1 00:00:00,000 --> 00:00:11,040 Welcome to the Cannabis Data and Science Meetup Group. 2 00:00:11,040 --> 00:00:17,120 So we meet here to talk about cannabis data, do some exploratory analysis, crunch some 3 00:00:17,120 --> 00:00:20,200 numbers, have fun in general. 4 00:00:20,200 --> 00:00:25,600 So just to give a brief introduction, my name is Keegan. 5 00:00:25,600 --> 00:00:32,960 I started working in the cannabis industry as a laboratory analyst where I did everything 6 00:00:32,960 --> 00:00:40,080 from hands-on analysis, so just the sample preparation and gradually found some ways 7 00:00:40,080 --> 00:00:43,520 to automate some of the lab work. 8 00:00:43,520 --> 00:00:49,880 Next thing you know, I'm a software developer and I've now launched a company, Cannalytics, 9 00:00:49,880 --> 00:00:56,080 primarily to help provide software solutions to help people in the cannabis industry's 10 00:00:56,080 --> 00:01:01,560 lives, primarily labs, lives to be easier. 11 00:01:01,560 --> 00:01:04,640 And then there's just such a high demand for analytics. 12 00:01:04,640 --> 00:01:11,000 And so that's what my background's in, so just trying to lend a hand. 13 00:01:11,000 --> 00:01:13,840 And how about yourself, Stephen? 14 00:01:13,840 --> 00:01:19,240 I guess Paul and Heather can introduce themselves here in a second, so I'm curious what brings 15 00:01:19,240 --> 00:01:21,640 you to the group today. 16 00:01:21,640 --> 00:01:29,720 So surprisingly, I am not a cannabis user and I don't intend to be. 17 00:01:29,720 --> 00:01:32,280 My interest actually is about terpenes. 18 00:01:32,280 --> 00:01:39,080 And so terpenes are also interesting to many cannabis users. 19 00:01:39,080 --> 00:01:44,440 And my interest is in plant terpenes. 20 00:01:44,440 --> 00:01:51,480 So I'm a consultant to a natural products company that makes insect repellents based 21 00:01:51,480 --> 00:01:53,560 on natural products. 22 00:01:53,560 --> 00:02:02,240 And plant products are, most of what you smell are volatile molecules, which are terpenes, 23 00:02:02,240 --> 00:02:05,880 10-carbon molecules that are terpenes. 24 00:02:05,880 --> 00:02:11,440 And so I'm very, very interested in terpenes, modeling terpenes, and particularly understanding 25 00:02:11,440 --> 00:02:17,880 by plant species like rosemary and so forth. 26 00:02:17,880 --> 00:02:23,920 If you take the essential oils or press them, or use distillation to take the oils out, 27 00:02:23,920 --> 00:02:30,800 what is the relative contribution of the different constituent molecules, constituent terpenes? 28 00:02:30,800 --> 00:02:34,160 And so I've done a fair amount of work in that area. 29 00:02:34,160 --> 00:02:37,720 And then I'm very interested in the tidy verse in modeling and so forth. 30 00:02:37,720 --> 00:02:43,720 So it turns out that one of the inexpensive ways of trying to understand plant terpenes 31 00:02:43,720 --> 00:02:50,160 more generally, or outside of marijuana, well, it turns out, if you want to understand that 32 00:02:50,160 --> 00:02:54,640 stuff, the inexpensive way to go is to use the marijuana testing labs. 33 00:02:54,640 --> 00:02:58,160 So I'm interested in understanding more about the marijuana testing labs. 34 00:02:58,160 --> 00:03:04,840 Also reportedly, some of the labs are cheating by showing higher THC. 35 00:03:04,840 --> 00:03:11,080 And I also see inconsistencies reported between the labs for the same product. 36 00:03:11,080 --> 00:03:14,200 And so I'd like to understand a little bit more about that, because from a production 37 00:03:14,200 --> 00:03:25,160 perspective, I'd like to incorporate marijuana testing labs in production of our plant-based 38 00:03:25,160 --> 00:03:26,160 insect repellent. 39 00:03:26,160 --> 00:03:28,440 So it's a mosquito repellent, shipping, and so forth. 40 00:03:28,440 --> 00:03:31,960 But to go through and just easily go through and test, which primarily I'm interested in 41 00:03:31,960 --> 00:03:32,960 the terpenes. 42 00:03:32,960 --> 00:03:35,280 Fantastic. 43 00:03:35,280 --> 00:03:38,320 Your interests align with the group perfectly well. 44 00:03:38,320 --> 00:03:42,320 So we've looked at a lot of laboratory data. 45 00:03:42,320 --> 00:03:49,600 Terpene data is particularly interesting and an area that we have neglected so far. 46 00:03:49,600 --> 00:03:56,160 So we've been working with this data set from Washington State, where we have a rich set 47 00:03:56,160 --> 00:03:58,840 of cannabinoid data. 48 00:03:58,840 --> 00:04:06,320 So and even, in fact, Paul and I were talking about this briefly. 49 00:04:06,320 --> 00:04:13,160 So essentially, we're interested in maybe trying to see, OK, is there any hybrid, sativa, 50 00:04:13,160 --> 00:04:16,240 indica distinction in the numbers? 51 00:04:16,240 --> 00:04:23,080 And so I know people have done work with that in looking at terpenes. 52 00:04:23,080 --> 00:04:28,700 So it would be interesting to see. 53 00:04:28,700 --> 00:04:31,880 So long story short, I think you're in the right place. 54 00:04:31,880 --> 00:04:36,040 And in fact, we may have a lot to learn from you as well. 55 00:04:36,040 --> 00:04:37,040 Great. 56 00:04:37,040 --> 00:04:38,040 Awesome. 57 00:04:38,040 --> 00:04:45,480 We'll maybe return to that here in a second, just to go ahead and go around the group. 58 00:04:45,480 --> 00:04:48,640 So Adam, another new face. 59 00:04:48,640 --> 00:04:51,560 So it's good to see you here. 60 00:04:51,560 --> 00:04:56,000 Would you be interested in introducing yourself to the group? 61 00:04:56,000 --> 00:05:06,960 If not, Heather and Paul, would you mind introducing yourselves to Stephen real quick? 62 00:05:06,960 --> 00:05:08,200 So Paul? 63 00:05:08,200 --> 00:05:09,200 Sure. 64 00:05:09,200 --> 00:05:10,200 Hi, Stephen. 65 00:05:10,200 --> 00:05:13,000 My name is Paul Kitko. 66 00:05:13,000 --> 00:05:18,880 My day job, I'm a data scientist with one of the big three automotive manufacturers. 67 00:05:18,880 --> 00:05:24,240 What brought me here is I was actually finished up a master's degree in data science. 68 00:05:24,240 --> 00:05:30,480 And I was looking for a big juicy data set to get my hand on to do my capstone project. 69 00:05:30,480 --> 00:05:34,360 And Keegan was helpful in getting me started in that area. 70 00:05:34,360 --> 00:05:39,120 So I was using this Washington state data set to do some what's called market basket 71 00:05:39,120 --> 00:05:45,460 analysis and retail analysis on sales and dispensaries. 72 00:05:45,460 --> 00:05:50,180 So I just wrapped that up last week, officially. 73 00:05:50,180 --> 00:05:51,800 So I'm done with my master's program now. 74 00:05:51,800 --> 00:05:52,800 So thanks, Keegan. 75 00:05:52,800 --> 00:05:54,560 Thank you, Paul. 76 00:05:54,560 --> 00:05:55,800 Thank you. 77 00:05:55,800 --> 00:05:58,320 And also, Stephen, you mentioned the Tidyverse. 78 00:05:58,320 --> 00:06:03,840 I like using the Tidyverse because it seems to be, at least for me, I'm not a natural 79 00:06:03,840 --> 00:06:05,920 born programmer. 80 00:06:05,920 --> 00:06:10,080 And in fact, if I didn't have to be programming, that'd be great. 81 00:06:10,080 --> 00:06:14,120 But it seems the Tidyverse is very approachable and easy to use. 82 00:06:14,120 --> 00:06:17,720 So that's my tool of choice as well. 83 00:06:17,720 --> 00:06:20,280 That's about it. 84 00:06:20,280 --> 00:06:21,280 That's interesting. 85 00:06:21,280 --> 00:06:26,760 Whenever there's multiple lines of agreement, I get interested. 86 00:06:26,760 --> 00:06:29,240 So what's this Tidyverse? 87 00:06:29,240 --> 00:06:32,920 So this is a data wrangling tool? 88 00:06:32,920 --> 00:06:43,760 So in R, there's a huge collection of packages for data analysis and statistical analysis. 89 00:06:43,760 --> 00:06:51,080 And it's just written in a way, at least from my perspective, that's easy to pick up. 90 00:06:51,080 --> 00:06:53,160 It's actually, to me, it's easier than Python. 91 00:06:53,160 --> 00:06:55,760 So that's kind of like gravitated towards it. 92 00:06:55,760 --> 00:06:56,760 OK. 93 00:06:56,760 --> 00:07:02,720 Well, I could maybe supplement Paul's comments. 94 00:07:02,720 --> 00:07:05,800 So generally, you'll find people who are doing production work. 95 00:07:05,800 --> 00:07:09,920 That is to say, people have come up from this computer science background. 96 00:07:09,920 --> 00:07:13,200 Typically, they'll have learned Python in school, and they like Python. 97 00:07:13,200 --> 00:07:16,320 And it also feeds into machine learning well. 98 00:07:16,320 --> 00:07:22,560 People who are statisticians or PhDs coming from areas that are not computer science, 99 00:07:22,560 --> 00:07:29,520 typically, they'll use R. R has been around for many years. 100 00:07:29,520 --> 00:07:35,680 As Paul's indicated, there's now a new collection, a subcollection, if you will, of highly integrated 101 00:07:35,680 --> 00:07:36,920 tools. 102 00:07:36,920 --> 00:07:39,000 So they snap together like Legos. 103 00:07:39,000 --> 00:07:42,840 And the goes out of one goes into the next. 104 00:07:42,840 --> 00:07:43,840 OK. 105 00:07:43,840 --> 00:07:47,720 So you can think of them as block modules. 106 00:07:47,720 --> 00:07:53,280 And so you can kind of connect these modules together to do hugely creative things. 107 00:07:53,280 --> 00:07:56,120 So it's abstracted for you, that is to say. 108 00:07:56,120 --> 00:08:01,880 So instead of trying to get down at the bit and byte level, you're dealing at a higher 109 00:08:01,880 --> 00:08:05,720 level of extraction, kind of like Visio or so forth. 110 00:08:05,720 --> 00:08:11,560 So the advantage here is, as Paul's pointed out, people who are not programmers can move 111 00:08:11,560 --> 00:08:12,560 quickly. 112 00:08:12,560 --> 00:08:17,560 I say relatively quickly with complicated data sets. 113 00:08:17,560 --> 00:08:22,680 So once you get a data set, for example, that's beyond two pages of Excel, it starts to be 114 00:08:22,680 --> 00:08:28,960 really difficult to try to understand, to visualize it, to model it, to just clean it 115 00:08:28,960 --> 00:08:30,960 up, all that sort of things. 116 00:08:30,960 --> 00:08:40,360 And so there's a fellow named Hadley Wickham, who has instigated this thing called the tidyverse. 117 00:08:40,360 --> 00:08:43,000 And it basically looks at the complete workflow. 118 00:08:43,000 --> 00:08:45,440 So if you start off with, hey, I've got some raw data. 119 00:08:45,440 --> 00:08:46,440 It's always messy. 120 00:08:46,440 --> 00:08:51,680 Maybe 78% of your time is spent cleaning up that dirty data. 121 00:08:51,680 --> 00:08:54,320 And then you want to start saying, well, what do it mean? 122 00:08:54,320 --> 00:08:55,600 What can I do with it? 123 00:08:55,600 --> 00:08:58,680 And how can it help inform my business? 124 00:08:58,680 --> 00:09:03,080 So there are a lot of tools around visualization that's important first step. 125 00:09:03,080 --> 00:09:08,960 And then trying to characterize what equations or what algorithms might model that and what 126 00:09:08,960 --> 00:09:13,840 might that inform about what you do on a go forward basis. 127 00:09:13,840 --> 00:09:18,040 So for example, you might say, hey, I have a current, you know, I grow marijuana right 128 00:09:18,040 --> 00:09:21,320 now and I want to increase a certain terpene. 129 00:09:21,320 --> 00:09:22,320 How can I do it? 130 00:09:22,320 --> 00:09:26,520 Well, one way you might try doing that is get some insects in to start munching on it. 131 00:09:26,520 --> 00:09:33,840 Because terpenes are made by plants to keep, you know, munching insects away. 132 00:09:33,840 --> 00:09:36,320 Or just you might say, I'm going to give it less water. 133 00:09:36,320 --> 00:09:37,320 See what happens. 134 00:09:37,320 --> 00:09:39,320 I'm going to pass it different ways. 135 00:09:39,320 --> 00:09:40,680 And then you'll get different data. 136 00:09:40,680 --> 00:09:44,080 And if you have that different data, then you can start to model it and say, hey, if 137 00:09:44,080 --> 00:09:54,960 I give it 10% less water and 20% more light, what does that do to the resulting product? 138 00:09:54,960 --> 00:09:57,920 You're hitting on something real clever. 139 00:09:57,920 --> 00:10:02,080 So this is something that several people are talking about. 140 00:10:02,080 --> 00:10:07,280 And even somebody's talking about with kinetics is essentially, yes, so just measuring the 141 00:10:07,280 --> 00:10:12,640 things, some of the inputs in your production and trying to estimate some of the yields 142 00:10:12,640 --> 00:10:15,640 or at least some of the effects on the yields. 143 00:10:15,640 --> 00:10:16,640 Yeah. 144 00:10:16,640 --> 00:10:20,880 I mean, if you were doing a production process of manufacturing something, you'd monitor 145 00:10:20,880 --> 00:10:21,880 it. 146 00:10:21,880 --> 00:10:25,480 You'd say, you know, here's what I expect to be in spec when it comes out of the production 147 00:10:25,480 --> 00:10:26,480 line. 148 00:10:26,480 --> 00:10:32,840 But natural products, especially, well, I know more about essential oils than I do about 149 00:10:32,840 --> 00:10:33,840 marijuana. 150 00:10:33,840 --> 00:10:37,840 But in natural oils, the way it's recovered is there's different ways to do it. 151 00:10:37,840 --> 00:10:42,960 You can physically compress it to squeeze out oil, or you can do something called steam 152 00:10:42,960 --> 00:10:43,960 distillation. 153 00:10:43,960 --> 00:10:46,640 That gives you two separate products. 154 00:10:46,640 --> 00:10:52,000 So that's a processing example that you might want to change or adjust your process. 155 00:10:52,000 --> 00:10:55,120 On the growing side, it's a lot like growing wine. 156 00:10:55,120 --> 00:10:56,120 Okay. 157 00:10:56,120 --> 00:10:58,880 You know, the terroir makes the difference. 158 00:10:58,880 --> 00:11:03,520 So what's in the soil, the water, the sun. 159 00:11:03,520 --> 00:11:10,640 All that stuff will greatly change the essential oil composition. 160 00:11:10,640 --> 00:11:15,120 So just as a general point, I would say there's probably some lessons to be taken from looking 161 00:11:15,120 --> 00:11:20,960 at just general plant science and then applying those to the specific case of marijuana. 162 00:11:20,960 --> 00:11:24,340 Because plants, there's a lot of conservation across plant types. 163 00:11:24,340 --> 00:11:28,800 So what happens in one species of plant very likely shows up in another species of plant 164 00:11:28,800 --> 00:11:33,360 as well, but different effect sizes. 165 00:11:33,360 --> 00:11:34,360 Okay. 166 00:11:34,360 --> 00:11:37,720 And this is where we like the cross pollination of ideas. 167 00:11:37,720 --> 00:11:43,240 And so just to introduce our two new guests real quick. 168 00:11:43,240 --> 00:11:49,400 So essentially, Adam, Mike's not working, but in the chat, Adam's also getting a master's 169 00:11:49,400 --> 00:11:51,200 degree in data science. 170 00:11:51,200 --> 00:11:54,440 So Paul's proving this is quite useful. 171 00:11:54,440 --> 00:11:58,360 And then you have experience working with CAMS companies in California. 172 00:11:58,360 --> 00:11:59,360 So excellent. 173 00:11:59,360 --> 00:12:01,480 We'd love to hear from you there. 174 00:12:01,480 --> 00:12:05,760 California is one of the states we haven't really studied too much in depth. 175 00:12:05,760 --> 00:12:12,080 So it'd be interesting to hear your take on things in the markets there. 176 00:12:12,080 --> 00:12:15,880 And now, Sasha, it's awesome to have you as well. 177 00:12:15,880 --> 00:12:17,400 So welcome to the group. 178 00:12:17,400 --> 00:12:23,600 If you want to take just 30 seconds to introduce yourself, you're more than welcome to. 179 00:12:23,600 --> 00:12:24,960 Sure. 180 00:12:24,960 --> 00:12:29,320 I don't actually have a data science degree. 181 00:12:29,320 --> 00:12:33,000 I just have done some data sciencey things. 182 00:12:33,000 --> 00:12:37,680 So I'm a data engineer and BI engineer. 183 00:12:37,680 --> 00:12:45,520 And so currently I'm working on building softwares for the cannabis industry, specifically banking 184 00:12:45,520 --> 00:12:48,880 and a point of sale. 185 00:12:48,880 --> 00:12:56,960 So the intent is to be for Oklahomans by Oklahomans and to try to ensure that if Oklahomans are 186 00:12:56,960 --> 00:13:02,480 going to spend money on a point of sale system, that it should be an Oklahoma based one so 187 00:13:02,480 --> 00:13:08,120 that that revenue stays in Oklahoma and that those taxes stay in Oklahoma and helps the 188 00:13:08,120 --> 00:13:09,300 community. 189 00:13:09,300 --> 00:13:16,800 So I am here to glean as much information from all of you brilliant individuals and 190 00:13:16,800 --> 00:13:19,320 hopefully be able to contribute as well. 191 00:13:19,320 --> 00:13:22,520 Well, it's awesome to have you Sasha. 192 00:13:22,520 --> 00:13:28,760 It's an area that we would love to learn from you about what's going on with banking. 193 00:13:28,760 --> 00:13:30,680 We do talk a bit about sales. 194 00:13:30,680 --> 00:13:34,520 Paul is probably the sales expert at the moment. 195 00:13:34,520 --> 00:13:40,920 And in fact, we have a little bit of Oklahoma data to look at today, just a little bit, 196 00:13:40,920 --> 00:13:48,800 but bite size to get us to start talking about this next idea. 197 00:13:48,800 --> 00:13:58,400 So I guess just to go ahead and to dive into what I was looking at. 198 00:13:58,400 --> 00:14:01,280 Hi Keegan, this is Paul. 199 00:14:01,280 --> 00:14:02,920 I think we forgot Heather. 200 00:14:02,920 --> 00:14:03,920 Oh, Heather. 201 00:14:03,920 --> 00:14:06,840 No, no, no, no, no, no, no, don't worry about it. 202 00:14:06,840 --> 00:14:07,840 I really appreciate you saying that. 203 00:14:07,840 --> 00:14:08,840 It's all good. 204 00:14:08,840 --> 00:14:09,840 Thank you. 205 00:14:09,840 --> 00:14:10,840 Hi Heather. 206 00:14:10,840 --> 00:14:11,840 Hey Sasha. 207 00:14:11,840 --> 00:14:16,840 Well, Heather's an awesome attendee. 208 00:14:16,840 --> 00:14:35,840 It is relevant because so essentially started just looking at a map here of, okay, so like 209 00:14:35,840 --> 00:14:43,760 I started thinking, okay, so let's start to really just start to get our ear to the ground 210 00:14:43,760 --> 00:14:51,600 and really start to figure out how much in sales and cannabis is really going on. 211 00:14:51,600 --> 00:14:56,680 So we started talking about sales last week. 212 00:14:56,680 --> 00:15:00,440 So we've been primarily looking at Washington State. 213 00:15:00,440 --> 00:15:13,080 And so there are polls out there with companies estimating the size of illegal markets or 214 00:15:13,080 --> 00:15:15,920 the size of the legal markets. 215 00:15:15,920 --> 00:15:24,680 But here at the Cannabis Data Science Group, we try to get our hands on the data ourselves 216 00:15:24,680 --> 00:15:27,360 and crunch these numbers ourselves. 217 00:15:27,360 --> 00:15:35,840 So I figured, so why don't in, this could be a little bit of an undertaking, but here 218 00:15:35,840 --> 00:15:38,920 in the next couple of weeks, I think we can do this. 219 00:15:38,920 --> 00:15:47,680 So essentially, I thought that, okay, why don't we try to make the Cannabis Data Science 220 00:15:47,680 --> 00:15:54,440 Meetup Group estimation of the size of the cannabis market. 221 00:15:54,440 --> 00:16:03,160 So that way, you know, Leafly and New Frontier Data, they have their estimates. 222 00:16:03,160 --> 00:16:12,680 And so then we can add our estimate to the hat to see where it, to see how it compares. 223 00:16:12,680 --> 00:16:20,480 And so, and what will be cool is we'll have all of our tools and sources is open source. 224 00:16:20,480 --> 00:16:25,760 So that way, you know, someone can follow in our footsteps. 225 00:16:25,760 --> 00:16:33,240 So long story short, we're going to have to do a little bit of homework here because, 226 00:16:33,240 --> 00:16:43,800 so for example, you know, they have New York as fully legal, however, as we know, and even, 227 00:16:43,800 --> 00:16:48,640 so for example, they've got Virginia as fully legal. 228 00:16:48,640 --> 00:17:03,000 And so I started looking in the, you know, the regulations here for Virginia, and I believe 229 00:17:03,000 --> 00:17:09,480 things really don't, you know, kick off until 2023. 230 00:17:09,480 --> 00:17:10,480 So right. 231 00:17:10,480 --> 00:17:11,480 Exactly. 232 00:17:11,480 --> 00:17:12,480 So, oh yeah. 233 00:17:12,480 --> 00:17:16,720 And so this is where Heather may be interested. 234 00:17:16,720 --> 00:17:21,440 So Heather is essentially about this dot. 235 00:17:21,440 --> 00:17:26,760 In that was the other thing is I started to think about the interesting location of Maryland. 236 00:17:26,760 --> 00:17:30,520 And so, you know, Maryland's surrounding DC. 237 00:17:30,520 --> 00:17:37,880 And then, like you said, you've got Virginia, which is not technically legal, but we've 238 00:17:37,880 --> 00:17:42,000 got some time to wait. 239 00:17:42,000 --> 00:17:49,000 So long story short, there's a couple factors to look at here. 240 00:17:49,000 --> 00:17:54,000 And so without further ado, I'll just start showing you some of the data that I've started 241 00:17:54,000 --> 00:17:55,640 collecting. 242 00:17:55,640 --> 00:18:06,320 So basically, we've been looking at a lot of the supply side. 243 00:18:06,320 --> 00:18:15,400 And the reason being is, you know, the supply side data is quite clean. 244 00:18:15,400 --> 00:18:19,360 So they've got the seed to sale tracking systems. 245 00:18:19,360 --> 00:18:28,280 So they relatively have a good measure of sales that are happening. 246 00:18:28,280 --> 00:18:34,960 And so, you know, and so that is essentially where supply and demand meet. 247 00:18:34,960 --> 00:18:39,320 So that's the markets cleared. 248 00:18:39,320 --> 00:18:44,240 That's equilibrium at our total sales right there. 249 00:18:44,240 --> 00:18:49,960 However, you know, we've only been looking at the supply side of the picture. 250 00:18:49,960 --> 00:18:57,720 And so, you know, I'm sure everybody can go through your data sheet. 251 00:18:57,720 --> 00:19:00,720 I put it on a bigger screen here because I got my problems. 252 00:19:00,720 --> 00:19:02,680 I need a big screen for spreadsheets. 253 00:19:02,680 --> 00:19:07,720 But you have to describe your data. 254 00:19:07,720 --> 00:19:10,640 That is to say, you know, you've got it on the screen there, but you need to go through 255 00:19:10,640 --> 00:19:13,920 the columns, at least for me, to explain what are we looking at. 256 00:19:13,920 --> 00:19:14,920 Yes. 257 00:19:14,920 --> 00:19:16,920 So let's look at it. 258 00:19:16,920 --> 00:19:22,080 Hold on one second here. 259 00:19:22,080 --> 00:19:23,080 Sure. 260 00:19:23,080 --> 00:19:27,720 I'll show you some of the visualizations here. 261 00:19:27,720 --> 00:19:42,160 So for example, started to just look at some of the data here in Maryland. 262 00:19:42,160 --> 00:19:44,320 And so now we're starting to. 263 00:19:44,320 --> 00:19:59,320 Okay, so for example, you know, we've got the Maryland, you know, we've got Maryland 264 00:19:59,320 --> 00:20:01,440 sales. 265 00:20:01,440 --> 00:20:08,120 So you see, you know, in Maryland, we've got a nice, you know, they've got a steady increase 266 00:20:08,120 --> 00:20:10,160 in sales. 267 00:20:10,160 --> 00:20:15,520 You know, at the same time, you know, there's more and more patients. 268 00:20:15,520 --> 00:20:20,720 So if you were, you know, only looking at the sales, it would be just, you know, kind 269 00:20:20,720 --> 00:20:22,560 of hard to explain this. 270 00:20:22,560 --> 00:20:28,440 And so then, okay, well, that can be maybe explained by the number of patients. 271 00:20:28,440 --> 00:20:36,600 And so, well, you know, maybe the number of patients that may essentially track along 272 00:20:36,600 --> 00:20:39,320 with population. 273 00:20:39,320 --> 00:20:47,760 So or maybe it may out the number of people signing up may be outpacing any change in 274 00:20:47,760 --> 00:20:49,440 population. 275 00:20:49,440 --> 00:20:56,640 So long story short, some of the variables that I thought would be worth looking at would 276 00:20:56,640 --> 00:21:00,900 be essentially, we'll start making panel data. 277 00:21:00,900 --> 00:21:11,920 So we'll keep track of the state, and we'll keep track of the date and what's nifty is 278 00:21:11,920 --> 00:21:26,080 the, you know, the census has recently come out with the 2020 estimates. 279 00:21:26,080 --> 00:21:36,280 So we can now get relatively recent, you know, population for all the various states. 280 00:21:36,280 --> 00:21:44,360 So this is where you see things start, there start to be some estimates. 281 00:21:44,360 --> 00:21:54,940 So for example, you know, we just have the fixed point 2020 population for Maryland. 282 00:21:54,940 --> 00:22:03,400 And so now if we're starting to incorporate population into our analysis, do we, you know, 283 00:22:03,400 --> 00:22:06,200 start assuming that population's growing? 284 00:22:06,200 --> 00:22:10,400 Or do we just leave population as a constant? 285 00:22:10,400 --> 00:22:15,360 And you know, it's hard to get constants in this case. 286 00:22:15,360 --> 00:22:20,540 Like when I looked at this data first, I thought that I could use the number of caregivers. 287 00:22:20,540 --> 00:22:26,680 So in this case, these are people that don't necessarily have to have their Maryland card, 288 00:22:26,680 --> 00:22:33,020 but they would arrive at the dispensary to pick up the product for the intended patient. 289 00:22:33,020 --> 00:22:40,460 So I thought that that was a measure too, but it does go up, but not nearly to explain 290 00:22:40,460 --> 00:22:44,480 the monthly increase. 291 00:22:44,480 --> 00:22:50,300 So anyway, yeah, it's like 2% of the population in Maryland is roughly hold to card. 292 00:22:50,300 --> 00:22:54,920 So we don't know about the other types of sales, but for legal sales in this case, yeah, 293 00:22:54,920 --> 00:22:57,520 2% of the population. 294 00:22:57,520 --> 00:22:58,520 Exactly. 295 00:22:58,520 --> 00:23:02,240 And Stephen, were you going to make a comment here? 296 00:23:02,240 --> 00:23:05,920 Well, yeah, I mean, there's just a few things. 297 00:23:05,920 --> 00:23:11,520 One would be, I would get rid of the cents on your column, column D and column. 298 00:23:11,520 --> 00:23:12,800 Which one? 299 00:23:12,800 --> 00:23:18,980 For your revenue and total patients, just for clarity and for easy scanning that you 300 00:23:18,980 --> 00:23:21,760 might just get rid of the dot zero zero. 301 00:23:21,760 --> 00:23:22,760 Yes. 302 00:23:22,760 --> 00:23:26,640 It's just, I mean, I realize it's a small thing, but trying to understand it and on 303 00:23:26,640 --> 00:23:28,120 a go forward basis, that'd be nice. 304 00:23:28,120 --> 00:23:31,160 The same thing for total revenue. 305 00:23:31,160 --> 00:23:36,560 And then also revenue per patient, like just round it to the nearest dollar. 306 00:23:36,560 --> 00:23:40,000 That will make things easier when you go to start plotting it and printing it and have 307 00:23:40,000 --> 00:23:41,320 labeled points and stuff. 308 00:23:41,320 --> 00:23:46,640 Because it's not really significant to have, you're showing six significant digits and 309 00:23:46,640 --> 00:23:50,560 obviously it's not significant beyond the dollar. 310 00:23:50,560 --> 00:23:51,560 Oh, yes. 311 00:23:51,560 --> 00:23:54,200 That'd be the first thing. 312 00:23:54,200 --> 00:23:58,080 And then as far as making it more readily understood. 313 00:23:58,080 --> 00:24:03,760 The next thing is that, of course, the population number is not going to change once the census 314 00:24:03,760 --> 00:24:04,760 is set. 315 00:24:04,760 --> 00:24:08,040 I don't know that there's a way to show incremental increases to the census data. 316 00:24:08,040 --> 00:24:13,080 So that's going to be a static thing for the next 10 years or something. 317 00:24:13,080 --> 00:24:18,760 If you're looking for additional things, you might want to break that population down, 318 00:24:18,760 --> 00:24:20,000 but by age tranche. 319 00:24:20,000 --> 00:24:28,400 Because I think that you'll find that marijuana use, legal and illegal, you know, sanctioned 320 00:24:28,400 --> 00:24:34,040 or unsanctioned, whatever, is not consistent across ages and across gender. 321 00:24:34,040 --> 00:24:36,040 So I would think that that'd be useful. 322 00:24:36,040 --> 00:24:41,080 Further, if you do it for one state, like we do it for Maryland, you might find that 323 00:24:41,080 --> 00:24:44,520 that model will carry over to other states. 324 00:24:44,520 --> 00:24:49,560 So you mentioned that you might like to do some predictions versus other states. 325 00:24:49,560 --> 00:24:54,720 I suggest diving down on one state, maybe Maryland, and understand that really well 326 00:24:54,720 --> 00:24:59,440 and then say, hey, people in Maryland might be similar to other states. 327 00:24:59,440 --> 00:25:06,180 So there's a really nice database of census information that's free from the US government. 328 00:25:06,180 --> 00:25:08,720 It's a census.gov or something. 329 00:25:08,720 --> 00:25:12,800 So you can pull down a whole bunch of very specific information. 330 00:25:12,800 --> 00:25:18,840 For example, you might want to model this if you've got data, dispensary data by zip 331 00:25:18,840 --> 00:25:21,800 code in Maryland. 332 00:25:21,800 --> 00:25:25,800 You can then go back and say, hey, in this zip code, here's the distribution, the population 333 00:25:25,800 --> 00:25:31,160 distribution by age and gender, as well as income distributions. 334 00:25:31,160 --> 00:25:35,200 So you can do a lot of that stuff to get a better model. 335 00:25:35,200 --> 00:25:38,600 So in other words, I guess what I'm really getting at is you could choose to dive down 336 00:25:38,600 --> 00:25:44,600 onto Maryland, come up with a characterization of Maryland based on characteristics that 337 00:25:44,600 --> 00:25:49,400 you get from the census data, and then pretty quickly just flip it around and say, hey, 338 00:25:49,400 --> 00:25:55,040 if the rest of the states were like Maryland, here's what we would expect to see. 339 00:25:55,040 --> 00:25:59,640 And that may be really different than I think your other people are more likely to say, 340 00:25:59,640 --> 00:26:01,840 hey, what do we think's happening? 341 00:26:01,840 --> 00:26:07,400 People try to derive their estimate of the US market a different way. 342 00:26:07,400 --> 00:26:13,920 But if you did it this way, it would be a useful, I think, I mean, I'm imagining not 343 00:26:13,920 --> 00:26:17,120 knowing anything about your industry really. 344 00:26:17,120 --> 00:26:23,160 But my guess is that they're not coming at it from a deep dive on one. 345 00:26:23,160 --> 00:26:27,520 This is like you could use the Maryland data as a sample of the US. 346 00:26:27,520 --> 00:26:31,000 It's a biased sample, but it's not bad. 347 00:26:31,000 --> 00:26:35,320 And you have it, that's some information you can get. 348 00:26:35,320 --> 00:26:37,000 So again, we look back at the population. 349 00:26:37,000 --> 00:26:41,120 I recommend splitting that by age tranche. 350 00:26:41,120 --> 00:26:47,440 And you can do that easily as I said, but it's like census.org or something. 351 00:26:47,440 --> 00:26:49,000 They have data sets you can download. 352 00:26:49,000 --> 00:26:53,120 And I started first by just looking at Maryland and trying to understand Maryland well. 353 00:26:53,120 --> 00:26:54,120 Exactly. 354 00:26:54,120 --> 00:26:58,880 And then from there, and so the first thing I would do is I'd download the population 355 00:26:58,880 --> 00:27:01,120 data from Maryland and I do that. 356 00:27:01,120 --> 00:27:07,440 There's typically I'll break it by zip code or there's something else called a FIPS, F-I-P-S 357 00:27:07,440 --> 00:27:08,440 code. 358 00:27:08,440 --> 00:27:10,360 So just two different ways. 359 00:27:10,360 --> 00:27:14,520 I think the census maybe has both. 360 00:27:14,520 --> 00:27:19,600 But the thing that's nice about that is that you can get a lot of correlations. 361 00:27:19,600 --> 00:27:20,600 Exactly. 362 00:27:20,600 --> 00:27:27,200 And so you've hit on basically what's key here. 363 00:27:27,200 --> 00:27:33,200 So we're basically, so if you can see my screen, basically I just have these quick facts from 364 00:27:33,200 --> 00:27:34,600 Maryland. 365 00:27:34,600 --> 00:27:39,560 And so with some states, you're right, we can get more granular. 366 00:27:39,560 --> 00:27:49,880 So Washington state, Colorado, you may be able to get a number of licenses by county 367 00:27:49,880 --> 00:27:53,000 in Oklahoma, perhaps. 368 00:27:53,000 --> 00:27:57,720 I believe you can because you know the county and the licensees, so you can at least get 369 00:27:57,720 --> 00:27:59,760 licensees by county. 370 00:27:59,760 --> 00:28:07,840 So with certain states, it can be a bit more granular. 371 00:28:07,840 --> 00:28:22,600 We're almost just trying to do like a national demand for cannabis. 372 00:28:22,600 --> 00:28:28,000 And so long story short, I like your idea. 373 00:28:28,000 --> 00:28:34,320 And it's in this similar vein where we're basically we're trying to aggregate these 374 00:28:34,320 --> 00:28:46,720 explanatory variables and see if they can't help us predict demand in other states. 375 00:28:46,720 --> 00:28:55,160 So basically the idea was, okay, so we can get the revenue in Maryland. 376 00:28:55,160 --> 00:29:05,160 And so this is about 2% of the population or the patients are 2% of the population. 377 00:29:05,160 --> 00:29:14,800 And they're spending on average almost $400 a month. 378 00:29:14,800 --> 00:29:30,320 And so looking at actually a smaller state, Oklahoma, so Oklahoma's population is a little 379 00:29:30,320 --> 00:29:33,880 more than half of Maryland. 380 00:29:33,880 --> 00:29:54,920 And here you have so recently there was a bulletin that showed, okay, in 2020, there 381 00:29:54,920 --> 00:30:04,760 were about 330,000 patients in Oklahoma and then this year there was 375,000. 382 00:30:04,760 --> 00:30:08,240 So we're just chalk those in. 383 00:30:08,240 --> 00:30:14,800 You know, those are just yet more data points for us. 384 00:30:14,800 --> 00:30:21,360 And the striking thing here is that Oklahoma's got 8% of the population or 9% of the population 385 00:30:21,360 --> 00:30:24,080 is using it, whereas Maryland is only 2%. 386 00:30:24,080 --> 00:30:28,960 So the elephant in the room is why is one four times bigger than the other? 387 00:30:28,960 --> 00:30:29,960 Exactly. 388 00:30:29,960 --> 00:30:35,760 And so this is, I think, where some interesting analytics may be worth bringing in. 389 00:30:35,760 --> 00:30:44,680 So one is, could we just assume that everyone in the United States basically has around 390 00:30:44,680 --> 00:30:47,640 the same baseline consumption? 391 00:30:47,640 --> 00:30:57,800 And then the only variation is just being just the black market essentially? 392 00:30:57,800 --> 00:31:06,320 Or are there actually systemic differences between consumption in Oklahoma and in Maryland? 393 00:31:06,320 --> 00:31:08,600 So I'll pause you there for a second. 394 00:31:08,600 --> 00:31:13,000 And you see stuff right now with COVID and the misreporting that happened with COVID, 395 00:31:13,000 --> 00:31:15,240 for example, in Mexico. 396 00:31:15,240 --> 00:31:16,880 And it was underreported. 397 00:31:16,880 --> 00:31:21,600 But what they did, what some people did is in Mexico City, they really get death reports 398 00:31:21,600 --> 00:31:22,600 by district. 399 00:31:22,600 --> 00:31:25,840 And by giving the death reports, they could make an estimate then of what was happening 400 00:31:25,840 --> 00:31:30,720 in Mexico City for COVID, way ahead of any published data, which showed it was much higher 401 00:31:30,720 --> 00:31:31,720 than expected. 402 00:31:31,720 --> 00:31:36,560 In a similar vein, you might be able to say, hey, I get 10 friends or 20 friends or whatever 403 00:31:36,560 --> 00:31:40,160 and buy a sample, but just see what the range of use is. 404 00:31:40,160 --> 00:31:43,920 Like how many, I mean, whatever the unit of measure is, is it a, I mean, joint doesn't 405 00:31:43,920 --> 00:31:47,360 sound like it's a standard measure, but maybe you've sold by weight or something. 406 00:31:47,360 --> 00:31:52,240 Here's the weight that I'm using per month and see what the difference is. 407 00:31:52,240 --> 00:31:55,520 You might find that the distribution is rather small. 408 00:31:55,520 --> 00:31:57,080 The difference might be small. 409 00:31:57,080 --> 00:32:02,640 You might find that people who use it use some minimum and the maximum, there's a limit 410 00:32:02,640 --> 00:32:05,600 maybe to how much you could possibly smoke. 411 00:32:05,600 --> 00:32:08,240 So that would give you an idea then of that spread. 412 00:32:08,240 --> 00:32:13,240 Just knowing what that is, even with a group of 20 people will tell you something as opposed 413 00:32:13,240 --> 00:32:17,280 to, you know, it's a simple thing for you, relatively simple thing for you to do to get 414 00:32:17,280 --> 00:32:20,800 just some basic understanding of what the distribution is. 415 00:32:20,800 --> 00:32:25,480 Cause you may find that, Hey, there's some people who, I mean, it could, it could happen, 416 00:32:25,480 --> 00:32:30,560 for example, that 80% of the people are light users and 20% are just super heavy. 417 00:32:30,560 --> 00:32:33,000 That would be good to know. 418 00:32:33,000 --> 00:32:38,160 I can tell you also being an Oklahoma and myself that getting a medical marijuana license, 419 00:32:38,160 --> 00:32:42,560 at least in Oklahoma is, is much easier than some other places. 420 00:32:42,560 --> 00:32:48,840 So that could also account for the higher number of individuals that have a medical 421 00:32:48,840 --> 00:32:49,840 marijuana license. 422 00:32:49,840 --> 00:32:52,200 I think Sasha's kind of hit it very hard. 423 00:32:52,200 --> 00:32:58,680 You might add another column, which says just subjectively, do we think it's easy, hard, 424 00:32:58,680 --> 00:33:01,360 you know, impossible, whatever to get a license? 425 00:33:01,360 --> 00:33:04,440 Cause that's, I think, I think Sasha said it directly. 426 00:33:04,440 --> 00:33:11,120 I mean, I tangentially, what I think I hear, you know, in my environment is that, is that 427 00:33:11,120 --> 00:33:15,280 in California, like, Hey, if you're already smoking, how could you make it legal? 428 00:33:15,280 --> 00:33:17,760 Oh, let's go get a marijuana license. 429 00:33:17,760 --> 00:33:20,800 That's what I kind of think happens as opposed to, Hey, I'm in pain. 430 00:33:20,800 --> 00:33:21,800 I tried, you know, painkillers. 431 00:33:21,800 --> 00:33:23,800 Now I want to try, you know, I tried CBD. 432 00:33:23,800 --> 00:33:26,560 Now I want to go to this, right? 433 00:33:26,560 --> 00:33:28,960 So again, I don't know much about the patient process. 434 00:33:28,960 --> 00:33:36,120 So I don't know much about what you do to get, to be a licensed marijuana receiver, 435 00:33:36,120 --> 00:33:39,200 but I think Sasha's, it's gotta be something like that. 436 00:33:39,200 --> 00:33:41,120 There's a huge difference. 437 00:33:41,120 --> 00:33:43,480 And so that would be another predictive variable. 438 00:33:43,480 --> 00:33:44,480 Okay. 439 00:33:44,480 --> 00:33:48,720 So if you just simply went through the 50 states and say, would ask 10 people to say 440 00:33:48,720 --> 00:33:55,800 how, you know, there's a lot of value to getting just, uh, getting a consensus estimate and 441 00:33:55,800 --> 00:34:01,040 stuff, how difficult it is to get a license, get a permit by state. 442 00:34:01,040 --> 00:34:03,840 And so you might get, you might go saying 10 people, where is it easy? 443 00:34:03,840 --> 00:34:04,840 Where is it hard? 444 00:34:04,840 --> 00:34:06,120 And I would make it like high, medium and low. 445 00:34:06,120 --> 00:34:09,800 It doesn't have to be very difficult, not difficult. 446 00:34:09,800 --> 00:34:12,240 Hey, anybody who wants one gets one. 447 00:34:12,240 --> 00:34:17,160 Just some, some breakdown like that would go a long way to explaining this difference. 448 00:34:17,160 --> 00:34:21,680 So your population doesn't change very much. 449 00:34:21,680 --> 00:34:25,160 It might change somewhat by state, but the biggest contributing factor here is likely 450 00:34:25,160 --> 00:34:28,200 to be how easy or how hard it is. 451 00:34:28,200 --> 00:34:31,680 And even though you may look at that and say, well, there's no numbers for that. 452 00:34:31,680 --> 00:34:37,120 You can make something called factors, just, just being able to say it's high, medium or 453 00:34:37,120 --> 00:34:41,680 low, something like that, that would be very useful in explaining the difference between 454 00:34:41,680 --> 00:34:42,680 states. 455 00:34:42,680 --> 00:34:43,680 Exactly. 456 00:34:43,680 --> 00:34:53,280 And so, well, I think Sasha, what you mentioned, I think that's going to be helpful. 457 00:34:53,280 --> 00:34:57,360 So basically we can, you know, start making some assumptions here. 458 00:34:57,360 --> 00:35:04,000 So basically we can say, okay, it's easy to get a license in Oklahoma. 459 00:35:04,000 --> 00:35:15,320 So maybe this is close to like the, like the upper bound actual number of cannabis consumers. 460 00:35:15,320 --> 00:35:22,880 So we can say, okay, you know, maybe almost everyone who consumed cannabis in Oklahoma, 461 00:35:22,880 --> 00:35:26,080 you know, falls into this, these numbers. 462 00:35:26,080 --> 00:35:36,520 And so, you know, it could be the case that in Maryland, that, you know, that about 6% 463 00:35:36,520 --> 00:35:44,280 of cannabis users, they may be using illegal cannabis. 464 00:35:44,280 --> 00:35:47,160 You know, growing their own. 465 00:35:47,160 --> 00:36:00,360 Or not 6%, but more like 80%, but, you know, making up about 6% of the population. 466 00:36:00,360 --> 00:36:08,400 You know, the suggestion that, you know, Sasha's making about, you know, essentially trying 467 00:36:08,400 --> 00:36:16,600 to, I think Steve was saying to quantify the difficulty of obtaining a license as a measure. 468 00:36:16,600 --> 00:36:23,480 You may also look at the different data sources from the time that the application of the 469 00:36:23,480 --> 00:36:25,920 license was made until the time it was issued. 470 00:36:25,920 --> 00:36:30,320 If they keep track of that, just as a, another way of trying to figure out how difficult 471 00:36:30,320 --> 00:36:33,680 it is to, you know, as a proxy of difficulty. 472 00:36:33,680 --> 00:36:45,560 One thing, this is going to be tough to parse, but I believe they have data on, okay, how 473 00:36:45,560 --> 00:36:49,600 much in like tax revenue was brought in from license. 474 00:36:49,600 --> 00:36:55,400 So like how much licensing fees were in there. 475 00:36:55,400 --> 00:37:03,720 I'm not certain that perhaps the NN license, I'm not certain though. 476 00:37:03,720 --> 00:37:10,000 So you may be able to divide that by the actual license cost to try to get the number per 477 00:37:10,000 --> 00:37:14,560 month. 478 00:37:14,560 --> 00:37:23,880 So long story short, when you start to look at, you know, the patients and consumers, 479 00:37:23,880 --> 00:37:26,160 the data gets a little messier. 480 00:37:26,160 --> 00:37:31,840 So we're going to have to be real rigid about any assumptions we make. 481 00:37:31,840 --> 00:37:38,200 But essentially this is just going to be, you know, the beginning of the path. 482 00:37:38,200 --> 00:37:46,920 So it's basically just going to start collecting the data points that are there for patients 483 00:37:46,920 --> 00:37:53,540 and just to try to start putting together, you know, the demand side of the picture. 484 00:37:53,540 --> 00:38:03,880 And then along the way we can, you know, utilize the census and say, okay, you know, in Oklahoma 485 00:38:03,880 --> 00:38:12,800 we can, you know, use the population and, you know, we can use the like an age factor. 486 00:38:12,800 --> 00:38:25,960 So it would be interesting to then just see, okay, can just using a crude panel because 487 00:38:25,960 --> 00:38:33,620 if we add, you know, 30 states or so, or even just 20 states, we can start to get a picture 488 00:38:33,620 --> 00:38:44,040 of, okay, are any of these explanatory variables correlated even just like at a rough level? 489 00:38:44,040 --> 00:38:49,480 And then, like I said, then we can even start to just start to make conjectures. 490 00:38:49,480 --> 00:39:00,880 So we can say, okay, let's just conjecture that every state has cannabis consumers at 491 00:39:00,880 --> 00:39:03,560 the same rate as in Oklahoma. 492 00:39:03,560 --> 00:39:10,240 Then what would you expect, you know, what would you expect sales to be in those states? 493 00:39:10,240 --> 00:39:14,000 And so we could start to just start make estimates. 494 00:39:14,000 --> 00:39:20,360 And so then that there was just estimates, but they may start to reflect, okay, what's 495 00:39:20,360 --> 00:39:23,000 the size of the illegal market? 496 00:39:23,000 --> 00:39:30,200 And so we could say, okay, you know, this is the population in Maryland. 497 00:39:30,200 --> 00:39:37,640 And we could say, so, you know, I'm not certain what the populations are off the top of my 498 00:39:37,640 --> 00:39:48,680 head, but you know, we could start maybe Maryland has a comparable population to South Carolina, 499 00:39:48,680 --> 00:39:52,240 like not certain the population of South Carolina. 500 00:39:52,240 --> 00:40:00,320 But you could start to make comparisons and see, okay, you know, what is the potential 501 00:40:00,320 --> 00:40:08,800 demand if, you know, the whole country legalized and everybody had the same consumption rates 502 00:40:08,800 --> 00:40:17,000 as in Oklahoma, or everyone had the same consumption rates as in another state? 503 00:40:17,000 --> 00:40:19,320 So a couple things you could look at here. 504 00:40:19,320 --> 00:40:24,460 One is that you could say the Oklahoma versus Maryland, the differences between is like 505 00:40:24,460 --> 00:40:26,000 one's like twice the other. 506 00:40:26,000 --> 00:40:29,360 Okay, not 10 times, twice. 507 00:40:29,360 --> 00:40:30,360 Okay. 508 00:40:30,360 --> 00:40:37,720 So that suggests that on average, you know, there may be both are averages. 509 00:40:37,720 --> 00:40:43,080 It does suggest that, hey, there's probably distribution around that average, you know, 510 00:40:43,080 --> 00:40:44,080 for sure there is. 511 00:40:44,080 --> 00:40:48,440 But, you know, that would give you an estimate then of how much per user. 512 00:40:48,440 --> 00:40:54,800 But you can also see that, hey, because it was easy in Oklahoma, a lot of these presumably 513 00:40:54,800 --> 00:40:58,360 already current users said, hey, I'm going to go get that medical thing. 514 00:40:58,360 --> 00:41:03,320 So if you want to try to start to estimate, if you say, if you took the perverse point 515 00:41:03,320 --> 00:41:07,880 that people on medical marijuana, let's say none of them needed it for marijuana for medical 516 00:41:07,880 --> 00:41:10,200 purposes, they just want to smoke it. 517 00:41:10,200 --> 00:41:13,600 Then you could suggest, hey, this is really an indication of how difficult it is to get 518 00:41:13,600 --> 00:41:20,120 a permit, but you can see that a place where it's easy, you might expect, you might expect 519 00:41:20,120 --> 00:41:24,940 as a hypothesis, as you might call it, you might call it a hypothesis that where it's 520 00:41:24,940 --> 00:41:30,640 easy to get a permit, everybody who wants to smoke it for any reason will get a permit. 521 00:41:30,640 --> 00:41:36,960 And so I would suggest going and looking at the states that have the easiest rules and 522 00:41:36,960 --> 00:41:41,160 then see if you can find out what percentage of population there has it because people 523 00:41:41,160 --> 00:41:45,120 might be using the same thing nationwide, might be the same percentage nationwide. 524 00:41:45,120 --> 00:41:50,520 And just a quick search, you can get the state by state policies from someplace called the 525 00:41:50,520 --> 00:41:55,480 cannabis industry or in CIA news state policies. 526 00:41:55,480 --> 00:41:59,960 And so maybe that's a way to kind of go and say, let's pick out the states that are easy 527 00:41:59,960 --> 00:42:04,480 and then go from there. 528 00:42:04,480 --> 00:42:14,040 And putting on my economist hat, the way we can potentially quantify the easiness is just 529 00:42:14,040 --> 00:42:16,560 the price. 530 00:42:16,560 --> 00:42:24,720 It's maybe a crude measure, but just, okay, how many dollars do you have to pay to get 531 00:42:24,720 --> 00:42:27,200 a patient license? 532 00:42:27,200 --> 00:42:35,720 So then one could assume that, okay, so in Illinois, you don't need a patient license. 533 00:42:35,720 --> 00:42:38,520 So that would just be zero. 534 00:42:38,520 --> 00:42:44,560 In Oklahoma, you would use their licensing fee. 535 00:42:44,560 --> 00:42:54,880 And then, and then I'm curious, Heather, would you know what the licensing fee, if there 536 00:42:54,880 --> 00:42:58,680 is or, and if so, what it may be in Maryland? 537 00:42:58,680 --> 00:43:00,360 It depends if you have to renew or not. 538 00:43:00,360 --> 00:43:06,080 So the first time that you come in, it might be like $200 cash. 539 00:43:06,080 --> 00:43:10,880 And the next time you renew as cheap as $100 to 150. 540 00:43:10,880 --> 00:43:13,120 Okay. 541 00:43:13,120 --> 00:43:18,320 So I don't know for certain, but I want to say that in Oklahoma, I actually, I have no 542 00:43:18,320 --> 00:43:19,320 idea. 543 00:43:19,320 --> 00:43:24,680 I'll have to look this up for next week, but I don't think it's 200. 544 00:43:24,680 --> 00:43:33,200 So I want to say it's between 60 and, I want to say it's between 60 and 100, but I'll have 545 00:43:33,200 --> 00:43:35,240 to look this up to be certain. 546 00:43:35,240 --> 00:43:39,680 Yes, it's like, exactly. 547 00:43:39,680 --> 00:43:43,240 And so those are just, those are just two data points. 548 00:43:43,240 --> 00:43:53,120 However, essentially that would just be a step, you know, a fixed state by state variable. 549 00:43:53,120 --> 00:43:59,500 So basically we'll just start, we'll just basically just start adding variables here. 550 00:43:59,500 --> 00:44:05,520 And so it'll just be the, you know, the patient license cost. 551 00:44:05,520 --> 00:44:12,480 And so we'll add this variable, you know, for Maryland. 552 00:44:12,480 --> 00:44:19,600 And then they'll, we'll add whatever, we'll add Oklahoma's fee here. 553 00:44:19,600 --> 00:44:28,440 And so essentially we'll maybe even be able to use those as explanatory factors in, okay, 554 00:44:28,440 --> 00:44:30,240 how many patients are there. 555 00:44:30,240 --> 00:44:33,240 Yeah, but it's really small. 556 00:44:33,240 --> 00:44:38,640 I mean, just not knowing much about your understanding. 557 00:44:38,640 --> 00:44:44,960 You might also want to look at why I had a question first. 558 00:44:44,960 --> 00:44:51,880 Does the patient pay a different price than the illegal price for marijuana? 559 00:44:51,880 --> 00:44:56,600 Oh, I don't know, but that's a great question. 560 00:44:56,600 --> 00:44:57,600 I will ask around. 561 00:44:57,600 --> 00:45:04,280 Yeah, because if patients, if basically if quote medical use of marijuana had a lower 562 00:45:04,280 --> 00:45:07,320 price or a higher price, that would be both, that'd be important. 563 00:45:07,320 --> 00:45:11,640 If it was a higher price, it would suggest that, Hey, that's a disincentive to go get 564 00:45:11,640 --> 00:45:12,640 it. 565 00:45:12,640 --> 00:45:17,400 If it was a lower price or the same price, then, Hey, I want to, I want to join the book 566 00:45:17,400 --> 00:45:18,400 club. 567 00:45:18,400 --> 00:45:19,400 You know, it's cheaper. 568 00:45:19,400 --> 00:45:21,400 It tends to be cheaper in Maryland. 569 00:45:21,400 --> 00:45:23,680 It's like joining in Costco or something. 570 00:45:23,680 --> 00:45:24,680 Right. 571 00:45:24,680 --> 00:45:29,160 Well, we normally have a guest from Oregon and I think he could attest to this. 572 00:45:29,160 --> 00:45:35,080 So essentially I think in Oregon, they're going through measures to try to, you know, 573 00:45:35,080 --> 00:45:39,480 make sure that everybody is operating in the recreational market. 574 00:45:39,480 --> 00:45:46,160 And it's one of the funniest tales because it's basically, you know, they're not, I forget 575 00:45:46,160 --> 00:45:51,880 if it's Oregon, I want to say it is, but I feel they're, they're giving subsidies to, 576 00:45:51,880 --> 00:45:54,200 you know, the cannabis producers. 577 00:45:54,200 --> 00:46:01,720 And so I just thought it was funny where it was just sort of coming, coming full circle 578 00:46:01,720 --> 00:46:08,000 that now something that used to be illegal, you know, strongly illegal. 579 00:46:08,000 --> 00:46:13,480 And now they're, you know, the government's kind of giving subsidies to get people to 580 00:46:13,480 --> 00:46:14,480 do it legally. 581 00:46:14,480 --> 00:46:19,480 So in Oklahoma, you can, oh, sorry, go ahead. 582 00:46:19,480 --> 00:46:23,640 The other thing you probably get numbers on is CBD. 583 00:46:23,640 --> 00:46:28,680 So as I understand it, CBD does not have THC, the primary motivation for recreational people, 584 00:46:28,680 --> 00:46:30,620 I think is the THC. 585 00:46:30,620 --> 00:46:36,560 So if it's a CBD user, then I think what we'll have is that might be a good indication of 586 00:46:36,560 --> 00:46:38,880 the medical use by state. 587 00:46:38,880 --> 00:46:43,200 So I would go get CBD numbers by state if you can, because that, because I think it 588 00:46:43,200 --> 00:46:48,280 will reflect people doing it for true medical reasons as opposed to, hey, I just want to 589 00:46:48,280 --> 00:46:49,280 smoke. 590 00:46:49,280 --> 00:46:53,080 And then, and that's those numbers you probably could get. 591 00:46:53,080 --> 00:46:58,560 And that would also probably help you understand better the distribution of medical users as 592 00:46:58,560 --> 00:47:02,320 opposed to recreational users, because you can look at CBD and then compare that to the 593 00:47:02,320 --> 00:47:05,600 sense of the state that you can easily download. 594 00:47:05,600 --> 00:47:12,280 And you want to get out of Excel because you're going nuts trying to do it in Excel. 595 00:47:12,280 --> 00:47:15,000 Just to be a devil's advocate here. 596 00:47:15,000 --> 00:47:22,400 So from a chemistry standpoint, marijuana has something called cannabinoid. 597 00:47:22,400 --> 00:47:25,760 So CBD is one THC. 598 00:47:25,760 --> 00:47:32,400 There are other ones a little bit more obscure like CBN, et cetera. 599 00:47:32,400 --> 00:47:41,040 And the plant itself, medicinal, medicinally naturally occurring, not plants that are bred 600 00:47:41,040 --> 00:47:47,600 specifically to have high THC, but naturally occurring THC and a plant that has not been 601 00:47:47,600 --> 00:47:49,480 modified. 602 00:47:49,480 --> 00:47:53,460 The cannabinoids work best together when they are used together. 603 00:47:53,460 --> 00:48:02,320 So CBD itself, if you consume CBD, it's guaranteed to have less than like 0.3% or 0.03% THC. 604 00:48:02,320 --> 00:48:05,520 So that it doesn't register on drug screens. 605 00:48:05,520 --> 00:48:15,320 But I think probably some individuals consume THC with their cannabis because it gives them 606 00:48:15,320 --> 00:48:17,960 a more holistic medicinal effect. 607 00:48:17,960 --> 00:48:18,960 Yeah. 608 00:48:18,960 --> 00:48:25,040 I mean, what you say is, I mean, I don't disagree that there are some people, but I think for 609 00:48:25,040 --> 00:48:28,320 most people, they don't get much of a buzz off CBD as I understand it. 610 00:48:28,320 --> 00:48:30,320 And- I would disagree. 611 00:48:30,320 --> 00:48:31,320 I'm so sorry. 612 00:48:31,320 --> 00:48:32,320 I would disagree. 613 00:48:32,320 --> 00:48:33,320 But- Okay. 614 00:48:33,320 --> 00:48:34,320 So I really don't, I'm speculating. 615 00:48:34,320 --> 00:48:38,480 But I don't know how you would- But your position is that there are some people 616 00:48:38,480 --> 00:48:41,320 who say, I'm going to take CBD for the buzz. 617 00:48:41,320 --> 00:48:47,360 I mean, I'm looking for your input or your advice. 618 00:48:47,360 --> 00:48:53,760 You can get quote unquote high, I guess, from CBD, but it's not the same as a THC high. 619 00:48:53,760 --> 00:49:01,200 So the quote unquote high is more medicinal, like calmness, lesser anxiety, things like 620 00:49:01,200 --> 00:49:05,320 that when it comes to altered mental state. 621 00:49:05,320 --> 00:49:07,880 Would you agree, Heather? 622 00:49:07,880 --> 00:49:08,880 Thank you. 623 00:49:08,880 --> 00:49:09,880 Well said. 624 00:49:09,880 --> 00:49:14,640 So along those lines then, you might want to just go, again, ask your 20 people, hey, 625 00:49:14,640 --> 00:49:21,360 do any of you guys just take CBD without taking any THC or supplemental marijuana? 626 00:49:21,360 --> 00:49:27,440 And that would give you an idea then of the, if you will, the medicinal usage of it. 627 00:49:27,440 --> 00:49:32,680 I think in general, and again, I'm speculating here, this is a hypothesis, that the people 628 00:49:32,680 --> 00:49:37,880 who are using it medicinally might not be the same people who use it. 629 00:49:37,880 --> 00:49:41,520 Well, there's a group of people who literally do use it. 630 00:49:41,520 --> 00:49:44,920 They have migraine headaches or something, and they literally just want to get the relief 631 00:49:44,920 --> 00:49:45,920 from the migraine. 632 00:49:45,920 --> 00:49:48,600 They're not looking for the THC. 633 00:49:48,600 --> 00:49:51,880 The THC is in it, well, it's hard to remove THC. 634 00:49:51,880 --> 00:49:52,880 It's a small amount. 635 00:49:52,880 --> 00:49:54,880 They didn't get it for that effect. 636 00:49:54,880 --> 00:49:58,160 And even though it does contribute, it has a specific receptors that it's hitting. 637 00:49:58,160 --> 00:50:00,000 It's a different receptor. 638 00:50:00,000 --> 00:50:01,000 So you know. 639 00:50:01,000 --> 00:50:02,000 Yeah. 640 00:50:02,000 --> 00:50:04,160 You can never have too many data points. 641 00:50:04,160 --> 00:50:05,160 Exactly. 642 00:50:05,160 --> 00:50:08,600 And Sasha, you basically hit on the key here. 643 00:50:08,600 --> 00:50:17,040 And basically, I think what it comes down to is, basically, I'm just not satisfied with 644 00:50:17,040 --> 00:50:23,240 just kind of just seeing these large estimates like, oh, the illegal market's this big or 645 00:50:23,240 --> 00:50:30,000 oh, the legal market's going to be worth this many in sales in 2020. 646 00:50:30,000 --> 00:50:32,720 So it's or in 2021. 647 00:50:32,720 --> 00:50:40,480 So that's why we're going to start essentially collecting these data points ourselves and 648 00:50:40,480 --> 00:50:43,360 then essentially let the data speak for itself. 649 00:50:43,360 --> 00:50:44,360 Absolutely. 650 00:50:44,360 --> 00:50:48,320 Yeah, you never know, you might plot some obscure data point that everybody thinks is 651 00:50:48,320 --> 00:50:51,360 useless and it ends up being the outlier that explains everything. 652 00:50:51,360 --> 00:50:52,920 You never know. 653 00:50:52,920 --> 00:50:53,920 Exactly. 654 00:50:53,920 --> 00:50:56,920 So thanks for bearing with us. 655 00:50:56,920 --> 00:51:04,080 So essentially, this is what we're going to be embarking on in the next week or two is 656 00:51:04,080 --> 00:51:10,040 essentially so on a state by state basis, on a month by month basis. 657 00:51:10,040 --> 00:51:15,440 So that way we can get estimates for 2021 and we can look at the past. 658 00:51:15,440 --> 00:51:25,640 We can see, OK, what are the actual sales that are occurring in the states? 659 00:51:25,640 --> 00:51:30,000 What was their population? 660 00:51:30,000 --> 00:51:35,240 If they have a medical program, how many patients are there? 661 00:51:35,240 --> 00:51:38,540 And then what proportion of the state is that? 662 00:51:38,540 --> 00:51:45,520 And then we can start filling in factors like, OK, what's the licensing cost in that state? 663 00:51:45,520 --> 00:51:49,800 What is the age breakdown in that state? 664 00:51:49,800 --> 00:51:57,000 And so then these will be explanatory variables and depending on the size of our data set 665 00:51:57,000 --> 00:52:03,720 and the amount of variation, you know, we may not really be able to utilize them to 666 00:52:03,720 --> 00:52:10,720 their full extent, or they may not have significant relationships, but it's always worth looking 667 00:52:10,720 --> 00:52:13,920 and we can begin to make plots. 668 00:52:13,920 --> 00:52:22,960 And perhaps there may be things that are striking visually that even if they don't have statistical 669 00:52:22,960 --> 00:52:25,380 significance. 670 00:52:25,380 --> 00:52:32,120 So to the long story short, that's the road we're embarking on. 671 00:52:32,120 --> 00:52:33,920 And I think it'll be fruitful. 672 00:52:33,920 --> 00:52:40,000 And so long story short, we'll put together the cannabis data science estimate of the 673 00:52:40,000 --> 00:52:42,400 size of the cannabis market. 674 00:52:42,400 --> 00:52:49,040 And then we'll start making some, we'll basically make scenarios with various assumptions. 675 00:52:49,040 --> 00:52:54,240 So we're saying, OK, we may even have a calculator like this. 676 00:52:54,240 --> 00:53:02,960 And let's assume that every state consumed similar to Oklahoma, you know, with about 677 00:53:02,960 --> 00:53:06,760 between 8 to 10 percent of the population consuming. 678 00:53:06,760 --> 00:53:13,480 Well then we could estimate the potential size of the entire US market. 679 00:53:13,480 --> 00:53:23,300 Or we may just end up taking the average number of patients once we get even more data points. 680 00:53:23,300 --> 00:53:31,680 So once we have Oklahoma and Maryland and Colorado and Washington and Illinois and where 681 00:53:31,680 --> 00:53:39,600 have you, we can take the average and maybe the average is maybe looking maybe 5, 6 percent 682 00:53:39,600 --> 00:53:41,400 of the population. 683 00:53:41,400 --> 00:53:50,000 So we can play it out and we can kind of we can make our own estimate of, OK, what's the 684 00:53:50,000 --> 00:53:57,440 size of the current market based on the cannabis data science group's estimates. 685 00:53:57,440 --> 00:54:02,640 And then we may even be able to estimate, OK, what is the potentially illegal market 686 00:54:02,640 --> 00:54:12,560 that's occurring that you may see become a legal market gradually. 687 00:54:12,560 --> 00:54:19,160 Because I'll leave you with this, because basically my joke is, OK, as we see this map 688 00:54:19,160 --> 00:54:28,520 fill in, you know, once all 50 states are permitting cannabis, then at that point, then 689 00:54:28,520 --> 00:54:35,040 maybe the federal government may just have to legalize things by default. 690 00:54:35,040 --> 00:54:37,520 So I'm not sure how that works at that point. 691 00:54:37,520 --> 00:54:43,960 So long story short, that's the direction I see things going. 692 00:54:43,960 --> 00:54:51,880 Does anyone else have any comments or thoughts as we round near the end of the hour here? 693 00:54:51,880 --> 00:54:58,280 Well, I just suggest this looks like a market study. 694 00:54:58,280 --> 00:55:06,200 So if you wanted to find other things to leverage, you know, so there's a lot of literature and 695 00:55:06,200 --> 00:55:08,000 so forth about how to do a market study. 696 00:55:08,000 --> 00:55:11,840 That's essentially what we're doing here. 697 00:55:11,840 --> 00:55:19,560 And then I think that there's a whole lot about data science that would start to come 698 00:55:19,560 --> 00:55:23,600 in as to what variables would be predictive and useful. 699 00:55:23,600 --> 00:55:26,440 So you want to see which variations, which things vary a lot. 700 00:55:26,440 --> 00:55:30,040 The ones that vary a lot are often very helpful for explanatory. 701 00:55:30,040 --> 00:55:33,960 And it was things that don't vary very much, whether or not to be very useful data points 702 00:55:33,960 --> 00:55:36,840 because they help explaining things that are varying. 703 00:55:36,840 --> 00:55:43,560 So just a couple things to realize that there's a whole body of knowledge that you can tap 704 00:55:43,560 --> 00:55:50,960 into outside of the narrow marijuana industry to better understand how to approach it. 705 00:55:50,960 --> 00:55:51,960 Exactly. 706 00:55:51,960 --> 00:55:55,680 And so essentially we are doing a market study. 707 00:55:55,680 --> 00:56:04,120 And so because, well, one could imagine if there was federal cannabis laws, then there 708 00:56:04,120 --> 00:56:07,320 could be interstate commerce of cannabis. 709 00:56:07,320 --> 00:56:14,280 And then you could see, then it would be relevant to companies, you know, the multi-state operators 710 00:56:14,280 --> 00:56:19,720 at the moment, but other cannabis companies now, because even if, you know, there was 711 00:56:19,720 --> 00:56:26,440 cross-state, then, you know, you could have your craft growers in Oregon or maybe you 712 00:56:26,440 --> 00:56:33,880 have craft growers in Oklahoma just shipping their products to people all over the country. 713 00:56:33,880 --> 00:56:39,480 They could be shipping their products to New York or Maryland or where have you. 714 00:56:39,480 --> 00:56:44,480 So I'm just laughing a little bit because there's this other thing that's pretty similar. 715 00:56:44,480 --> 00:56:45,480 Cigarettes. 716 00:56:45,480 --> 00:56:51,320 Turns out that the Indian nations are sovereign nations within the United States and they 717 00:56:51,320 --> 00:56:58,040 don't pay federal taxes on their, you know, on their marijuana or on their cigarettes. 718 00:56:58,040 --> 00:57:03,400 It could be, and they, and by the way, these Indian nations ship cigarettes between their 719 00:57:03,400 --> 00:57:05,920 nations already today. 720 00:57:05,920 --> 00:57:10,880 And so you could have a situation where people say, hey, go to your nearest Indian reservation 721 00:57:10,880 --> 00:57:14,680 and you get your marijuana there. 722 00:57:14,680 --> 00:57:18,920 And get by, I haven't, I don't have any insight into that particularly, but you've got, for 723 00:57:18,920 --> 00:57:23,040 example, in Washington state, I know there are big reservations up there and people go 724 00:57:23,040 --> 00:57:24,520 there already to buy gasoline. 725 00:57:24,520 --> 00:57:32,400 So if you want to get something nationwide, well, that might be a way that you can legally 726 00:57:32,400 --> 00:57:34,280 go to the Indian reservation and buy something. 727 00:57:34,280 --> 00:57:37,040 It's not legal to take it off the reservation perhaps. 728 00:57:37,040 --> 00:57:42,840 I can tell you that in Oklahoma, there are several tribes. 729 00:57:42,840 --> 00:57:47,040 So anytime you do anything on a reservation, you have to get approval from, I guess you 730 00:57:47,040 --> 00:57:48,600 would call the tribal board. 731 00:57:48,600 --> 00:57:54,480 So there's a company, a chocolate company called Bedre and they, I think it's the Choctaw 732 00:57:54,480 --> 00:57:55,480 nation. 733 00:57:55,480 --> 00:58:02,320 I can't remember exactly where, but anyways, anytime that company makes any business decision 734 00:58:02,320 --> 00:58:04,640 they have to run it by the tribal board. 735 00:58:04,640 --> 00:58:12,280 Well, this company was going to partner with the cannabis industry to make edibles, chocolate, 736 00:58:12,280 --> 00:58:14,160 et cetera. 737 00:58:14,160 --> 00:58:20,440 But the tribal board would not approve it because they did not want any kind of marijuana 738 00:58:20,440 --> 00:58:23,160 or marijuana derivatives on tribal land. 739 00:58:23,160 --> 00:58:27,440 Well, I can see why they already have an alcohol problem. 740 00:58:27,440 --> 00:58:35,200 But when it does occur, there could definitely be a cash cow for them, no doubt. 741 00:58:35,200 --> 00:58:36,200 Exactly. 742 00:58:36,200 --> 00:58:38,000 So lots of opportunities. 743 00:58:38,000 --> 00:58:43,160 And as you pointed out, there's, you know, things are still getting hashed out. 744 00:58:43,160 --> 00:58:46,000 You know, everybody's deciding, okay. 745 00:58:46,000 --> 00:58:48,240 And even at a local level. 746 00:58:48,240 --> 00:58:56,080 So that's the story in Michigan is the local jurisdictions, you know, different in Illinois. 747 00:58:56,080 --> 00:59:05,880 And so there'll be, so basically people are deciding, okay, who are going to be the participants 748 00:59:05,880 --> 00:59:10,400 and you know, some people may opt out and that's perfectly okay. 749 00:59:10,400 --> 00:59:17,480 And so it'll just be, we're just doing our part to just start collecting data just so 750 00:59:17,480 --> 00:59:22,680 people can have a lay of the land as, you know, as the rules are hashed out. 751 00:59:22,680 --> 00:59:25,520 So that way, you know, people aren't flying blind. 752 00:59:25,520 --> 00:59:31,360 So that way we can know, okay, you know, this is the consumption rate in Oklahoma. 753 00:59:31,360 --> 00:59:38,440 It's similar to state X, Y, and Z. And then you can look at the characteristics in those 754 00:59:38,440 --> 00:59:39,880 states. 755 00:59:39,880 --> 00:59:49,040 So we're just doing our part to help crunch the numbers since data analysis is in high 756 00:59:49,040 --> 00:59:52,240 demand and low supply. 757 00:59:52,240 --> 00:59:57,840 I really appreciate this group and everyone's insights and background and bringing forth 758 00:59:57,840 --> 01:00:00,280 different things that I never even thought about. 759 01:00:00,280 --> 01:00:05,080 So I just want to thank everyone for taking out time to be here today. 760 01:00:05,080 --> 01:00:06,080 Super helpful for me. 761 01:00:06,080 --> 01:00:07,080 Definitely, definitely. 762 01:00:07,080 --> 01:00:11,040 Thank you for coming, Sasha and Stephen and Heather, as always. 763 01:00:11,040 --> 01:00:15,480 So next week we'll look at some more numbers. 764 01:00:15,480 --> 01:00:18,600 This week we were just kind of talking a lot more. 765 01:00:18,600 --> 01:00:20,760 So yeah, so thank you. 766 01:00:20,760 --> 01:00:32,560 And yes, keep your nose, we'll keep you to the ground and let me know if I was talking 767 01:00:32,560 --> 01:00:34,840 about nose, we were talking about terpenes earlier. 768 01:00:34,840 --> 01:00:42,840 So yeah, let me know if anything sparks your interest or comes across your mind and we'll 769 01:00:42,840 --> 01:00:44,400 pick up again next week. 770 01:00:44,400 --> 01:00:45,400 Awesome. 771 01:00:45,400 --> 01:00:46,400 Thanks everyone. 772 01:00:46,400 --> 01:00:47,400 Awesome. 773 01:00:47,400 --> 01:00:48,400 Enjoy your week. 774 01:00:48,400 --> 01:00:49,400 Bye now. 775 01:00:49,400 --> 01:00:51,400 Bye.