd love your perspective, too, coming from the UK.
So feel free to share what the industry is like there.
Not even sure if they've got permitted.
Do they have permitted medicinal?
Yeah, it's new.
It's not, again, it's not very widely used or accepted stills because it's still considered
as a drug.
So it's not that widely used, but definitely still is.
Yeah.
Awesome.
Well, we'll have to hear from you as things develop across the seas.
So Joachim, if I'm pronouncing that correctly, would you mind introducing yourself?
Well, my name is Joachim.
I'm from Germany.
I live in Aaraba.
Like Zabihullah and Marjana, I took a boot camp in data science some time ago.
Like Zabihullah, like in Zabihullah's case, that didn't lead to a related drop immediately.
And I must admit, I just came across this meetup, and it seemed a quirky thing to do
for my lunch break.
And I'm looking forward to seeing what's in store for me.
Oh, awesome.
We've got quite the international crowd today, so happy to have you.
We're representing at least four countries, United States, Canada, Britain, and Germany.
So Joachim, sorry for mispronouncing, but you're also welcome to share your perspective.
Correct me if I'm wrong, but there may even be cannabis imports into Germany.
Well, a new government is in the process of being formed.
And they, I think, talked about legalization as something they might want to tackle.
And ideologically, they're in a good position to do so.
But as we speak, I think, but it remains illegal for all intents and purposes, except for possibly
some medical uses.
I'm not quite sure there.
So a booming export market may come into being, but it's not there at the moment.
And like some other people on this meetup, I am currently in Canada.
I live in Ottawa.
Well, as things develop, love to hear your experience.
And then our last guest, I don't have your name, so you're welcome to introduce yourself.
Oh, sorry, you guys talking to me?
Yes.
Okay.
It's just unknown on name for me, but you're welcome to introduce yourself.
Okay.
My name is Courtney.
I'm from Calgary, Alberta in Canada.
And I'm actually attending this meeting.
I'm going to school right now.
I'm completing my master's in data science at the University of Colorado.
So I'm attending this as part of a class, actually.
But I'm excited to see kind of the data science process and relate it to cannabis a little
bit.
Well, cool.
You'll have an interesting story to report back.
So we've got some good material to look at today, and that's exciting.
So we can maybe chat more throughout the presentation.
But without further ado, I'll just show you essentially some material that I prepared
for today to just sort of start a discussion.
So without further ado.
So in the past few weeks, we were looking at Massachusetts in specific.
And so I've got a major correction for today, but that's what we do here, right?
We're doing exploratory statistics.
And as new information comes to light, well, we can update our statistics and our forecast
as well.
So long story short, we were calculating statistics based on just the total number of retail licenses,
379.
And same for manufacturers, 218.
Well, I listened into this Massachusetts public meeting last Thursday, the day after our meetup
last week, and new information came to light.
So we presented this table where you essentially have a breakdown of the applications by type
and the stage that they're at.
So as you can see, not all retailers have commenced operations.
So you've got pending applications, and then you have licenses that have been provisionally
approved.
And then it's not clear if all of these licenses have begun operating yet.
So when we were calculating sales per retailer and we were using 379 retailers, well, that's
going to bias our estimates downwards.
So our estimate of sales per retailer is probably much lower than the actual.
So today, we can revise that statistic.
And it got me thinking, well, what is essentially the average sales per retailer?
Because it would be nice to have a figure to try to gauge our estimates against to see
if we're in the right ballpark.
So did some digging, and Nevada commissioned a technical memorandum.
And I'll share the link with you.
It's definitely worth reading through because they've done a lot of work that's in a similar
vein as work that we've done.
And one can only imagine how much the firm that was commissioned to create the report
was paid.
And this is all public data.
So that's what we're here at the Cannabis Data Science Group to do is use public data
to create statistics that people find valuable.
So if the state of Nevada paid a lump sum of money to get these statistics calculated,
then they must be valuable.
So we're going to essentially try to recalculate these statistics, right?
Because that's part of the scientific process is to replicate other people's results.
One second.
Let's get...
And welcome to the group.
OK.
So just had to let someone in.
So we're going to try to replicate these data points here.
So for example, in Massachusetts, they calculated in 2020, I believe.
Once again, this is sort of going to be the lesson of the day is calculating precise numbers
is difficult and hard to reproduce.
And that's why we're trying to do things open source as an open box, right?
Because that way all of you can look at the source code.
Anyone in the world can look at the source code and critique it or improve upon it.
So that way it's not just a black box that's spitting out these numbers.
We actually have a nice transparent box so we can see the process about how these numbers
are calculated.
Because...
So right off the bat, right, they say by state 2020.
Well, their dispensary count says 2021.
So well, we've got the numbers right here in front of us.
And so should check that.
But centrally, where exactly are they getting this revenue per dispensary?
And as we were just talking about in Massachusetts, these retailers, not all of them necessarily
are operating.
So where are they getting this 84 retailer number from?
Are they counting just final licenses?
Are they just counting people that they think have commenced operations in 2020?
So this is something that we're going to poke at.
And there's some dimensions to this that we'll talk about here shortly.
But long story short, we're, one, we're going to try to reproduce these numbers, but reproducing
numbers or statistics just for statistics sake is not that interesting.
So welcome, Eric.
So we need a research question.
So what jumps out to me and the reason Nevada commissioned the report to begin with is they
were curious about how many potential new retailers they could bring into the market
and how many retailers will the market sustain?
So for example, states like Oklahoma have gone a route where they haven't really introduced
a cap and it's just you pay the license fee and you can open up shop.
So we may want to essentially compare states that have caps in those that don't and not
just caps, but essentially policies that affect how many dispensaries can open up and why
well, going to incorporate a little bit of economics here, not to scare anyone off, but
this is a good introduction to industrial organization and we're just scraping the
iceberg here.
But basically, industrial organization economists break down the market into four different
structures.
Of course, if you studied economics, you know about perfect competition and this is the
baseline assumption in most models.
We're assuming infinite firms, no barriers to entry, no market power.
So price is equal to cost.
So that means no one's making a profit.
This is quite the assumptions.
And so, you know, similarly on the other side of the spectrum, you have the monopoly where
you have one singular firm, right?
There's only one.
So the barriers to entry is essentially infinite.
You can't enter and they have high market power.
So they can set the price to maximize their profits.
And they do so.
There are a few examples of monopolies.
So in certain geographical areas, you may have one power company or one internet provider.
So this is possible, but one can argue that there's not that many examples of pure monopolies.
And then, like we said, you don't really see perfect competition.
So that brings us to the two market structures that are observed empirically.
And these are monopolistic competition, where you have many firms, low barriers to entry,
low market power.
So this you could think of as restaurants.
There's many, many, many, many restaurants.
The barriers to entry, there are some, but they're relatively low.
You know, think of the lemonade stand.
And their market power is relatively low.
You can't set price that much above cost.
So your profits are going to be low.
So that's why you see in the restaurant industry, low profit margins and lots of turnover.
So you see lots of firms entering, lots of firms exiting.
As you move, and you can think of this almost as a spectrum.
So as you move towards monopoly, you're going to have fewer and fewer firms.
You're going to observe more and more barriers to entry.
And each player depends on game theory and market dynamics.
So their market power may be low, it may be high.
So it's a bit more indeterminate, but one can generally say that they've got medium
to high market power.
So this is just a little background of economics.
Why does it matter?
Well, you can think of dispensaries per 100,000 adults as the measure of the number of firms.
So if we ever saw infinite dispensaries per 1,000 people, that would be perfect competition.
If we just saw a really, really small number where there's just one, it would be a fraction,
where there's one dispensary for everyone.
So that's sort of the idea is we can use the dispensaries per 100,000 people as a measure
of the number of firms in the market relative to population.
And it doesn't control for costs.
So one would assume costs of say opening a retailer very state by state.
That goes back to the barriers to entry.
So we can't measure costs, we can't measure profits.
But the next best thing we can measure revenue.
So we can essentially measure revenue as a proxy for profit.
Like I said, it's not necessarily going to be one to one because costs are probably not
the same across states.
So long story short, if we're just looking at a couple of these, lots of dispensaries
in Alaska, the next most dense, as far as dispensary goes, is Oregon.
So 23 dispensaries per 100,000 people.
And then on the other end of the spectrum, you've got the low point of Illinois, one
dispensary per 100,000 people, and then you've got some other ones like Vermont, Maine, and
Arizona, and Massachusetts that also have a low number of dispensaries per 100,000.
Well just visually, if you're just trying to do just a visual correlation here, you
see, oh look, Oregon revenue per dispensary is about 1.5 million.
On the other side, Illinois, one dispensary per 100,000 revenue, 10 million annual.
Well obviously it's not like a perfect correlation here because look at, we've got some outliers,
right?
Maine.
Maine doesn't have many, and so look, this is real interesting.
So here Maine has 1.4 dispensaries per 100,000.
Massachusetts has a comparable 1.5.
But look at the difference in revenue in a Maine retailer on average versus a Massachusetts
retailer.
So on average you're having less than a third of a million in revenue in Maine, whereas
in Massachusetts it's almost 8.3 million per year.
So we can use statistics and we can actually quantify what this relationship is.
So without further ado, let's do just that.
And I forgot to commit today's code.
So long story short, you can check out the cannabis data science repository to get the
code as we go through it.
So for those who just joined today, maybe a little hard to follow along, but in the
future I'll post the code here so that way you can follow along in Python if you're a
Pythonista.
If you're not the end of the world, all of this is just statistics and can be reproduced
in your favorite programming language.
So without further ado, I've just taken this data and put it into Excel.
Bear with me, Excel updated itself.
So long story short, here's the data.
I just copied and pasted it from this table and I'll share the link with you.
Here's the link right here to the report.
And so I'll share this with you after the meetup.
And so this is the technical memorandum, as I said, commissioned by Nevada, produced by
RCG economics.
They probably didn't produce the report for free.
So that means the statistics have some value, what the value is, not 100% certain, depends
on how much they paid.
But long story short, let's calculate these statistics.
So first, let's just use their data.
And then, as I said, we're going to try to reproduce their statistics.
So first things first, we read in the retailer stats.
Let me just make sure.
OK, so we've got the retailer statistics here.
Also, if I'm just droning on it too long at any point, just feel free to chime up and
speak up if you have any questions at any point.
OK, so we've got our states here.
As we can see, some states are missing revenue per retailer.
At the end of the world, we just don't have those observations to work from.
So here, we just exclude all the, and I'm calling them observations, because as you
can see, we've got two observations here in Nevada, including Nevada as a whole.
So in my analysis today, I'm including these two counties.
If you think it's better to exclude them, then they can be excluded.
For our sake, I'm not a big fan of throwing away data.
And so I think they're interesting observations, and so I'm going to include them, because
we only have 12 observations here.
So I don't want to throw any away if I can help it.
And so we only have 12 observations, so I'm sure a lot of you have taken statistics, and
your statistics professor probably told you that you would like a minimum of maybe 25
or 30 observations to run a regression.
And this is true because the law of large numbers starts to kick in the more and more
observations you get.
And so what the law of large numbers is, is the more you observe a data set, the more
likely you are to have a representative sample.
So if you observe every point, then you've observed the population.
If you're only observing certain points, you have a sample of the population, and basically
the larger and larger your sample gets, the more representative it gets, and you're going
to have more accurate results.
Here in the Meetup group, we take what we're given and we make the best of it.
So we're going to run a regression here with 12 observations.
And we're essentially just going to run a regression of revenue per retailer on retailers
per 100,000.
So I'm assuming that the direction of the relationship is the more retailers that are
licensed into the market, I'm assuming that's independent of revenue per retailer.
So the commission is just licensing people as they commit, as they make successful applications.
And then the revenue per retailer is dependent on the number of retailers in the market.
So our dependent variable, retailers, independent revenue.
We run our regression.
We have a weak fit, so low r squared, so there's a lot of variation left to be explained.
However, even with our low number of observations, we do have...
If you're a frequentist, you almost would be able to declare statistical significance
from this parameter here.
But as some of you may have picked up, I'm a bit more on the Bayesian spectrum in that
I don't put as much importance on significant values as certain people, and I'm open about
my biases and my priors.
But I still do frequentist statistics.
So anyways, but that's a whole nother can of worms.
If you're interested in statistics, do some searching about frequentism and Bayesian statistics.
Whole can of worms, so I'm not going to get into it today.
But long story short, we at least have a coefficient here so we can make a statement about the
statistical relationship, the correlation between these variables.
And in English, essentially what we're saying is...
So here, we estimated a negative coefficient on retailers per 100,000, which is sort of
what we expected in that as there are more and more retailers entering the market, revenue
per retailer is decreasing.
So they're cannibalizing each other.
It's not just one retailer comes online and all those sales just come out of thin air.
No, there's only so many sales happening in the market.
And if another retailer enters, some sales will filter from other retailers to that retailer.
So what we're saying is, if retailers per 100,000 adults increases by one, everything
else held constant, you would expect the average revenue per retailer to decrease by 350,000
per year.
So it sounds like a lot.
Just to put this in perspective real quick, I was just going to show you Massachusetts
population here.
To kind of put things into perspective here.
One second.
Okay.
So right.
So one per 100,000.
Well, that would be
approximately 69 retailers in Massachusetts.
So what our analysis would suggest is, if 69 retailers came online in Massachusetts,
everything else held constant, you would expect the average revenue per retailer to decrease
by 350,000.
Well, we've got a bunch of data here.
If you listen to Edward Tuft at all, and if you haven't, I'd recommend checking out his
books.
He's all about visualizing data.
So enough looking at the numbers.
Let's visualize them.
So here is a scatter plot of revenue to retailers plus the regression line.
And so I'll share the code with you, but here I'm going to make a, I'm going to redo this
same figure inspired by Edward Tuft, and we're going to make it look nice.
And then we're going to talk about it because that's what we're here to do.
So just made a beautiful chart.
And so now let's talk about that rather than just looking at numbers.
So here we have plotted all of our observations, which we have from the table that was prepared
for Nevada.
As we observed visually earlier, just from the table, Maine does in fact look to be like
an outlier.
So that's a whole other research question of its own.
Why is Maine an outlier?
So it's not just population alone.
There's something else going on there.
So it would be worthwhile trying to answer that question.
And then we see the negative relationship here.
And we see, as we observed, Oregon on the far end where they have many retailers per
1,000 adults.
The average revenue is low.
And what I think is interesting is it looks like there's almost two groups here.
So if you were going to do like a principal component analysis and try to group these,
I would almost do two groups.
I would do these.
I would do this group, like this low revenue per dispensary.
And that would be Maine, Michigan, Washington, Colorado, and Oregon.
And I would try to find some, maybe there's some trait that all of these states have that
this group doesn't, right?
Because this looks almost like a separate group here, where you've got Arizona, Illinois,
Oregon, Nevada, Massachusetts, California.
What is different about those states than these states?
Well, that's a whole question that we may not be able to answer today.
So that's what being a scientist is all about, right?
Now it's time to hit the books, do some research,
and look at the cannabis laws and everything
in these states versus these states
and see if there's some sort of systematic difference.
One can note, right, Washington, Colorado, the two first states
to legalize cannabis, followed shortly by Oregon.
Michigan's interesting because they legalized in 2018,
but they have a long history of medicinal cannabis
that may go back till 2008 or so.
Don't quote me on that.
So and I don't know the history of Maine cannabis that well.
Most people know the history of California cannabis, right?
That's where cannabis is famously come from historically.
And if you talk to people in the industry,
California's got everyone will say that they've
got big problems right now.
So it's tough for licensees there because anecdotally,
there's still a large illegal market in California.
I always find it funny when people
try to estimate the size of the illegal market
because I mean, that's the whole point is it's not measured.
It's illegal.
So if we were keeping a tab on it,
then it wouldn't be possible.
So by its nature, it's unmeasured.
But some people estimate that maybe 50% to 75%
of all production in California is on the black market.
And so that may have some sort of effect
on what's going on with the structure in California.
Massachusetts is much different, right?
So Massachusetts has tried to be a industry leader,
and they're open about that.
So if you listen to the public meeting,
they'll talk about how they are.
It's not a goal of theirs, but they
don't mind being a leader for other states.
And they've consulted with Maine, New York, New Jersey,
Virginia, Rhode Island, Connecticut, right?
They are talking with all of the leaders
and commissions in all of these other states
to talk about what's working well for them and what's not.
So Massachusetts is definitely trying to be a leader here.
And then I just don't know that much
about some of these other states.
Nevada is relatively new.
Don't know too much about Nevada.
Illinois allowed adult use in January 1st of 2020.
Arizona is an interesting market because they've
had medicinal for a long time, and they just permitted
adult use not that long ago.
So long story short, we just have the relationship here.
So we don't know for certain why the states are shaking out
along these different points.
But we can observe that there's a negative correlation
between retailers per 100,000 annual revenue.
And this is in line with our economic theory, right?
The more firms there are in the market,
the more competitive it becomes.
And they're not able to raise prices above costs.
And so their average revenue may decrease.
Once again, we're not measuring profits here.
So it's hard to speak to the economic side.
Now, we want to add more data points
and see if this relationship holds up, right?
Because we only have 12 data points here.
So it would be really cool if we could add many, many, many,
many, many more data points to the economic side.
Many, many, many, many, many more data points.
The number of states we have is limited.
So I'm going to introduce you to panel data.
And I'm just going to talk about it briefly today.
If you tune in the Saturday morning statistics this week,
I believe I'm going to try to estimate hopefully both
the fixed effects and the random effects model.
And so what are these models?
OK, well, we can essentially create panel data here.
If we look at this data, we've got x sub i.
So we've got x of Arizona, x of California, x of Colorado.
Well, guess what?
There's another dimension here, time.
We can thank Einstein for discovering this dimension.
But time's a whole other dimension here.
So we can basically say we've got an individual dimension,
which is our state.
And then we've got a time dimension, which is over time.
So we can actually measure all of these statistics over time.
So what we have here is a snapshot of 2020.
But if we aggregate the data for each point in time
so that we know the revenue and the number of retailers
in each state at each point in time,
well, we're going to have a lot of observations.
And we can use fixed effects and random effects models
to better estimate our parameter here, beta.
So here, we already estimated beta
as negative 350,000.
However, this may be biased since we only
have 12 observations.
So we can get a lot more observations
and see if our estimate is still the same or if it's different.
And essentially, we can just get a better measure.
Well, just to go ahead and show you
how we're going to be going about that for each state
and how this could lead to discrepancies against the data
we've already seen.
Well, so we'll eventually want to do this for each state,
California, Oregon, Colorado, Illinois, so on and so forth.
However, for today, we can just look at Massachusetts.
So in prior weeks, we've looked at this data.
And so please check out some of the prior videos
just to see how we read in this data
and some of the intricacies of the data.
Massachusetts has a awesome open data API
so we can read in the number of licensees and sales
on an almost real-time basis.
So let's do exactly that.
So once again, I'll share the source code with you.
I'm sort of going to be moving through it quick just
so we can get to the interesting parts.
So long story short, just going to read in the licensees data
and the production data.
So licensees, we've got 914.
And then if you were going to look
at a specific observation, we have many, many
data points for each licensee.
In particular, we're going to be looking at retailers,
so all of the licenses with license type retailer.
Well, this brings us back to what we talked about
at the very beginning.
Not all licensees are operational.
So if we were just going to calculate sales per retailer,
we may overstate how many retailers are actually
operating.
So what you can do is you can just
look at the retailers here that have a final license.
So the retailers would be all of the final licenses
where the license type is a marijuana retailer.
So 191.
And this is where it gets tricky comparing apples to apples.
So we're measuring 191 retailers.
Well, some of these may have come online since November 18.
Not impossible.
But long story short is I think these ones that
have the final license in the open data,
I believe those are the ones that are commensurating
operations.
Because in this last meeting, they just
approved retail licenses for a hand tail of retailers.
So long story short, there's already confusion here.
So I'm not even going to pretend that I have this figured out.
I don't have any clue how to figure out
which of these retailers are actually operating.
And it's gotten to the point where
I'm going to have to email the commission,
try to get a hold of their data scientists,
and try to figure out what exactly they mean when they're
talking about these license types.
Because like I said, the data we're getting in the open API
is not matching up with their internal data
that they presented at the meeting.
And what we're going to do is we're just
going to do the best we can and basically consider
any retailer operating at the time
they got their final license.
So as you can see, this retailer has a provisional license.
They have not gotten their final license yet.
So I'm going to consider this retailer as not operating.
Well, we could do a bit of homework.
We could actually look at one of these.
We could even check out their website
or give them a call, Flower and Soul.
And that's where you have to be a bit of an investigator
when you're doing all of this.
We could even call up Flower and Soul and ask them,
hey, are you operating or not?
So as I said, there's a lot of homework to be done here.
So enough of crepacing all of that.
I'm going to do my best here to calculate
the number of retailers at any given point in time.
So we're going to use the date of final licensure.
And we're basically going to say the retailer started operating
when they got their final license.
Is this perfect?
No.
But this is the best I can think of for the time being.
As we learn more, we can calculate
better and better statistics.
Well, let's go ahead and calculate these.
So we're in specific looking at retailers.
So here we see our count of retailers going up and up
and up and up until the point where we have the, well,
now we're counting 193.
This should be 191.
So there's a discrepancy here between those
that have a final license and then those that have
a final licensure date.
So that's more investigation.
But we're almost here at the end,
so I'm just going to power through this,
keeping in mind that we've got some imperfect statistics here.
So we've got our retailers going up and up and up and up.
Well, look at this.
In 2020, well, you don't have the same number
of retailers at the beginning of year
as you do at the end of the year.
So that makes me wonder, when they calculated
their statistics, how are they calculating
the number of retailers in 2020?
Are they using the end of year number of retailers?
Are they using 2021's retailers?
How are they calculating these numbers here?
Because this is an increasing number over time.
Well, we've got sales per week.
Right?
We've got weekly sales.
We can get weekly sales.
So here is sales over time.
And those of you just joining us today,
Massachusetts has a real interesting market here,
where in 2020, they closed for two months.
Right?
I mean, you may want to put an asterisk here
on the Massachusetts one and say Massachusetts
was closed for two months in 2020.
Right?
Because I mean, that's just not even taken into consideration.
Like 2020 was closed for two months.
So 2020 is quite the atypical year in Massachusetts.
Well, just to keep powering through this
and making some statistics here, we can now do sales per retailer.
And we've calculated this in weeks past.
But now we actually get to calculate it
with the number of retailers that we think are in the market.
And so there is sales per retailer per week.
Well, we now have sales per retailer.
Well, we can try to measure apples to apples.
What is our estimate of sales per retailer
in Massachusetts in 2020?
We're saying 10.86 million.
Well, that is not 8.36 million.
So we've got a different number here.
We probably have a different number
because we're taking into consideration the number
of retailers that were actually operational at any given time.
So we're calculating a different statistic than they are.
If they're just using sales per dispensary at the end of 2020,
well, I think they need to specify this.
So basically, this is sort of the lesson I'm driving home today
is you need to be really explicit in your notes
and how you describe.
And like I said, they've got a report.
So they could be buried in the report somewhere.
And it probably is.
So next week, I'll make my corrections
and quit slamming these guys.
And tell you more about how they created their statistics.
Because like I said, it's probably in the report.
But like I said, you need to be really explicit in your notes.
And that's the whole thing about data science.
And that's where I think it needs to be done out
in the open and out in the light.
Because there are so many assumptions
you can make along the way.
It's like, are you calculating average?
It's like, how are you calculating this average?
Are you calculating average sales per retailer,
taking into consideration which ones were actually open?
Or are you just estimating it using the end of year total
and keeping in mind that that's going to be an estimate and not
the actual?
So long story short, we can go ahead
and we've cast some uncertainty there.
Well, we can say, OK, let's at least look
at the retailers per capita.
Hopefully, that one we can maybe have some agreement upon.
And once again, our numbers vary because we're not
calculating apples to apples here.
Because I'm pulling the best I can do is pull pop.
And maybe this is the best I can do.
So I may have to revise it.
But pulling just total population.
But you see, they use the actual adult population.
So not quite apples to apples.
But we're estimating that the retailers per capita,
you know, in 2020 was on average around one.
As you can see, it's growing up.
And you see the year looks like the year end around 2020,
it was 1.5, which is what they measured.
They measured 1.5 dispensaries per 100,000.
So that may be like year of end total.
But at the beginning of the year,
there was only 0.5, approximately 0.5 retailers
per 100,000 people.
So when you just see, oh, in 2020,
this number changed in 2020.
So long story short, I think I may wrap it up
here for today.
But for the coming weeks, this is essentially
going to be our goal, or at least next week,
is basically get the data from more states,
continue to reproduce these statistics, retailers
per capita and sales per capita, and see
if we can't replicate all of these statistics here.
And as we said, we're not doing perfectly apples to apples.
So if there is some slight discrepancy,
that's not the end of the world.
But we just want to make sure that we're not
systemically measuring things differently here.
And as I said, we get to create panel data.
So here, we actually have sales per retailer
throughout time in Massachusetts.
So we have many, many, many more data points
so we can run a regression.
And maybe, in fact, I don't have the time to do it today,
but it would be interesting to do the regression of this
just on Massachusetts.
So redo this regression of revenue
against retailers per 100,000, but just do it in Massachusetts
and see what the effect is, see what the beta is
in Massachusetts.
And then, as we said, replicate it for all the other states.
OK, so this is sort of what we began with.
And now we're down a rabbit hole where
we're questioning the certainty of the data.
And we're going to have a lot of work on our hands.
But this is the work of a data scientist.
So I'm going to go ahead and stop presenting for today
just to save some for next week and just not go overboard.
However, does anybody have any questions
from all of this work today?
I just had a comment.
So we did the linear regression.
And I'm assuming we are assuming that it's not normalized,
right, because the data points are skewed on one side.
And there are a couple of points, like two or three
on the other end.
Can you please repeat that question?
My connection got spotty there for a second.
OK, yeah, we did the linear regression.
And I was just making a comment that obviously we
can't take this for what it is because the majority
of the data points were skewed to the left.
It's not normally distributed.
So I'd be curious to see how, like you already pointed out,
that we need more data points.
100% correct.
That's where the law of large numbers comes in,
is if we did have more and more observations,
our errors may approach the normal distribution.
Yeah, because even though it's a negative correlation,
is it really because of how the data points are skewed?
Yeah, because it's significant.
But yeah.
That's just a perfect observation.
And I couldn't have said it better myself.
Because I mean, you hit on a key point here
is you can't put too much stock into these estimates we're
doing because we only have 12 observations.
There is probably systemic differences going on.
So that's essentially why we're on this road to get panel data,
is to increase our statistical power.
So if we're able to get these observations throughout time,
then we may be able to estimate the relationship a little
better than we did today.
Because like I said, we're saying,
oh, if one retailer enters per 100,000,
it may go down by 350,000.
I wouldn't put too much stock on those numbers.
So if you're doing policy decisions,
I don't know if I would make them based on those numbers.
So welcome to the world of statistics and data science.
So I hope I've cast enough uncertainty.
But long story short is, I think it's
useful to at least look at the data, at least plot it,
see what the regression line is.
But just take it at face value.
So just know this is 12 observations.
There could be measurement error going on.
There's a lot of factors that are questionable here.
So further investigation is needed.
But that's my typical is just hedge everything.
Be skeptical of data.
Be skeptical of statistics that people put before you.
Always ask about methodology.
Ask about any assumptions that may
have been made along the way.
And likewise, when you're presenting your results,
be upfront about the assumptions you make
and any shortcomings in the data, and so on and so forth.
So it's the best we can do in an imperfect world.
Well, definitely feel free to reach out throughout the week
if you have any questions, one thought cannabis or data
science or cannabis data science.
And if you have any good ideas, avenues for future research,
yeah, always feel free to reach out.
Always happy to have a discussion.
Well, thank you all for coming.
Enjoy your Thanksgiving.
If you're taking time off from work, if not, then
keep your nose to the grindstone.
And then until next time, stay productive.
Feel free to tune in through Saturday morning statistics.
We'll look at panel data models.
And then next week, we'll pick up
with our comparative analysis of the various states.
Thank you so much.
You're welcome.
Have an awesome one, everyone.
You, too.
Thank you.
You're welcome.
Bye.
Bye now.
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