So I'm a relatively recent convert to the Wayland way of doing things as you guys know if you've watched my channel for any Monatime, you know that for the longest time I was very very anti Wayland and it's only been recently within the last three or four weeks that I've actually switched full time to Wayland and While I have many thoughts on that transition I've already made one video and I have another one that I'm kind of contemplating in my brain hole. I have Overall been fairly happy with Wayland I have had some issues with it, but I think for the most part I'm have at least I've talked about how I'm ready to say Wayland is ready for me as well as everybody else So what I want to do today is talk about one of the key features or one of the key things you hear most when you hear people talk About Wayland specifically when you hear people talk about Wayland Compositors or Wayland window managers, which is what I like to call them So when you hear people talk about these things usually you hear them talked about in conjunction with something called the portal Or an XDG portal and if you don't know what the lingo means you're probably quite confused like what is a portal? Why do I need one? So that's the question we're gonna try to answer today is what is a portal? Why do you need one? Why is it good? What is the benefits of using this system and Why we're where we are as a community in terms of Wayland and portals and all that stuff So we have a broad range of topics to choose from and that's what we're gonna talk about today So let's go ahead and jump in but before we do if you leave a thumbs up on this video I'd really appreciate it would really help the channel. So so let's go ahead and talk about the main question What is an XDG portal and why do you need one? Well, the answer to that question really lies in the way that Wayland has been built Wayland itself and the compositors that are built on top of Wayland are all built with the idea that security is one of the primary concerns of The display stack so this is an in contrast to X org Which is this gigantic behemoth that controls basically everything outside of system D and it if you displays on the screen X org is in control of it Whereas with Wayland as I understand it. It's much more containerized now When we talk about containers things get awful confusing because there's many different types of containers and even in the realm of Containers oftentimes the word container doesn't mean what you think it means so Explaining what containers mean is kind of hard and because it can mean different things and also Just because something is containerized or sandboxed if you will doesn't necessarily mean that it has no access outside of that sandbox Which is to me very weird because the idea of something being sandboxed or containerized kind of implies the You know security of it being you know contained or Sandboxed if it's not actually sandbox then it shouldn't be you shouldn't use the word words actually have meaning But that's beside the point But the point of Wayland is just to kind of get back on the point is that Everything that runs and it is displayed on the system through the Wayland protocols and all the stuff that goes along with it Are sandboxed together they cannot talk at least in general to each other at all That's the whole point of the way they have sandboxed every application in terms of Display and user interaction so for example in your browser when you want to upload a file The first thing you do on xorg or the first thing you see on an xorg is a file picker That's the thing that you you know you It's basically a file manager But it's used for specifically choosing a file to upload or save on xorg Oftentimes that file picker or at least in the past Oftentimes that file picker was attached to the individual applications So you'll see this a lot with specific types of Applications things like GIMP they have their own file chooser it things like Firefox Oftentimes would have two different file choosers depending on which desktop environment you're in so it would have a QT file picker Or it would have a GTK file picker you get the idea right each application would be in full control of what that file picker Looked like and oftentimes it was contained with the application itself right so it had control over everything that the file chooser had Access to which usually included all of your files So that interaction basically between two different applications is what Wayland was trying to Prohibit when they came up with the system or when they designed the Wayland system right at least as I understand it It's not just pickers that have to do with this thing things like screenshots screen Sharing things like access to network status all that stuff is Stuff that happens outside of an application that an application oftentimes needs access to but in Wayland can't because in Wayland Applications can't access outside of their sandbox So their solution was to create different portals and basically each desktop environment in Wayland compositor basically has created their own portal which gives access from an application To a system level service or action So for example if you want to do screen sharing inside of OBS by itself without a portal on Wayland OBS cannot do screen sharing or screen capturing at all. It just can't be done Okay, because OBS is sandboxed with a portal basically It is a bridge between OBS and those system level functions allowing things like screen capturing So you install something like the XDG portal for KDE or GNOME or Hyperland or whatever And that runs as a system service in the background basically allowing any application Which is sandbox to access the features that those portals provide so things like screen sharing things like net network access Things like a file chooser all that stuff, right so basically To boil it all down a portal is simply a bridge between an application on Wayland and an underlying suite of services that allow it to do certain things like file picking like screen capturing and so on so I hope that I explained that fairly reasonably and I will also just note here that I'm a very much a Wayland noob So I'm trying to explain this from a noob's perspective because I am one I are one and Just if I got anything completely wrong, I'll try to correct it in the comment section below I'm sure someone will correct me if I'm absolutely wrong in the comments below as well So that happens. So that's basically what a portal is and it kind of goes a long way Towards explaining why they're needed because without them applications on Wayland do not have access To any of those services or capabilities that they would normally have on xorg things like screen sharing file choosing all that stuff Right. So the portals are what enable applications to function and Communicate with other services and applications. So without them Wayland would be not as useful So with all that said, let's then talk about My opinion on portals just because I can't do a video without me talking about my opinion on on Wayland or portals or whatever So earlier in the video, I talked about how Wayland was basically ready for me And I think that I am at that case. I've made a video about it recently I don't need to rehash all the reasons why I've switched why I thought now was the time all that stuff But what I want to talk about instead is that there is one part of the Wayland ecosystem, if you will That still doesn't feel quite ready. And of course, I'm talking about portals So I have been using Wayland now for about three weeks and I'm using hyperland so if I actually show you my hyperland desktop, this is hyperland here and I've been using this now for about three weeks. It's a very good window manager It's a very good Wayland compositor if that's the words you want to use to to describe it I've it's a very good experience. I've had very good interactions with a developer I've had had bugs that I've submitted and had fixed almost immediately Which is just unheard of and a project this big So I've had a very good time with hyperland But you've probably also heard me talk about some things where I've been unhappy Mainly when it comes to actually having my screen captured in obs That's where I have been having the most problems And all of that surrounds all of the problems that I've had are directly associated with the portal itself So I can't actually show you this because it's working right now But if you watch the previous podcast, you'll know that twice The screen capture that I was using to capture Tyler On the podcast froze and I had to restart the portal and Basically set it up to from the beginning in order to actually get it to work it froze twice I've also had this weird issue where the first time I open up obs For the day It will bring up the little picker for which screen I want to capture Which is the way it's supposed to work and I select The main monitor which is the one that I almost always want to capture because it's you know right in front of me But for whatever reason it won't capture that monitor right away It'll capture the other two monitors for whatever reason it does fine with two Won't do anything on the third. I have to restart the portal in order for it to capture that monitor It's really weird So my point here isn't to complain about the developers or the development of the portals It's just that for some reason oftentimes when you do have issues on Wayland Chances are The thing that's causing those problems at least in terms of Wayland compositors at least Is the portal the portal that you're using and I think that oftentimes the reason why that's the case is because the portals are Moving targets that are being developed as they go along. So not only do they have to Deal with changes inside of Wayland and the compositor. They also have to deal with changes On the applications part. So when OBS changes something really weird The compositor has to change in order to accommodate those changes. So All these moving pieces sometimes leads to bugs. It's the Way things go basically with anything. So it's not that surprising But I think that overall my experience has been brought down a little bit because of the bugs that are still in the portal system But my experience isn't the norm for most people Because I capture the screen a lot more than most people do. I mean some people take a lot of screenshots Most people don't make a ton of youtube videos That's just not a normal thing that people do So my experience with OBS and with certain parts of the portal are going to be different than everybody else's And from what I've heard from most people They usually don't interact with the portal outside of a file picker So as long as the file picker works, which he almost certainly always does They're not going to have any problems with the portal Whereas I because I interact with it in different ways than most people do I'm gonna kind of see Where some of the bugs still kind of lie So I hope that I did a fairly good job of explaining what a portal is And if you still have questions, you can leave those in the comment section below I'm sure there are technical details that are beyond me that someone else can explain But I'll still try to answer questions if you have them in the in the comment section below You can follow me on mastodon or odyssey those links will be in the video description You can support me on patreon on patreon.com slash the linux cast You can also support me on kofi or as a youtube member Via the button down below my face You can also find all of my merch over at shop.thelinuxcast.org There you'll find hats and t-shirts and desk mats and all sorts of stuff All the proceeds for that go directly to help me make more linux content So if you would be so kind just head over there shop.thelinuxcast.org I'd really appreciate it. Thanks to everybody who does support me on patreon and youtube because they're all Absolutely amazing without you the channel just would not be anywhere near where it is right now So thank you so very very much for your support. I can truly do appreciate it. Thanks everybody for watching. I'll see you next time


Ευχαριστώ πολύ για να παρακολουθούσατε τη δημιουργία μας, σε αυτή η σημερινή σημερινή. Είμαι Γιάννη Στοιιτσής, είμαι ο κ. Αγρονο, και σήμερα θα μιλήσω πώς μπορούμε να καταλαβαίνουμε τα δημιουργία της δημιουργίας, by introducing a new solution that will catalyze the access to food safety data. Θα ξεχωριώ με τον σημερινό μου ένας πραγματικός, μια δημιουργία που είχα ποτέ ώστε time με ένα κουραγία της δημιουργίας της δημιουργίας της ευρωπαϊκής Ευρωπαϊκής, εμείς είμαστε παρακολουθούντας για την δημιουργία που η δημιουργία της δημιουργίας πρέπει. Ξεκάζεται ότι η δημιουργία θα πρέπει να μην καταλαβαίνει όλα τα δημιουργία με το κλεικ της καταλήμπτης. αυτό δεν σημανωθεί με την τεκνότητα back in and which ventos make the solution and by accessing such critical data, were will be able to prevent food safety and fraud incidents he told me which are increasing during the last years causing financial significant financial loss. και αυτό είναι κάτι πολύ σημαντικό. Υπάρχει για πολλές χρόνια στην κατασκευήκη, με τα δέτα προβλήματα στην κατασκευήκη, και τα φωτοσύκταση, έχουμε δημιουργήσει ότι έχετε τρεις τρεις φορές, όταν έρχεται να εξετάξει όλα τα δημιουργία, τα εξαιρετικά δέτα, τα εξαιρετικά δημιουργία, τα εξαιρετικά δέτα, τα εξαιρετικά δέτα, τα εξαιρετικά δέτα, και τα φιλοσυκτικά δέτα. Πάμε να πούμε τις ισχύεις των αυτών. Πάμε να ξεκινήσουμε τα εξαιρετικά δέτα. Στο κατασκευήκουμε και αυτό είναι κάτι που μιλάμε, όταν μήνες τρεις σηκώδες, ότι το φορές είναι τραγουμένοι, να παίρνει πιο και πιο στιγμή. Αυτό είναι ένα σημαντικό amount of data that is generated by different entities and at different stages of the food supply chain. Data from the food production, data about the weather, environmental data, records data, laboratory testing data, production data and many more. Data that are stored in local databases in information systems that are disconnected. And this leads to data that are stored in data silos and are completely disconnected. So every time that we need access to the global food safety data, we are frustrated that we cannot use all this data to assess the risk and to predict the risk. The second obstacle category of obstacles is the internal ones. It needs a lot of time and resources in order to set up internally and to build the know-how that you need to deal with all these data sharing challenges. So you need resources, you need to restructure the team, you need to change workflows and processes. And we should not forget that in all this internal change there will be a cultural resist to change. So you need also a lot of effort for the change management. And my favorite is also philosophical obstacles. We had a lot during the last three days that together we stand, we fully, we very much agree to that. If we are able to involve all the key stakeholders and to collaborate and sync efforts in data sharing, then we will be able to create high impact in the food supply chain and we will be able to prevent incidents. This is what we believe, but it's still difficult. There are many obstacles and it's difficult to set up such synergies and collaborations and to create such impact. And we have authorities that are suggesting and that are pointing out that it's very important to create public-private data trusts, large volumes of data that can strengthen preventive measures, but still such collaborations are difficult to be set up and there are obstacles that we need to address and to need to overcome. However, we should mention that there are some interesting cases like the case of Food Industry Intelligence Network, where more than 30 food companies are actively involved in sharing in a secure way the results of the laboratory tests that they perform in a way that is secure and also anonymous. And there are very well-defined processes in order to perform the data submissions, to perform the analysis of the data and to create the reports that are shared among this network. But it's still difficult to scale up such approaches and to bring also the public sector into this picture and to make the data sharing between the private and public sector, but between all the involved stakeholders of the food supply chain. We at agronau help food companies to prevent food safety incidents and we care a lot about your data sharing challenges. That's why in every work that we are doing, we are starting from the business challenge, from the business question. We understand very well the business question, we define the data that should be used to solve to address this business question or the different data types that they should be used. We use the technology as an enabler and we provide a solution to this kind of data sharing challenges. We also understand very well the data silos and we innovate to break them. So this is why we have been invited every time that there was such a challenge in the past, such a data sharing challenge in the past. So this was the case of aggregating scientific information in FAO AGRIS, which is the most important bibliographic engine for the agri-food sector. This was also the case of how we could aggregate and harmonize training information in global food safety partnership. This was also the case of aggregating and harmonizing all the available open data in the agri-food sector in the global open data and agricultural nutrition initiative. And using all this experience, we are now aggregating and harmonizing and combining global food integrity data to enable risk prediction. We innovate, we test and we introduce new technologies in order to facilitate the data sharing and the secure data exchange in very critical processes, like for instance the certification process. So this is the case of this BFSM project, which is an initiative that focuses on how we could share data in a secure way using the blockchain technologies in a way that will allow the travel, the secure travel of the data from the certification bodies to the food producers and vice versa throughout the different stages of the supply chain. We also share our knowledge and our experiences by writing about data sharing challenges. So in the block of agronome, you will find several articles about the data sharing challenges, about the technologies, the standards that can be used in order to address these challenges. And today we have a plan to better on how to better understand the emerging risks and to protect consumers by combining processing and extracting meaning from as much data as possible. That's why we introduce for the first time an online solution that will catalyze the access to the global food safety data. Risk data is the solution that provides the world's food safety data at your fingertips. It unifies and simplifies the access to million of food data records, data records about suppliers, hazards, recalls, border rejections, inspection results, scientific information, laboratory testing data, even production and trade data. Data that come from many different data sources and data that cover the majority of the regions in our world. The risk data is based on a data engineering on the data processing engine that does all the heavy work of processing, analyzing, enriching the data. So we apply big data processing techniques to collect and process the unstructured data. We apply artificial intelligence technique and text mining techniques to harmonize the data. We translate the data, we enrich the data and we also apply technologies to exchange the data when it is required also to integrate data that are sensitive and where we need to have security and privacy. And this data engine is open so it can also connect and it can also collect any new data type that will be required. There are only three steps that one should do in order to get access to all this data. First to visit riskadata.com then to go and select the type of the data and the format of the data and the delivery mode of the data that he needs and to the third step is to receive the data offer with the data that he needs in order to improve the risk assessment and the risk prediction. Not having and leveraging the right data will lead to invest serious times and a serious time and resources in order to set up internal data engineering and data science team to set up all the new processes. And this will focus and this will make all your resources to focus on solving the data problems rather on focusing on the risk assessment and the predictions. And until you achieve all that to set up and restructure everything there may be critical information that will be missed both for the ingredients in terms of hazards for the ingredients that you are using, but also in terms of incidents for your suppliers. So having and leveraging the right data can help data science departments that need data to improve risk assessment and prediction models. And they can also it can also help the food safety experts that need specific data in order to assess suppliers and ingredients, or they need to receive to create very specific reports in order to make some decisions, some critical decisions. Using such an online solution for risk data, you can establish a data driven decision making approach by getting access to live food safety data streams that are collected every few seconds to get access to data that are connected and that are not disconnected. So this data are combined and with one query you can get information from different types of you can get the answer from different types of data. You can streamline processes and food safety systems connecting all this data and you can select in which was or which way or which mode and format you would like this data to be integrated. So you can start by visiting the riskadata.com by telling us what type of data do you need, and in which delivery format, and we will get back to you with the data offer with the main goal of helping you to achieve your goals in terms of risk assessment and prediction. Of course, you can visit our booth and we can continue this discussion about the data. Thank you so much for your attention.


Statistics and Excel. Histograms with car-related data. Got data? Let's get stuck into it with statistics and Excel. You're not required to, but if you have access to the OneNote workbook, we're in the icon left-hand side. OneNote presentation 1065. Histogram with car-related data tab. We're also uploading trends. First, a word from our sponsor. Well, actually these are just items that we picked from the YouTube shopping affiliate program, but that's actually good for you because these aren't things that were just given to us from some large corporation which we don't even use in exchange for us selling them to you. These are things that we actually researched, purchased, and used ourselves. Bayer Dynamic? Not sure if I said that right, but this is the DT770 Pro 250 OHM Studio Reference Closed Back Headphones. I wear headphones basically every day for a large part of the day. They are important to me. Therefore, I've gone through many different kinds of headphones. I've had these for some time and they've worked quite well. They fit over my ears, but I'm still able to put my glasses on under the headphones. The headphones not pinching too tight on the glasses to give me a headache, which is nice. The quality of the patting is good and it has lasted for some time. I've had these for some time now and they haven't gotten all torn up on me or anything like that. I also like that I have a cord when I'm doing my recordings as opposed to a USB centered headphone because that frees up a USB port and I find the USB headphones to be less reliable. They come with an audio jack that looks like this, which is useful for me because that plugs into my audio interface. However, if you want to use the headphones for some other purpose, I believe it's fairly easy to get a converter to other types of audio jacks. If you would like a commercial free experience, consider subscribing to our website at accountinginstruction.com or accountinginstruction.thinkific.com where we have many different courses. You can purchase one at a time or have a subscription model giving you access to all the courses, courses which are well organized, have other resources like Excel files and PDF files to download and no commercials. Scripts to one note so that you can use the immersive reader tool, change the language if you so choose, and either read or listen to the transcript multiple different languages using the timestamps to tie in to the video presentations. Desktop version of one note here continuing on with our theme of taking data, making pictorial representations from it, the primary tool this time being the histogram. So we have a car related data. If you want to look up data sets you can practice with, we suggest looking up kaggle.com. Our first data set has the name of the vehicles on the left hand side and the miles per gallon. If we were to put this information in Excel for example we might then first want to sort the information by the miles per gallon because oftentimes we will first have the information by the name. But if we have a whole lot of data then still that's not going to give us enough to really extract meaning from the data so oftentimes we might want to look at other tools such as calculating the average and the median if you were to do this in Excel the average has an average function the average or the mean would be equals average and then just simply selecting the data is going to give us the 24 in this case. What is that average doing? Well it's taking this whole column of numbers summing them up and then dividing by the count of those numbers and then the median you'll recall is taking the one in the middle just like Rocky the boxers coach told him to do hit the one in the middle if you see three of them out there. So we took the median and in Excel the function would simply be equals the median or the second quartile but this is the more common function and you've got the 23 so that would be just simply picking the one in the middle so those are some mathematical data pictorial representation you might then have the histogram so in Excel creating the histogram typically as easy as selecting the entire data set and then inserting the histogram Excel then populating the buckets so now we're looking at the miles per gallon falling between 9 and 13 and then 13 to 16 16 to 20 20 to 24 and so on and so forth. Now if we look at this histogram we don't it's not exactly like a bell shaped kind of histogram right it's skewing to the right meaning we have the tail kind of happening over here towards the right so that when we get up to 46 to 50 miles per gallon we you know this one could be you know kind of outside the normal range so what is our goal typically when we're looking at this data we want to kind of have an idea of the center point if you think of this as like a teeter totter where does it kind of center over and then what is the spread that that goes around that center point and is it possible for us to then kind of approximate this with with a curve or a line of some kind that we can predict that we can make mathematically not always will that be the case right this doesn't seem to approximate exactly some kind of curve that we can easily make with a function the reason we would like to make a function out of it with a mathematical equation if it were possible with a data set is that that gives us more predictive power because now we've got you know an equation that we can plug numbers into which would be great but not all data sets will comply with what with what we would like to let's take a look at another one this is other car related data so we've got the name of the car and then the cylinders so the number of cylinders so if I was to then take the average this is the median and I've added the max and the mean these are our average calculations or our normal calculations that would do pretty much every time right we got our data we can sort the data so now I've got the highest ones on the right and then they go down to the number of cylinders down to three so clearly when we're looking at the number of cylinders notice the data set you expect whole numbers clearly right we're talking about how many cylinders are in a car so you would expect you know somewhere between two and eight here right the high so we don't have as much kind of variance of data and this could give us some different ways that it might be like easier to create the grass we'll take a look at but first we can calculate the average so we can sum this up and and take the average uh which is five now note that when we think about five you might not have a whole lot of vehicles that have five right you don't have any vehicle well there's three that have five cylinders but normally you would think going you know to four to six uh generally so remember that that average can sometimes depending on the data set be a little bit misleading so we have to know what we're talking about the the median the one in the middle uh is four this is the calculation for the median if I was to select that data set the max is taking this data set and picking the highest value the formula for that in excel is simply equals the max and then the data set and that's eight and then the minimum is three equals the minimum in excel will give you the minimum these are you know common formulas in excel the most common formula of course the sum function uh but then the other function is quite common average medium and then the less common but often quite useful to know about is the max and the men so then uh notice that uh we can we can have a graph representation fairly easily in excel here because we know when we think about the cylinders we don't have like 1.2 or you know we don't have a lot as much variant we know it's going to be somewhere in this case between one and eight so it might be useful to just look at this in a table format to just see how many populate between one and eight and it's an easy formula to make that in excel because we can use the count if formula to do that so the count if would be count if the range so we're taking this range and then we're we're saying uh the second is the criteria count if this is a one now none of them have a one uh two count if it's a two none of them have a two three there's four of them four there's 204 so clearly that's the the biggest number five although that was the average is is not the one that we're expected to have on outcomes just because knowing a little bit about how how many cylinders the car has and then six uh 84 and eight uh 104 here's another count if just picking this cell up same formula taking this dangerous data range so then if we were to construct a histogram with this if you just select this data and create a histogram then it creates our buckets uh three to four four to five uh five to five five to six now we can adjust these buckets down here because really we would want them to just be one through eight possibly but sometimes when you create a histogram in excel that can be a little bit tricky to try to get this uh this x axis to be exactly what you want it meaning i would like this to just be you know one two three four to eight because it wants to it needs to have a range so so so that could be a little bit tricky and so when you see this kind of data sometimes it might be easier to say yeah i would like a histogram but but instead of of making the histogram i would just need a bar chart so it might be easier actually to make this table and then create your histogram from a bar chart based on this table instead of the data set so we can come down here and say this is another histogram basically created instead of from this data from this little table we put together which sometimes is easier to put together than others in this case because like if we had a range between one uh zero and one of a bunch of decimals and we don't know what it is then the rain it's going to be a little bit more difficult we need the ranges we need the buckets but if you have something like this now i can say hey look i i just need eight eight buckets right that are pretty well defined so i can create a table and then make our our histogram uh in this format so now i just got one two three four five six seven and eight and so and and this might be a little bit more clear format for us to put it in recognize just recognizing what the data looks like and making a slightly different kind of uh histogram or using a different tool we might trim this down even further and say hey look i don't really need the the ones that don't have anything in it because i only have a few factors so we might cut down the data set to something like this and then and then we might get a little fancy we got a little fancy with the with the the colors here so different formats for uh the histogram let's take a look at another one uh in this case we've got the name and the horsepower so in contrast with the horsepower notice that the horsepower might vary a lot more so so i can't so it's going to be more difficult to use the two use the second method we saw last time so the general idea we've got the name and then we've got the horsepower we might try to sort the horsepower from low to high or high to low and that will give us an idea of the horsepower but we would like to see it see other information related to it here's our standard calculations let's do it this time this way the average taking the average which is summing adding all of these up and then divided by one two three four the the count right and then we're going to take the median take the one in the middle just like rocky the boxers coach told them to hit the one in the middle that's the one we're going to do we're going to hit the one in the middle and that's the median function we've got the max that's the highest one the function to do that in excel is simply equals the max meaning take the highest number in this data set and the min is just simply going to be equals the min brackets data set take the lowest number in the data set so when we're looking at horsepower we've got the max 230 the min 46 the average 104 and the one in the middle uh nine 94 now just looking at these numbers note that that like if this maximum is an outlier it's like 230 it's you know this it's not like it's out there all in and of itself but remember that the outliers on the high or the low end could have an impact on the average versus the median so if you see a big difference between the average and the median there's not a huge difference here but if you see a big difference then your thought process would be well maybe there's some outliers that are skewing the average that you need to kind of keep an eye on so if i look at a pictorial representation of a histogram we have something that's uh that's that's skewed to the right because we have the tail end on the right meaning the ones out here at uh 226 to 244 the high horsepower there's only a few of them out here right so we've got kind of the middle that's somewhere around here right the 104 is the average so the middle point if ours to take a teeter totter you know and try to put it like at the middle so this thing balances you know left to right uh kind of thing on the histogram and there is our spread of the data so clearly this is going from the low the buckets 46 to 64 the 64 to 82 the 82 to 100 the 100 to 118 and so on and so forth now note it is possible for us to create a bar chart and recreate this data but it's not as easy as with the last data because i don't simply have just one through eight different buckets that are well defined because now i have numbers you see these numbers i need a bucket i need something going from i can't list every number from 46 up to 244 that would the chart would be useless so so i have to have some kind of range on the buckets so that means if i was to try to make a a bar chart histogram i i would be a little bit more difficult because i would then have to make the ranges myself and then use a little bit more complicated it's still doable but you have to a little bit more complicated uh uh calculation to pick up the numbers that are between a certain range that's still a useful tool to do we'll do it in excel uh in future presentations because sometimes you might want a bar chart even when you're using the ranges because you might for example want to have two bar charts on top of each other and that might be easier to do then uh then with the histograms you might have a little bit more flexibility if you create your own table in other words and then create a bar chart with it then you do with uh the histogram tools so we'll take a look at those in future presentations