Welcome to this video lecture where we're going to simulate a p-value. So we're going to have two different groups, same numerical variable, and we're going to compare the means between those. Now, under the null hypothesis, remember, there is no difference in the mean. So we're going to use the simplest form of the null hypothesis. So we're going to state that there's zero difference between the two means. Two-tailed hypothesis with a zero difference. And I'm going to use the Julia computer language to do this. Now, I do have a video lecture out on how to install Julia, but just in case you haven't done so already, there's the website JuliaLang.org. And you can just click on the download button there. And you'll see at the time of the recording, we had the current stable release of version 1.7. And I'm going to do my coding in Visual Studio Code. So you can just search for Visual Studio Code or code.visualstudio.com and you can download the appropriate version for your operating system. Now, I've set up my system so that I can type Julia in a command prompt or terminal window here on the Mac and it will start Julia for me. There is a command that you can run. I'll show you the one for macOS on the screen. So that will add Julia to your path environment as a path environment variable and you can just type Julia and it'll start automatically. The reason why I do this is that I have separate environments for all my Julia projects and I install only the packages in that environment for that project. I don't install any packages in my base Julia installation. Once again, there's a description down below that will take you to a video where I show you how to set this up. So what I've got to do in my terminal window here is just to navigate to the folder that holds this environment for me. So there we are in the environment. I'm just going to show you the files that are in that environment and you'll see there's a project.to.ml file and a manifest.to.ml file and that holds all the information about the environment, the Julia environment that I've set up for all my data science projects. And all I have to do now because I've installed Visual Studio Code is to type code, space and a period or full stop, hit return or enter and that's going to start up Visual Studio Code for me. And there we have Visual Studio Code and the file that I'm going to look at here is resampling under the null hypothesis. By the way, for Visual Studio Code, if I click on these little buttons on the left hand side, you'll see all the extensions that pop up and you can do a search and there is Julia. So you can see I've installed that extension and that is what allows me to use Julia inside of Visual Studio Code. When I click on settings there, tool down and I click extension settings and if I move down, you'll see the path on my system to my Julia file. So it's applications Julia-1.7, the current version at the time of this recording.app, fort slash contents, fort slash resources, fort slash Julia, fort slash bin, fort slash Julia so that this extension at least knows a way to find my Julia executable. So resampling under the null hypothesis. So we're going to certainly look at that but also there'll be a few other things in this tutorial. We'll look at seeding the Cito random number generator which has been new for version 1.7 and we do that so that we get reproducible results when we call for random values. I'm going to show you just how to create a data frame again, show you how to sample from categorical values using statspace, sample from a normal distribution using the distributions package, make sub data frames using the filter function so we can do some conditional selection so that we can create new sub data frames, how to extract vectors from data frames, how to do summary statistics of continuous numerical variables using statspace.  I'm going to show you how to shuffle a vector, so all the elements in a vector, how to shuffle them around at random and then how to calculate a p-value using the hypothesis test package just so that we can check on our results. So as always, we start with the packages that we're going to use. I'm going to use the data frames package. I'm just going to be in that line that contains that code, hold down shift and hit return or enter and that's going to spawn the Julia REPL for me at the bottom here of Visual Studio Code and also activate the environment that I've set up because if we see that Julia prompted at the bottom, if I do my right square bracket, you'll see I'm not in the base installation of Julia. I'm in the data science environment for Julia. And by the way, if I just type ST then and hit enter, I see all the packages that are installed in this data science environment. So there are all the packages and you can certainly look at those and install them for yourself and that'll be a good set of packages for data science projects. Let's carry on. We're going to use random. That is a built-in package in base Julia using distributions, stats base, Plotli.js and hypothesis tests. So the first thing we have to do is at least to set up our data and we're not going to import any data, we'll just simulate some. So I'm going to have a sample size of a thousand and I'm going to store that in the computer variable N and then I'm going to see the CIDR random number generator just with a integer 12. If you use 12, we're going to all get the same results. I'm going to highlight both of those two lines of code. The second one is setting up a data frame and I've done that over multiple lines and I'm just going to execute and I'll explain how I set that data frame up, holding down shift, hitting return or enter and we're going to get a data frame and we can see there it's a thousand rows and three columns. So let's have a look at that. Use a computer variable DF, quite common. Use the data frame function and that comes from the data frames package just so that you know where these come from. I could say data frames dot and that'll do exactly the same thing just to reference the fact that the data frame function comes from the data frames package. So I'm going to have three columns and you can just write any words without illegal characters and they don't have to be strings or symbols. You know, the letters themselves. So ID, I'm using a range there, one colon N and remember the N holds the value of a thousand. Then I'm going to have a group column and that is going to be the sample function from stats base and the first argument there is going to be a vector and that vector contains two strings, one Roman numeral 1, Roman numeral 2 comma N so it says sample with replacement from those two elements a thousand times and then mass is going to come from, just could use generic variable mass that can be of a product of an organism, of a human being, doesn't matter what it is. I'm trying to be agnostic as far as our variables are concerned so I'm going to use the ran function to generate random values for me and I'm going to say this first argument is from the normal distribution and now this normal function, remember that comes from the distributions package so we can go there and say distributions dot and that means we can see where that normal function comes from and it takes two arguments, the first one is the mean and the second one is the standard deviation so I say a normal distribution with a mean of 100, standard deviation of 10 and I want from that distribution randomly select 1000 values for me and that's how we end up with our variable, our dataframing with a thousand rows. Now I want to make two sub dataframes. I know that I have two groups by my categorical variable called group, there is my categorical variable group and I'm going to use the filter function so that I only select rows which contain the value one in the group column so we use the filter function and then I'm setting up this anonymous function here and instead of row you can just say x and then it'll be x and then minus greater than so a little arrow symbol then you could have said x dot group but I'm going to use the word row because it kind of makes sense so go down row dot group and id with a group at least equals equals so that's conditional, is it equal to one? If that conditional returns true it will be included, if it's false it won't be included and that's what the filter function will do so if we ran this, shift return, shift enter we're going to get a new dataframe it only has 479 rows but you can see down the group column we only have Roman numeral one so it's all our observations that fall into group one and if we do that the same using the conditional for two we see 521 rows now and you can see under the group column there's only Roman numeral two so we've created two sub dataframes manually using the filter function next up what I like to do in many situations is just to extract a vector from a dataframe column so I'm going to take all the mass df dot mass so that's going to give me that kind of notation is going to give me all the values in the mass column and I'm going to pass that as an argument to the collect function which is going to return for me a standard Julia vector and I'm going to assign that to the computer variable mass so once that runs I see now I have a 1,000 element vector of 64 bit floating values and that's exactly what we want and I'm going to do two more where I just separately look at the masses for my new sub dataframes group underscore one and group underscore two so I have those two vectors as well remember again with the 479 and 521 elements and lastly I just want to know the sample size of each of these I should say so I'm just going to store those as n underscore one and n underscore two as Roman numerals and that is just passing this vector as an argument to the length function so that I get back those two lengths so as always we start with descriptive statistics if we want to know what's going on with our data and there's a described function in the stats base package and if I pass that vector to it we'll see down here if we scroll down in our repel at the bottom we see the summary stats we see the 479 elements there's zero missing the mean was 99.96 the minimum was 69 first quartile median third quartile maximum and the only thing that you don't see there is standard deviation which could of course be helpful but of course you can just use the STD function to do that let's also look at group two the masses of those in group two and we see the 521 values and they had a mean of 100 and the question is now is there a difference between these two means so it's the same variable mass two different groups formed by the sample space elements of the categorical variable so group one and group two next up we're going to visualize our data and I'm going to do it using the plot function from PlotlyJS remember we imported PlotlyJS so plotlyjs.plot and I'm just doing multiple lines which of course visual studio code is going to underline with these little blue markers because of things there is a problem but it'll work fine my first argument is my data frame on my x-axis I want mass and for my color argument I'm setting that to using symbol notation here for group and remember symbol notation there as well for that column mass so it's just going to separate out the masses for each of the two groups the kind of plot that I want is a histogram and then I just want to change the markers a little bit because remember where these two histograms drawn on top of each other you want a bit of transparency so that you can see both of the histograms so I'm going to set marker equals and then the attribute ATTR I pass a value of 0.5 or half to opacity and then layout is going to give me an access to the title argument and the bar mode argument and the bar mode I'm setting to overlay so that the two are drawn on top of each other so let's run that remember this is Julia and there's a bit of work that needs to happen behind the scenes before the first plot is rendered so that's going to take a second or two and there we see first of all right at the bottom you'll see a new icon there that's our Julia icon that's part of the Julia extension that we installed and that gives me all sorts of information in my current workspace for instance we'll see some values there N is a thousand N sub 1 is 479 we can see our data frame there so we get information on the variables and now we suddenly see a new tab with a plot and there you can see group 1 and group 2 and because this is plotly I can click on that and remove one of the groups now they both removed let's look at one so I can see the histogram of these separately and that's what's lovely about plotly and we can see the distribution there as far as the histogram is concerned it looks very similar and we don't expect that there will be a difference between these two means so let's go about doing that and we start by just looking at what the difference is in our data set and I'm going to store that difference in difference in underscore means variable and I'm just passing two vectors holding the masses for both groups passing that to the mean function and just doing that subtraction and I get a difference of minus 0.16677 etc now remember there was no reason to subtract mass 2, the mean of mass 2 from the mean of the masses in group 1 I could also have swapped that around and that'll give me a positive 0.16677 so that's very important to remember we have to look at both differences so now we're going to do this reassignment under the null hypothesis if you think about this very simple null hypothesis that there's no difference between the two means that means it does not matter in which group any one of our instances falls we can just shuffle them around reassign them to a different group now what we're going to do is we're going to keep group 1 at that sample size and group 2 at the original sample size but we can just swap members around between those so instances around those between the two under the null hypothesis there's no difference in the means between these two groups and what we say by that is or what we mean by that is it doesn't matter in which group our individuals fall the means are no different and they shouldn't change and if we do that multiple times every time we randomly reassign members to the two groups we calculate two new means and we take the difference of the two means and then we build up a distribution of all those possible means so we just do that thousands of times over and every time under that reassignment we're going to get a different difference in means and we can build up that sampling distribution and remember that that sampling distribution should then start to approximate a normal distribution so let's do just that I'm going to set up an empty vector there and I'm going to call it means and I'm going to do 5,000 reassignments and I'm going to do that with a little for loop so I'm going to say for i which is this my counter in 1 to resample so in 1 to 5,000 so this loop is going to this for loops going to run 5,000 times and what are we going to use here is this the shuffle function as you can see there the shuffle function comes from the random package so random dot shuffle and mass is just all the masses remember that's of all my subjects but I just randomly shuffle that and now I'm going to just assign that to a new computer variable shuffled mass and now I'm going to create two new groups new group underscore one and that is the shuffled mass so the order has been completely reshuffled but I'm going to go from 1 to n sub Roman numeral 1 remember that held a value of what my first sample size was so if I go from 1 to there that gives me the same sample size and then new underscore group underscore 2 that is also going to be shuffled and I'm using indexing again I'm starting at n underscore 1 plus 1 so the very next subject right till the end so it's that first lot and the second lot and they all reshuffled so I've randomly reassigned individuals to new groups and then I'm going to use append bang so that exclamation mark there means I can just continuously append to what is at the moment at the first loop is an empty vector there and I'm going to append to this means function to this means a computer variable the mean of the new group 1 minus the mean of new group 2 so I'm just every time this loop runs through there'll be a new difference in means added to that empty vector so let's just do just that so I'm just going to hover over the n there hold down shift hit return and almost instantaneously I have my 5000 values and what we're going to do here is just to plot them so I'm using plotly.js.plot again this time slightly different notation I'm just going to start using a data frame I'm just using a vector to plot so put that inside of square brackets use the histogram function as you can see there on my x-axis I want all those 5000 means and I'm adding a little bit of opacity and then outside of the square brackets is my layout and my layout is just going to contain just going to contain a title so let's run that and there we can see my 5000 sampling units of sampling distributions of 5000 values there and you can see that approximating a normal distribution now our mean, our difference in means that's our test statistic for somewhere there remember it was negative 0.16677 so that's going to lie just to the left of the 0 here but I could also have done the subtraction the other way around so I've just got to consider positive side as well so positive 0.166 and if you draw this up there you can imagine I want the area of if we were to use a function to the left of that towards negative infinity and to the right towards positive infinity what we're going to do here is just to ask what fraction of values was less than minus 0.16677 and what added to that the fraction of values that were more than that and that is going to approximate for us a p-value so let's just do that and it's a simple line of code here I'm going to sum over means dot less than difference in means remember my difference in means was my actual value so I'm wanting to know all the values how many values are less than that in my whole vector of 5000 means and I'm using the dot operator there such that I can do subtraction or that comparison at least element by element and because that returns true or false values for me and true is represented by a 1 and false is represented by a 0 I can sum over all those and that sum is just going to tell me the number of values in all my 5000 means that was less than my actual difference in means and I'm going to add to that doing the same things that were more than minus the difference in means so that's going to be positive 0.16677 and I'm dividing that by 5000 once I do that addition so that tells me the fraction of my 5000 values that was less than my difference and more than the reflection on the other side of my difference in means and if we do that we see 0.795 so that is approximating a p-value for me there let's just use the hypothesis test hypothesis test package there's a p-value function in there and the argument that I'm going to use is the equivalence variance t-test and I've just got to pass the vector 2 vectors mass underscore 1 and mass underscore 2 and that's going to work out the p-value for me and we see it's 0.7932 so very very close to each other those two and that is exactly how we approximate the p-value how we use the null hypothesis this reassignment and we work out just based on the actual difference in means that we have that's our test statistic what fraction of values are less than and more than on either side of our re-sample a histogram as you saw there so conceptually a very clear understanding of where this p-value comes from how we have this p-value for the difference in means


The first thing I want to do is say thank you very much to my co-authors for today. I've been very fortunate to work on a large project that worked on the sites of the 여id indices, several different teams have done work on them, and with the help of Stephanie and Ivana we've tried to bring some of the results together, and this paper is loosely based on some of the analysis they did when they did the dental microwave of those two sites, i chi'n gwneud am gyfwyrdd ac yn gwneud o adlwyddoedd a i'w meddwl i chi'n gwneud o amser i ddweud ym mhwysig yma. Felly, y ddweud i'r sefynt ac rwy'n gofio ar gwrthod ar gael ymlaen ymlaen, yn ymlaen i'r cyffredinol i gwybodaethau i'r ddweud i'r ddweud, i'r gweithio i'n gwneud i'r ddweud i'r ddweud, i'r ddweud i'r ddweud i'r ddweud i'r ddweud i'r ddweud i'r ddweud i'r ddweud, A'r cyfnodd wedi'i gweithio'r amsgwrs dros y cyfnodd gyda'i bywydau yma yn cynhyrch ar gyfer bywydau biochologicol a'r bywydau funerol. Rydyn ni'n fawr i'w wneud i'r bywydau yma o'r archiologiad yw'r bywydau, ac rydyn ni'n ffoistio yma ymgylchedd o'i gael o'r cyfrannu mynd i'w ddiogelio ar bywydau biochologicolig. A rhywbeth nid i'w wneud eu gweithio a wneud i'r cyfrannu. A fyddwniddon i gael i'r byfyddo i'r bai sydd wedi bod yn ymgyrchau cysylltu bai yn piolion ymgyrchol a'r byddo i'n ddod yn ymgyrchaf, a'r ysgafoddau a'r unrhyw fyddwn i'n gweithio, fel hynny'r ysgafoddau ymgyrch. Mae'r byddo i'r ffunorau yn ymgyrchafol a'r byddo i'r byddo i'r byddaeth eich byddaeth, Llywodraeth o bobl yn ymwneud wrth gael y cyfrifiadau ac yw'r ffaith i fynd eich bod ni'n ddim yn fan hynny o'r cynnig o'r canhau ymddangos.  That is the context in which I work. There are different specific questions that we tend to ask. The first one tends to be about the kind of variation in social practice that we see. Is this around the social structure that's egalitarian? Or are we seeing various forms of inequality or hierarchy? a chael ei wneud i'r ideae wedi'i gynhyrchu i wneud cwyllwr, a'r cyflwygannol a'r cyflwygannol, ac y cyflwygannol yw'r dweudio'r cyflwygannol.  Rydw i'n ffôr i chi wedi rhoi cyfnodd arddangos, yna hon i'r cwestiynau o rhag o ddalfodol ac mae'r ddalfodol ffordd yn rhan o gwaith eisiau gweithio'r patent. Yma rwy'n gallu meddwl arall, ac rwy'n cael byddech chi'n mynd i chi'n rhoi heb ychydig ffordd ymlaen o'r cyfreith iail. Yn cael bydda i rwy'n cael ei wneud am y gallwn chyfrwygoedd, ac efallai gael lle, ac mae'n rhaid i'n falch i chi ddalfodol. Mae'r ddweud o'n mynd i'n ffordd o'r ddweud i'w ddweud yr yswmfyniadau ar hyn oedd y cyfle, boedd yng nghyrch yn rhan o'r modau lleol yng Nghymru yn ymwysig o fynd i ddataeth. Mae'n ddweud o'n ddweud o'n ddweud y byw argymell argymell ar gyfer ddataeth, mae'n ddweud o'r ddweud o'r ddataeth yn ymdrach o'r ddataeth o'r ddweud o'r ddweud o'r ddweud o'r ddweud. Mae'n gweithio i'r ddweud y'r ideaeth sydd yn y 70s, ac yn ymwneud yn bwysig o'i ddweud yw'r ddweud, ond mae'n ddweud ymwneud ymwneud yn ymwneud. Ond ydych chi'n ddweud o'r ffosona ffosona, ond mae'n gweithio'r gwaith yn ymddych chi'n gweithio'r gwaith, ac mae'n gweithio'r gwaith yn ymddych chi'n gweithio'r gwaith. Mae'n ddweud i'r gwaith i'r gwaith. Felly, we kind of use the bioarchaeology data to add information to that, to add information on to the grade goods, to add information on to the person in the grade. It's not necessarily wrong to do that, but I think we need to unpick some of the assumptions which come with that. Personally that it's, the variable can sort of be seen as a package that comes together to tell you about the identity of that person. That we can make cross-cultural analogies based on economics, perhaps that we can draw on anthropology in particular ways. And underpinning everything we do is that the variation that we see in the grades is meaningful for the questions that we're trying to ask. And I just, I've kind of become aware of that very much as I've been doing my research. I wanted to kind of lay it out as an observation that these are the kinds of assumptions that I'm trying to make. These are the kind of assumptions that I have to rely on in order to go forward with my analyses. So the biosocial approach then, if we're going to try and think about it in creative and being creative ways, I think the knowledge is now commonplace. A lot of people are now trying to do this. We're trying to blend these two things together, but people are doing it in different ways. And I think it's most useful and most powerful when we begin to test that assumption between practices of death and life. So whether we begin to see patterns that we see in the bioarcheological data repeated in the funerary data and whether we see any correlations between the two. And also where we try and use it to identify social groupings that perhaps wouldn't show up in the funerary data. Perhaps to begin to unpick that package a little bit and see where there are perhaps multiple or conflicting identities co-existing within the burial record. Okay, so turning to my case studies. They both come from the linear bank ceramic called the LBK. This is the distribution of the LBK. It starts from the Great Hungarian Plain about 5,500 BC and spreads out in two phases. I think they look a little bit like dinosaurs. So the little dinosaur is the first phase to about 5,300 BC and then a second phase reaching all the way from Ukraine all the way through to the Paris Basin. And we see a lot of homogeneity across that distribution. It's largely thought to have spread due to migration and the recent ADNA evidence seems to be confirming that. And then in terms of our existing social models, there's been quite some debate over the last 20, 30 years or even 40 years of the kind of social structures that we see in the LBK, whether it's roughly egalitarian and that's been argued quite nicely from recent research coming out of the Paris Basin or whether it's much more based around kin-based lineages and hierarchies, particularly coming from the funerary evidence itself that perhaps we have more inequality in structures as we go across the course of the LBK. So looking at the LBK burials in a little bit more detail, the sort of classic LBK burial rights as it's defined is an incubation in a cemetery, though we find lots of variation. The majority of burials are crouched, though we also find different body positions and on their left-hand side. And typically when we talk about the way in which grave goods are selected to go into individual graves, we see them as determined by age and sex. And I'd argue that's true for some grave goods but not for all of them. For example often you'll find pottery is supposed to be thought to be in female graves, but when I looked at all of the data from across the whole of the LBK that didn't stand up as statistically significant. It's a slight tendency for more pottery in female graves but it doesn't actually work when you look at the whole picture overall. But that kind of normal picture covers a lot of variation. When we look at the kinds of practices and deviations that people have they spread out across a whole range of different activities. So we find burials on settlements as well as cemeteries. We find disarticulated remains, we find them in enclosures, we find them in rubbish pits, sometimes considered to be disturbed earlier graves, but perhaps not. Because we also find evidence of people re-entering graves so this is where the anthropologist who's excavated it thinks that they've been moved and disturbed and the bones have been moved around more than biotubation would allow. So we have possibly re-entering graves as well. Also skull caps are found so people modifying skeletons to perhaps take objects for drinking with love of your ancestors perhaps not. But definitely modifying bones in particular ways. We also find cenotaph graves sometimes in conditions where there are quite good bone preservation so rather than it not being the bone that hasn't preserved perhaps it was never intended to be a body in the first place. And then we also have mass burials and perhaps graves without noticeable ceremony evidence of violence or violent activities where normal funerary rights haven't taken place after those deaths. So turning to the case studies themselves in more detail. This is the two these are the two sites, Bedro Vita and Mitra. They're often grouped together because they're the largest cemeteries that we find in those areas so Bedro Vita if you include the second one burials has about 110 burials whereas Mitra has something around 72 to 75 depending on preservation and preservation and how you look at double graves. Radiocarbon dating has been carried out on both of the cemeteries. Bedro Vita was fairly successful and they managed to get a much better more refined model out from the cemeteries and it seems to be slightly before Mitra so it starts 5,340 to 5,230 and then ends about 510 to 590 so quite big spectrums but um and a long lived cemetery in Mitra unfortunately the bones have been consolidated with PVC blue and when we went and analyzed the collagen it had impregnated quite a lot of the collagen so we were unable to radiocarbon dates very many burials but I think we managed to get eight reliable radiocarbon dates out they were then of course not the burials with pottery in all the ones that had any stratigraphy as it's of law but starting slightly later than Bedro Vita and ending probably around the same time. In terms of the pottery Mitra seems to be later than the earliest phase at Bedro Vita and about 5200 in this region there's some big shift in the pottery in allegiances so the Bedro Vita probably falls either side of that whereas Mitra is classic you need to start at that particular point. So three projects have worked on these two sites the first one was the bioarchaeology project led by Max Bellerbill the next one was the LBK lifeways project led by Alistair Whittle and I was the post doc on that project and then finally there's a work from my colleagues Zyrgynydd Cymrufana who've done the dental microwave. So the data that we had comes from the kind of data that gives us information about life and the kind of information about death the kind of isotopes that you'd expect and also dental matter which is quite interesting to compare to the carbon and nitrogen because they have different chronologies and the questions that we were asking we decided to try and see whether we could see any correlations so straightforward correlations between the two sets of data or whether we could see any groups or clusters within the data. So to summarise some very complex very detailed evidence at Bedro Vita the funerary data females and children tend to be agferwish more so than males we have this male association with polished stone and chip stone Bondylus is slightly more with women but it's sort of of different quality men have less bondylus shells but they tend to have more and bigger pieces great good to thought to increase with age but when I looked at it in detail they increased up to a certain point and then it began to drop off after the age of about 40 and then there's really very little correlations between body positions orientations and particular grade goods there are no sort of packages of grade goods that might pick people out in particular ways at nature it's a very similar picture as well so we have some very strong trends around males and polished stone everything else there's kind of hints here and there but nothing as a strong or statistically significant the spatial analysis similarly was quite complex previous work and identified separate groups that perhaps had different grade goods but I find it quite hard to believe some of the groupings for example why is this grade in this group and not in that group and if you move the groups around the pattern breaks down um so to summarise that the key variations really that we have are male and polished stone tools variation with age and very little distinct clustering in the grade goods we're not getting group sort of totems appearing as grade goods we're getting things that are much more subtle than that okay so to look at the isotope data very quickly because I'm running out of time um basically for the um isotope data the main picture from the carbon and nitrogen is how clustered it is so in comparison to other periods the neolithic lbk nitrogen carbon and nitrogen data is incredibly flustered people are eating very very similar diets and it's the same furniture as well very very well flustered these are the inputs for the strong team evidence um we find a lot more mobility at the early site of bedragita than we do at nitra so it seemingly this slightly earlier slide there's a lot more variation in where people are coming from than we find at nitra on the whole women seem to have more outside of the local rough local rough local rage than men do um and there's always one of all that turns up with low a low value that's harder to sort so turning to uh the work of sphagniff in the valley they looked at the dental wear which tells you about diet in the last few months of life they look at the striations and the different uh coarseness of the diets causes different patterns to be left generally they they concerns what we see in the carbon and nitrogen evidence which is that it's a mixed diet with meat for some individuals so some people seem to have had higher nitrogen values but it's not it's not consistently across the whole population um the diet is more abrasive when compared to modern examples so it's a lot coarser um overall older adults have more abrasive diets so perhaps they're eating more of um the plants or or meat uh men and women seem to be roughly eating the same diets which confirms the carbon nitrogen data um and there seems to be a slight change over time between bedragita and nitra perhaps indicating a shift to more meat in the diet in the later phases okay so um to kind of summarise a whole amount of analysis we tried everything that we could coro tried to put correlations between every different of the different packages that we could it's quite challenging to combine continuous and categorical data statistically um but there are a few patterns that that came out on the whole in this region males have higher nitrogen values than females suggesting that they were eating more meat um that's not something we see across the whole of the lbk it might just be particular to these particular sites um nitra there was a correlation between eating having a higher delta 15 nitrogen value so something that's come from the diet over the long term with dental wear showing higher meat intake so maybe diet we're seeing a very consistent diet is I think what this evidence is telling us so we're seeing people eat mostly eating similar things and that happening over the long term so men with polished stone axes were consistently local they weren't the people that were moving around I think in contrast to papers we saw earlier where perhaps the people who are from outside are considered to be the exotic ones here it seems to be the local people that have the biggest uh grave goods uh women more likely to move fitting into the patch alone calm models um men with higher um nitrogen 15 had full utlunus graves well kind of sort of um sometimes it's not statistically significant but it's a trend towards that kind of more diet more grave goods so in conclusion um I wonder whether because everyone's eating a very very similar diet those that do have a slight variation stand out more statistically so if you do have a slight variation from a very consistent population you're going to stand out more than someone who's slightly variable from a population that already has a lot of variability gender and age factors do seem to influence what's going on but only in particular ways not consistently across all of the data and this leads me to conclude that we're seeing moments of gender or age popping out in certain ways in the burial evidence it's not someone's complete identity which is grabbing into the grave it's happening in subtle ways and in partial ways not completely in the entire package so just to end I was going to I've run out of time but I was going to go back and pick up the idea of how we think about bioarchaeology along with um fury fury data and I guess what I wanted to say is I don't think we're seeing um a single package we have to kind of look for the threads and work out where they cross over and where they don't in interpreting the data um that we're not seeing in the bioarchaeology data necessarily identity we're seeing practice and we're seeing the ways in which people are related to each other and the kind of community engagement that people people have um and and so I guess where I'm going next with this research is therefore to think about the ways in which our bioarchaeology data tells us about connectedness and about kinship and how people were related rather than thinking about their life the individual life ways per se and I guess the next challenge therefore comes in thinking about how we can do that over refined timescales can we begin to look at these cemeteries with much more detail on the chronology to think about how these kinships were formed over time and how people passed things on to each other okay thank you very much for listening us


Course backup. Save and reuse your course. As a teacher it's useful to have a copy of our course that we can keep safe outside of Moodle or perhaps reuse it in a different Moodle site or give it to a colleague in a different Moodle site or even have another version of our course in the same Moodle site. Moodle calls this copy a backup and to make a backup of our course we need to go to the administration block and to click the link backup. This is how you would backup the course that you will have created for the teaching with Moodle course. When we click the link backup we need to follow the instructions to decide what exactly we want to bring over with our copy of our course and not. If you're an experienced Moodler you can jump to the final step if you know what you're doing. But for us beginners first of all note that there are Xs next to enrolled users and user information. That means that because we are teachers and for security reasons we can only backup the actual course content the resources and activities. We can't backup the information the data that our learners have included.  We basically want to include everything else activities, blocks, filters and so we scroll down and click next. On the next screen we're presented with a list of all of our activities and resources and we need to decide which ones we want to include. By default all of them are selected. So for example if I didn't want to include an item I could untick or uncheck that box and again we scroll down and once we've decided which activities we want to include and leave out we click the next button. Then we're prompted to check all of our settings. Our backup course has been given a file name which we can rename if we want to or leave it as it is and we scroll down to make sure that all of the settings and included activities are the ones we want. If we made a mistake we can simply go back to the previous screen and change them. Now it's time to click the button to perform the backup and we get a message that the backup file was successfully created and clicking the continue button will take us to where we can get a copy of the backup file to download it and save it offline and if we scroll down we see our user private backup area which tells us the course and the size of it and the time that we actually backed it up. So if you want to get this out of Moodle to say for example on a CD or a USB drive you click the download button. We aren't in this video or this course going to look at how you can then put your course into a separate Moodle site but if you look in the administration block again and you look at the link restore you simply reverse the process by clicking the restore link, uploading your file and following the screen instructions in that.


If you're watching this video, I know you want to buy PC parts and I know you want to buy them at a good price. Ang problema medyo hasel to determine kung ano ba yung good price. Dadan ka sa isang katutak na stores, tapos kailangan may historical data ka. Ko compare mo yung price ngayon sa price noon. Para malaman mo kung yung price ngayon, okay ba siya based on the historical price of the item? That's a lot of effort. That's a lot of research. In short, hasel. Makamagalala kasi ginawanan namin yung lahat for you. All of the research, all of the tracking, lahat nung hasel, we've done all of that. We've been tracking CPU and GPU prices here in the country since last year from the top four largest computer stores in the Philippines. So makiita talaga natin yung averages kung matas ba yung price yung ngayon, kung mababa, if now is a good time to buy. Lahat nung data na yun, lahat nung research na sa Google Sheet, link in the description below and anytime you want to buy a computer part, pwede mo sorry in yung sheet na yan kasi na-update rin namin kahit wala tung Price Watch episode which we do once a month. And this month in particular, something happened. Two things have happened that I've never seen before. Both good news, both good for prices, never pato nangyari which you're gonna find out after hear about the good prices from our sponsor. Pwede na lang, ay CD keyovers dot go. Wadalilo kong order. Search for the software you need. Add to cart. Daan ka sa payment options nila. Wala pang 5 minutes finished. May legit, working CD key ka na para sa windows mo. Gamit na nang aming code para makakakalap. Pwede na lang, ay CD keyovers dot go. Wadalilo kong order. Kikikad na para sa windows mo. Gamit na nang aming code para makakakuhapan ng discounts. Kaya kong nagahalap ka ng legit, mura, at original software. Check it out, CD keyovers dot go. Let's jump right into it, the two things that I've never seen before. Number one, 3060 belo 20,000 pesos. Yung 30 series medyo dumating nung pandemic, chip shortage, GPU apocalypse and the 3060 is the mid-range card that everybody is gunning for. Pero alam mo na Nvidia, siyempre mahal yung 3060, but this month is the first time that we've seen it belo 20,000. The average price is just slightly above 20, but there are stores selling it for belo 20, including hardware sugar. Thank you, Jesus. With all of the new 40 series cards coming out, bumaba ba na yung pressure ng 30 series? Usually, pagdumadating yung 40, the other 40 cards don't go down in value, but the 30 series does. At yung mas mahalaga pa dito, this is the 12GB version of the 3060. The 3060 comes in two flavors, 8GB of VRAM versus 12GB. Now, VRAM has been a trending topic lately kasi may nagsasabi na kulang na kulang na yung 8GB for most modern games, let alone future AAA games. 12GB VRAM na daw yung minimum na kailangan. Now, we've always tracked the prices of the 12GB version of the 3060, kasi tingin namin, mas value yung for money. Yes, mas murang anong kontay yung 8GB version, but bang for your buck, value for money. Yung 12GB talaga yung mas maganda. So we've been tracking 3060 prices since October last year, and those are all 12GB variants. First time namin makakita na belo 20K na siyang ngayon. So if you're in the market for a good mid-range to high-end card, depending on the relative specs of your PC, a brand new 3060 12GB belo 20K is pretty good. Yun nga, first time namin to makakita in our almost one year of tracking GPU and CPU prices dito sa atin. And the second big news for this episode, another one that I've never seen before, is that for all of the four stores, walang tumaas na GPU price. Latong changes dun sa GPU prices for all of the stores, pa-bababa. I never thought I would see the day. And we tracked a variety of GPUs from AMD, the 6000 and 7000 series, from NVIDIA, the 30 series and the 40 series. So sari-sari yung mga tiniting na natin na cards. But from all of the four major stores, walang price increase for any of the cards we tracked. It's the first time I've ever not seen red. Kasi kinacolor code natin yung data to make it easier to see the differences. If you see red, that means the price has gone up from the last time we checked. Kung blue, bumaba siya. Lahat nung GPU prices for this episode, either stayed the same or bumaba. So andami mong marilinig online, mababasa online na nag-reclama about GPU prices, pero alam namin ang tao online, Here we have multiple data points gathered since last year, indicating a clear price trend downwards. I wish the trend was the same in CPUs, although there was slight price increases notably from one store. So hindi to clean sweep sa GPUs lang bumaba lahat nung presyo. Although CPU prices are generally more stable than GPUs. So they're not as much of a concern. Yung GPU prices talaga na trauma na yung mga tao when they shot through the roof during the pandemic. Alright, so jumping into the actual sheet, this is what we were talking about the very first time I've seen all blue. So walang red. All of the price changes we tracked for GPUs were prices downwards. 3050, 60, kaya yung 4090 na high and bumaba. And this extends to AMD models. Although we are having difficulty finding data on some of the AMD models. Asy sobrang makhirap makahanap nung 6700 XT, 6750 XT reflecting the good prices and people are snapping up those GPUs when they come on the market. So it's hard to find stores actually na may stock nito. Even the XTX, the 7900 XTX, the fastest currently of AMD, only two of our four stores have it. Although it's interesting to note that yung pinaka-high-end the AMD around 66K, 67K, yung pinaka-high-end ni Nvidia, of course, is well above that. Yung 4090 mga 106, 107K. So very roughly you would get two of AMD's highest-end cards. Again, very roughly. Compared to just one of the high-end, pinaka-high-end the Nvidia. But gripes aside, yun, the best time, these are the best prices I've seen for GPUs. The only time we've ever seen it na walang price increase in any of the models that we've tracked. So I did mention that there are 3060s now for below 20K. So we've seen 119,740. This one is great, colorful, 18,500. We have talked about the colorful brand in the past na quality, reliable plus mura kasi hindi pa siya ganun kasi great deal on this 3060. Although the average price is still slightly above 20,000. If you take the aggregate price of the four stores, 20,135 yun price niya. And this is in stark contrast to the last data point we have from last month. The average price of the 3060 was close to 21,100. Ngayon close to 20,100. So a 1,000 pesos difference. Quite substantial. You can find it for even cheaper. Generally the good prices extend to CPUs. Actually galit nga ako dito kay Roy kasi sinira niya yung trend. Malapit na rin tayo makasweep dun sa CPUs na lahat ng price changes pa baba. Except ito si Roy who is actually well known for having very well priced items. Not a lot of high end items. Yung medyo mid range type. Tomas yung prices nila and the 5600G. Thanks Roy. Sobrang mura nung 5600G nila dati. And to be fair yung 5600X is just slightly more expensive. 25 pesos more expensive. Now you notice hindi tugmatong rose natin. 5600X on the last data point becomes the 7800X3D because that's another news point for price watch we have added the 7800X3D more high end CPU from AMD a bit rocky start with the flame outs involved with the motherboards but we have seen interest we have seen demand in fact they sold rather well with hardware sugar. So just as an added data point na and if you're looking for the best gaming performance usually the X3D series of AMD is very good value for money. So not multi core tasks but like single core applications like gaming really benefit from the added 3D architecture the added cash of the X3D series. So you can see that with the 5800X3D and the latest iteration AM5 platform 7800X3D. So we started to track the prices only two stores carry it and I'm happy to know that hardware sugar also stocks the 7800X3D our current price is below the average price. So your average price on the long stores is 28,000 is 28,177 our price is below that one. Just in case you're in the market for a 7800X3D. So mid year is actually looking like a good time to buy. Usually medyo dead air yung middle of the year because the new products have just launched. We're waiting for the new new products towards the end of the year. So not much action going on on the product release front but the prices are doing so well. So if you're in the market now for a GPU a brand new 3060 below 20K is really very attractive. The AMD cards, the 6000 series have really good prices if you can find them especially the upper end like the 6700XT. On the CPU front or on the whole rig front if you're thinking about jumping to a new platform whether that's Intel 13th gen or AMD's AM5 prices are looking good also. Price drops with the highest end 3900K all of the stores that have them. So we have 3 stores that stock the 3900K and they all had price drops. This week the average price of a 3900K is around 37K only a slight price drop from last month na 37500K. So we have summarized all of that data in this other tab itong summary. So you can see the summarized average data per model. So our newest entry the 7800X3D no data from before but starting from now we'll track it. Just a quick rundown 1650 no price change price drop in the 3050 price drop in the 3060 as mentioned price drop 4070 price drop 4080 2000 peso average and also a 2000 peso price drop average for the 4090. Big price drop for the 6500XT 1210 I really can't find any or at least the 4 stores that we track don't have any 6700XTs the first time actually that I can't present any data we've been tracking this model since week 4 so that was last year mga November may data na tayo noon but this month June 2023 wala nang mahanap slight price increase actually in the 7900XT price drop for the 7950X price the same for the 7600X the other 7000 CPUs are not selling that well slight price drop for the 5600X the 5000 series of AMD really has staying power sobrang in demand pa nila slight price increase for the 5600G and a very good price decrease this is really sort of the older models but still good if you're looking for bank for your buck very big price drop 1,000 peso price drop for the Ryzen 53600 slight price drop for the 3900K slight price drop for the 1300K and a not in substantial so are of 1,000 peso price drop for the 12500 overall price drops pretty much across the board looking for a new system looking for a new CPU or GPU now is a great time to buy and as always you can check out this sheet online 24-7 link in the description below you can check it even if we don't have a price watch episode kasi ginagawa lang namin to once a month but this sheet we update or we try to update regularly so you can see the price trends in real time and if you've been waiting now might be time to pull that trigger thanks for watching full service PC store ang hardware sugar nagbabenta kami ng PC components nagbabenta rin kami ng fully assembled rigs we clean computers kasama na rin yung excellent cable management namin and CPU cooler repasting sa cleaning we also clean and repaste GPUs na sa makati yung physical store namin and you can also buy from our site www.hwsugar.ph na 100% palaging up to date yung inventory doon kung instock yung item sa amin available yung sa site we also ship nationwide thanks for watching and maybe one of these days magkita tayo sa shop