What's going on guys? I got Kyle and Sean here, Lane Sportsguards, and we are losing it. We got two boxes of 2023-2024, bum and chrome you, basketball. Rich is losing it. We're putting ice- we're putting ice- Just microwave your milk, and add it to your ice cream, then microwave that. We're microwaving ice cream and milk. Then you have just a puddle. Yeah, that's how Sam gets his milk. He just microwaves his ice cream until it turns into milk. That's how he drinks all his milk. I will tell you the frozen milk ice cube is not a bad, bad idea. Yeah, that's not a bad idea, Lane. I don't mind milk. Does milk freeze perfectly like that? I assume so. It's not Jack Daniels. We get a few- or we get all our packs out here. Yeah, Sam was- I think the most he did Sam got yesterday was when me and a couple other people came into Chad and were mentioning we didn't like our ice cream melted or boiled. Oh, now I know what they're doing. He's like, you don't boil it. It's only 10 seconds. He's screaming. I'm sure you do as well. Yeah. You don't boil it. You just have diabetes milk and chug it. That's what I'm talking about. I mean, I'm like, I'll give that a shot. Yeah. That's good for sugar. It's ridiculous. Yeah, there's some questionable, uh, questionable stances last night. Questionable stances last night. Sam was dying on the hill. Hand in hooker questionable for a tooth injury. It's really tired.  I think I've ever seen that. It does run hot. It does run hot. It's a bowl of some ice cream pretty quick. To be honest, maybe this is the real jumbo then. I don't believe it anymore. I don't know who's real or not anymore. I don't know who's real or who's not anymore. Yeah, it does run hot. Will the real jumbo please stand up? What's up, Damien? How are we doing today, sir? Damien, what's happening? What is going on? What's going on, dude? First Refractor is Reed Shepard, freshman for Kentucky. Caleb Love. X-Fractor first to Von Castle for UConn. We're finding him everywhere. And it's $2.99, Taren Shaden. First there for fighting a line-eye. There's Brawny, X-Fractor Brazil. Justin Edwards and Max Abness. The four horsemen of microwaving. Does run hot. There's a Refractor first, Omaha Balu. There's Isaiah Collier. $2.99, got L. Ellis on the green for Arkansas. And purple to $3.99, it's going to be Cormac Ryan. FSU almost took him down yesterday at UNC. I put Sean at quarterback. There's Tyrese Proctor, Sharpshooters. I trust him at quarterback. McNeely, with that roster, come on. Sean could get it done. Sean could get it done with that roster. For darts. Refractor first is Isaac McNeely. Brawny on the insert. Jumbo Diaz, Battle Royale. All the Jumbos to play a game of Madden are something to decide who's real. There's Ashton Hardaway. Penny's Sunday, $2.99. The Little Penny. There's a Max Abness. Purple Shimmer. Tyrese Proctor, Deleon. Madden's tournament to decide who's Jumbo. There's a Daren Green Refractor first. Justin Edwards. And Otto is going to be Armando Baker. That one does have a dent in that. I'm going to have to send a box with this one. Chai Thiwot. He does have a dent up there next to his head. It's Armando Baker on the Otto. Not the best Otto I've seen recently either. Someone said he said like flatlining. It's an EKG. A Fortnite tournament. Oh boy. Oh boy. Fortnite tournament to decide who is Jumbo. No, his Otto's pretty bad. It's pretty bad. So $3.99 we got Javante Taylor. Sharp Shooters Wagner and Haley Van Liff on the first box. Go with the box too. Good luck here. Yeah, he just does a little thing in the front. Straight line across and then like into the canary. Say real life battle royale. So we're saying a rocket league. Life. Real life. Go out to the paintball fields or something. Bare Knuckle. Or bare knuckle. Bare knuckle. Or we could bare knuckle. Two men in there, one man leaves. Oh boy. Let's go. Real life. Oh boy. I'm in it. All right Jumbos. Meet by the power poles three o'clock after school. Let's go. Come on Jumbos. You're all going to be there. See you then. It's like Mr. Smith in the Matrix. It really is. Anyone could be Jumbo. Anyone could be Jumbo. Gotta keep your head out of swivel here. I think we got, we got some ties I think. I think we'd have a couple ties. Good boy. Everyone would lose. We could have the Jumbo Diaz. 40 yard dash and just see which one doesn't fall. This Diaz is real by that. Yeah, we'll know. Every five minutes I hear Jumbo. Yep. Yep. That's unfortunately might not change for a little bit, Joe. We got, we got some situations going on. Yeah, we got a Jumbo situation. We got, we got multi Jumbos. Jordan Dingle refractor. Yeah, Mackenzie and Bakko. Extractor first night's Brani. Brani James. Ooh, nice. Bakko Huna insert auto is going to be Zach Eadie. Nice one there, the big man out of Purdue. Two 150 on the insert auto. Very nice, Scott. The two 99 RJ Jones. Extractor, Risa Sheik. There's a Brani insert. I don't know if we can do a 40 yard dash because he didn't even make it 40 yards. True. True. Top of the 20. Refractor first, Evan Royal. Caleb Love. Whoa, whoa, chill, Tyler. It's a three 99 Kevin McCuller. Chill, Tyler. David Wilter on the Sharpshooter. I'm still trying to get over this. Tyler's coming for him. Tyler's coming for him. Love it. There's an orange coming. Mookie Cook. Refractor Spencer Jones. Isaiah Collier insert. Orange coming. Extractor first to Sean Harris Smith. Tyler, that I believe in though. And Orange Wave is going to be the 25 Cormac Ryan. Former Notre Dame player for UNC. Take Justin Fields right now. Josh Zimmer, Darren Green. You can have two. You can have two. Oh, give me Fields right now. No love for two anymore. No, I'm done. God, it's a 199. Another 80 for Purdue. Poplar on the refractor. It's a Brani insert. I saw that, Lane. I meant to text you back this morning. What did you go with? Did you really? Trey Norman. He texted me at like, it was like 2 a.m. or something. He's like, I just won this. What should I pick? It was like a seafood basket, a wagyu basket, a dry-aged basket. I was like, what the heck, man? 199. We got Trey Norman from Marquette. And Cameron Brayton. Nice one there. 199 on the blue. You know what? I would take him too, Tyler. At least he can call Turner. And he can sling it. Righty there, Chyothiwok. That'll do it for your two boxes, Bowman Chrome Basketball. Appreciate you as always, my friend. We'll get that right out to you.


everybody forced here ripping 2022 flawless collegiate football hobby one box number 27 left side serial numbers let's go ahead and do our random and then we'll just roll right right into the box opening here thanks everyone for joining good luck randomized four times names four times and the numbers four times we'll put them side by side to see who's got what one two three four Matthew L down to Jennifer J four times on the numbers one two three and four twelve down to four all right Matthew L 12 Jamie P one Ronald B 10 Marshall S eight William S 25 Andrew M 5 Keith B 7 Nick C 22 Stephen L 9 Craig R 20 Dale C 11 Craig R 21 Matthew L 16 Stephen L 14 Joseph H 18 Stephen W 23 DW 13 Nick C 17 Brandon J 3 John M 6 Andrew M 2 Tom H 15 Craig R 24 Todd C 19 and Jennifer J with four no not right now Blake probably not going to beat tonight all righty there's your Lincoln chat I'll also call out the hits as we go along as far as who's who's who's yeah Blake I don't know if I'm looking for that got kind of trouble tonight all right triple patch Jamar Chase Justin Jefferson Odell Beckham Jr 10 of 20 10 spot Ronald B nice trio there legacy patches Kway Walker rookie patch 8 of 25 8 spot Marshall S patch card Kyle Hamilton 7 out of 10 with a very nice under name patch there 7 spot Keith B logo signatures Tyler Van Dyke 13 out of 20 13 spot DW rookie uh auto here 9 to 15 Felipe Franks 9 spot Stephen L it's from 2021 Felipe Franks got an RPA of Kennedy Brooks 2 out of 5 2 spot Andrew M 2 color sooner patch there I don't think they have anything other than 2 color at Oklahoma unless you get a logo the non over there patch auto Carson Palmer 3 of 10 3 spot Brandon J game worn Carson Palmer that's pretty cool star swatch signatures game worn USC patch nice hit Brandon dual jersey auto bow Jackson 9 of 25 9 spot Stephen L player worn on the bow that's awesome Auburn Tiger bow Jackson right there sapphire gem Mike Evans 18 out of 20 18 spots Joseph H thanks for joining Brandon of course man it's a tough call right here tough call but I'll go here first jumbo diamond one of one Justin Fields sick jumbo diamond Jamie P with the one spot very nice second year fields collegiate good stuff this is a good box it's a very good box there's a diamond guarantee there and ruby gem rookie auto Kenny Pickett 20 of 20 that's Craig with the 20 spots there you go awesome KP Kenny 2 gloves that was a very good box thank you everybody for joining the break congrats on those who hit you're on to one more of these the second of the case


I've been involved, as has Ron obviously, but in this programme from the start over the last five years, it's been a significant part of my life in NERC, but this day really served as a reminder to me of just how much has been undertaken within this programme since it was launched and again how many people have been involved in the delivery of all of those various funded activities. So for the majority of today we've been talking about the demonstrators, but there are also a large cast of actors associated with the feasibility studies, two waves of feasibility studies that were carried out and obviously with the mini demonstrators as well. So, you know, we may just happen to be sort of front of house talking about this stuff, but we're part of a much larger cast and community that have been delivering and building what will be a significant legacy, I think, and that's already embedded within a lot of NERC ways of working that will be carried, will be carried forward. So, again, making these sort of events happen take an awful lot of work. I've been acutely conscious of the fact that I handed a difficult task to the champions and their colleagues at Cranfield in the sense we had a very big event at the British Antarctic Survey only a matter of a month or two ago. And with August in the middle we had to do it all over again but with a much more applied focus at the Royal Society in early September.  So the last thing I'd like to say, and Steve, I think it's your turn to say something next, is we're having a small reception immediately after this, which will involve champagne, I've been told. So if people would like to remain and mingle then please do so.


When culture tries to pressure you into believing and preaching according to how they believe, you hold fast on truth. Do not be deceived.     Just smile and say, Yes, I am narrow-minded because Jesus said, Narrow is the way.


I think we all knew that this was going to happen, didn't we? Yeah, it says that Avengers Age of Ultron hits one billion at the box office. Disney and Marvel Studios, Avengers Age of Ultron jumped the one billion mark at the global box office on Friday, becoming the third Marvel title to do so after the Avengers and Iron Man 3, really, okay? Well, new Avengers did, I didn't know Iron Man 3 did. And it's the eighth Disney release to surpass this threshold. Through Thursday, the movie's global total was 990 million including the massive three-day China total of 69 million and a domestic total of 333 million. In North America, Josh Whedon's Age of Ultron is on the verge of overtaking various seven to become the top-grossing release of the year to date. Age of Ultron becomes the 21st movie to gross one billion or more at the worldwide box office. The first Avengers also directed by Whedon is number three on the coveted list and Iron Man 3 is number seven. Okay, the superhero mashup still has a ways to go in terms of catching up with Universal's Furious 7 which has earned 1.47 billion since April 15th, 2015 release. I didn't know that, making it the top performer of the year to date. Other territory totals include South Korea 76 million, UK 63 million, Mexico 41 million, Brazil 39 million and Russia 32 million. The Rocks probably sitting there like, please don't let it happen.  But I wonder if they're kind of, I wonder if him and Ben is kind of like, I don't want it to done.  It's hard to overtake a superhero movie. What do you think is gonna be the next movie to do 1 billion like that? I did not know Furious 7 did 1 billion. It's gonna be in a pretty drastic world. It's gonna be one of the blockbusters coming out. But those comments down below let me know what you think, all right? One million subscribers. Woo!


Thank you very much. Thank you all very much. And I bring you apologies from Nancy. It seems that she has a schedule too. As a matter of fact that was one thing I didn't know about this job and I should have anticipated it after being governor that there is somebody that tells us what we're going to be doing every 15 minutes all day long. It's a little bit like Irving Berlin and that World War I song about the bugler when I find out who's doing that. But I've been looking forward to meeting with you today welcoming you here to the White House. You know around the turn of the century there was an English gentleman Samuel Butler who remarked that the advantage of doing one's praising of oneself is that I can lay it on so thick and in exactly the right places. Well I did come to give generous praise but not to myself or anyone in our administration although I must say for them I'm very proud of them and of the work that they've been doing. But I came to pay tribute to you the new superstars of American grassroots politics. You represent across section of Americans from all backgrounds different regions across this country. In fact you remind me very much of that one special interest group that I mentioned on inauguration day. The one special interest group that has been neglected and needs help that is we the people. Thanks to your efforts in supporting the Graham Latta spending resolution you gave all of the people of this country I think a tremendous victory. Not a victory for one party but for more bipartisan cooperation and less inflation and for a government that will finally begin living within its means. The bottom line is that millions of Americans are renewing their faith in our political system and all of those people who've waited so long for someone in Washington to listen and care and take action. So from all of them to all of you simply thank you for what you've done for your country. Now before you start feeling too good let me warn you you're not going to you're not going to get out of here scot-free. We need we need your help with another vital bipartisan piece of legislation now you have no idea what I'm talking about. We absolutely must have across the board a three year 25 percent rate reduction for every American taxpayer and we're not going to settle for less. Now there are those who are insisting that we settle for less. They demand proof in advance that what we propose will work and they refuse to accept the record of history which clearly demonstrates that tax rate reductions do work. I think we can prove that what they've been doing in the last few key decades hasn't worked and never will and I don't think it automatically follows that those who brought about the present economic mess are simply by virtue of having done that the best qualified to clean it up. They never answer one question that I keep asking and you've probably heard me ask it. Why is it inflationary if the people keep their own money and spend it the way they want to and it's not inflationary if the government takes it and spends it the way it wants to. So we're asked to believe that their proposal is more compassionate to the working people but their proposal won't even match the built-in tax increase that they themselves are responsible for. As a matter of fact let's be honest about it our 25 percent across the board tax cut actually is not that much of a tax cut at all. It is simply removing the imposition over the next three years by way of inflation which moves people up in brackets and built in tax increases of the tax increase that will be imposed on the people without that across the board reduction and theirs doesn't match that. The people over the next three years will get a tax increase unless this is adopted. Now it's that simple. Our opponents want more money from your family budget so they can spend it on the federal budget and make it remain high. Maybe it's time that you and millions like you remind them of a few simple facts. It's your money not theirs. You earned it. They didn't. You have a right to keep a bigger share than you've been allowed to keep for a great many years now. When they insist we can't reduce taxes and spending and balance the budget to one six word answer will do. Yes we can and yes we will. For too long the government has stood in the way of taking more of what our people earn no matter how hard they try. It's almost become economics without a soul and that's why the ultimate goal in everything that we're trying to do is to give this economy back to the American people.  Some say the future is looking impossible filled with danger and uncertainty and scarcity. Well the futures always look bleak till people with brains and faith and courage who dreamed and dared to take great risks found a way to make it better. People like Robert Fulton and Eli Whitney and Thomas Edison and Henry Ford and Albert Einstein. Einstein himself gave us the formula everything that is really great and inspiring was created by individuals who labor in freedom. So our message today is simply help us pass these incentives so we can help you. We want nothing more than to turn you loose so you can make the 1980s the most exciting successful decade our nation has ever known. Years of renaissance for American entrepreneurs. Years when millions of free men and women went out and found the energy to make us secure and created a revolutionary technological breakthrough on every front. Years when they rebuilt our cities and in the process created millions of new jobs stronger families and a real hope for young Americans everywhere. That's the kind of country I want to leave for our children and I know it's what you want to. Now if we keep working together just like we have we're going to succeed and save that American dream that we all love.


Hey guys, this is Mary Lee Johnson here with Alex RSD and you just did your first 21 convention speech. How did you find the 21 convention? Cool, you know, yeah, a lot of really committed self-development guys. We're going to make the most of their lives, yeah. You were so deep on that. I actually meant how did you find it? How did you get involved? Oh, right, right. How did I find the experience? How did I discover it? I said you went deeper, though. Yeah. I was invited to speak at a convention in Melbourne, I think, two or three years ago. But I was abroad at the time. And I kept it in mind because my schedule is moving around quite a lot. We have a very transient schedule. And I've realized that I'd be in Miami when this was in Tampa. And I was like, okay, let's get in there. Let's go and say hi. So describe to me during your speech value as it pertains to your speech. Value. Yeah, right. So the speech was mostly about how every guy is of value and doesn't need to worry about being high value and showing off his value, how he's special. And he doesn't need to worry about being low value and avoiding being low value and being defensive and kind of clammed up because of that. But rather that we go by this ideology that there's no reason why that you as a guy, there's no reason why you're not enough. And that reveals that you're of value, that you're a valid guy that reveals over a function of time, not that you have to demonstrate it and kind of shove it down people's throats and convince them of that. Because that's really, it appears to a girl as compensating for a lack of value or a lack of confidence or being kind of like overconfident and kind of macho or arrogant or something. So it's probably about being lighthearted, making yourself approachable, making yourself easy to talk to, not taking things too seriously and not being too formal. And it's actually really implementable. Really easily straight away for guys wanting to learn how to go out and meet more girls from cold approach in their life. And then you said that girls are most relaxed or girls are most sexy when they're relaxed. And I'd like you to explain that. It's kind of hard to be sometimes relaxed if you're in a dress and in high heels and in a bar. So how do you help facilitate that? Right, right. So in a bar, yeah, absolutely. Guys don't realize that girls are in so much pain when they're in high heels. It's incredible. Like we never stop and think and realize that it's easy for a girl to move in high heels and to stand still. That's what it is. Running around in circles and dancing because that's a lot easier than stopping and talking. But yeah, so what I spoke about was that it's a lot easier. Sorry, a girl feels most sexy when she's relaxed. And if you can be the guy who doesn't put her under pressure and doesn't have her freaking out and feeling distressed and worried that you're trying to pick her up and her avoiding your advances or worrying about what your advances mean and all that stuff, then she can just, it's more easy going for the girl and you're the guy who she can be comfortable going home with and that's where more intimate sexy things can happen. So what I am picturing for a guy when he goes out is that he can be light-hearted, easy going, chitchat, know how to do the physicality, know how to be appreciative, know how to use negative expressions in a tasteful way and be the guy who a girl will walk home with or after-party with or you want to be the kind of guy who the girl's like, you know, I just want to go home with you, keep my heels off, wear track pants, eat Nutella and relax. And when the girl kicks her dress off, the lingerie, and the girl really likes it when a guy can create a comfortable, relaxed situation where she can let loose, where she can be sexually confident because girls love somebody who buys into it and really appreciates her on that level. Not for the sex, but for her being sexually confident because it's a beautiful thing. Gosh, you are so high energy and I hear that you work all the time. How do you keep that up? Can I buy whatever it is that you are using to keep that energy so high or is it just that you love your work? How do I keep it up? I'm not high energy. Actually, when I'm hanging out on my own, I'm very chill, very passive and I like to be introverted in trying to observe people because I'm too high energy like this away from work and away from seminars, it can be quite overwhelming to people. So I like to be more appreciative and empathetic, but this high energy that you talk about here and in the seminar, it comes from the desire to influence. Sorry, if I could have a big influence and there's people waiting to learn more and expand their reality and open their minds and stuff like that, that definitely gives you a lot of energy and you can use that to present seminars and do interviews like this. So that's where the motivation and the high energy comes from is the sense of being able to influence people. Well, thank you so much for coming here for being part of this and hopefully we'll see you again. Hopefully you will be a reoccurring thing at the 21 Convention. This has been Maryleigh Johnson with Alexander and we're here at the 21 Convention 2014. Bye, guys.


This is stock number 9300. We are at Summit Automotive in Fond du Lac, Wisconsin and we are checking out perhaps one of the cleanest 2013 Chevy Silverado 2500 extended cab shortbox we have ever had on the lot. This truck has a 6.6 liter Duramax diesel with the LML motor. Which gives you 397 horsepower. Greystone metallic is the color. From this HD video you'll be able to tell that this truck is just about perfect all the way around. Has the 20 inch polished aluminum rims. No scuffs, no scrapes, no corrosion. Has Goodyear Wrangler LT26560R20 tires with just about I'd say 80 to 90 percent of the tread left. Frame and underbody is very very clean. This is how this truck came in. Didn't see a single dent or ding on the front fender or anywhere for that matter on this truck. We shoot all of our videos in 1080p so if you have HD capabilities on your computer tablet or a smartphone device turn them on now because it's like you're right here looking at the truck with me. Passenger side rim, no scuffs, no scrapes, no corrosion absolutely perfect and as you go down this side of the truck you can see just how clean the body is, how reflective and glass like that paint is. We take these HD videos so if you are far away or even if you're close behind you just cannot make the trip down but you're still interested in purchasing the truck. You can still see the truck, hear the truck and have confidence in the vehicle that you're looking at before you even get here. We sell a lot of these trucks out of state so you can have the confidence that you're dealing with the right people and the right truck.  You can see that the frame and underbody is like new underneath. Very little to absolutely no corrosion, has all the remaining factory exhaust so it hasn't been modified in any way and all the lower rockers look really good. Factory chrome step bars that are in absolutely perfect condition as we go around to the back here. Rear bumper absolutely perfect, no dents, no dings, it does have the full towing package which includes the receiver hitch and seven pin wiring. Tailgate is in excellent shape, I didn't see a single dent or ding on that. Has the backup camera and the locking tailgate, easy lift assist, has a spray on bed liner or rhino liner, fifth wheel bed rails and of course the seven pin wiring inside for that fifth wheel. Also has a Truxedo soft roll-up tonneau cover that's in absolutely perfect condition, no rips or tears on that and as we go down this side of the truck just as clean as the passenger side. No dents, no dings, no scuffs, no scrapes. Absolutely one of the cleanest Duramaxes I have seen on our lot. Usually these trucks are used really hard for work and they get dings and they get scuffs and that's just what happens but this one not the case. Inside you get the LT1 package, there are no rips, there are no tears on these seats, this truck has never been smoked in, has a power driver seat factory, all other floor mats throughout, carpeting is like new underneath, it does have the telescopic tow mirrors with the built-in directional signals, power windows, power locks, power mirrors, the factory brake controller, auto headlamps and as we take a look inside one of the reasons this truck is so clean is it only has 13,389 miles on the leather wrapped steering wheel, no scuffs or scrapes on that, has bluetooth audio controls, cruise controls, steering wheels in excellent shape, the six speed allison transmission, turn dial four wheel drive, dual climate control, power pedals, backup sensors, stability track and the factory brake exhaust brake. CD player, so you get AM, FM and serious XM radio capabilities, up here you have your on-star as well as the backup camera and that little button right there is signifying that this truck has the snow plow prep package, not that I think a snow plow has ever been on this truck but it does have that package, passenger seat is very clean as well, no rips or tears and we got a factory all weather format on that side and you got the two glove boxes, also right here is a USB jack and then you have an aux jack but you can also play music with your bluetooth phone I believe on this truck as well, take a look at the back seats, just as clean as the front seats, no rips or tears back here, it has the latch child safety system, rear defrost, fixed glass window, these seats do fold up for extra storage and you can see we got the all weather format back here, carpeting is like new all the way through and one thing I like about these with this new body style that Chevy came out with they got the doors that open up all the way, also you have the power window back here which is a pretty neat feature that shuts just like that, we'll take a quick look under the hood, also these mirrors they fold in like so and they also telescope out so you can use them for your trailer, under the hood we have the 6.6 liter Duramax diesel with the LML motor, engine bay is very very clean runs very smooth this truck has been fully safety to and inspected by our service shop has a fresh oil and filter change all the fluids have been checked and topped off and the truck has been gone through mechanically 100 and this truck is 100 ready to go if you want to get your hands on one of the cleanest 13 extended cab Duramax is out there this is the one go check out more pictures at our website www.summitauto.com full pictures and descriptions of every single vehicle on our lot videos of every single used vehicle that we have all at summitauto.com thank you so much for checking out the video if you want to make this truck ears which I highly recommend give us a call right now<PHONE_NUMBER> ask for one of our sales associates to make this truck ears today once again that number is<PHONE_NUMBER> thanks again for checking out the video if you want to check out more go to youtube.com slash summit auto remember to like subscribe and share on all those videos including this one and in fact in a second you'll see a link to subscribe to our youtube channel on your left a link to more heavy duty truck videos like this one on your right if you haven't been to our website on the bottom link to our website to this truck go check it out go check us out and we really look forward to helping with this super super clean 2013 Chevy Silverado 2500 extended cab short box in fact that number again is<PHONE_NUMBER> call now thanks again


What's going on everyone? Sam here with Leighton Sportsguards. We've been 22, 23, Penini, Spectre, Mosaic, Basketball, Hobby, 4Box, Mixed, No. 9, Random, Team Style. Look again at your random results. As always, you can find those on the website, under the results tab, as well as a link in the chat. So, good luck. Here we go. Tracy, pick up the nuggets in 22, down 2, 3 to go, and Felix, taking two spots and Flawless. The first Flawless, we are down to 10 to go, everybody. Let me drop you a link for that really quick. Flawless, uh, Flawless, Basketball again, the last one of the year. 10 to go, Flawless, NBA, NBA. Alright, let's start with, uh, Mosaic here. We have a lot of it left, so it's like, they've just been in Mixers. Brett, what up buddy? How you doing buddy? Happy Saturday, man. Happy College Football Saturday. You wanna see what the, uh, what's the Miami UNC game at right now? Cause that might be more worth watching than this, at least from the time being. She'll lay at Oregon State, and then we'll just kinda scroll through the base there. Uh, Joarri Walker, Josh Bernat, Mike Conley, Red DeAndre Aiton base, and a purple Trey Young to 99. Yeah, that might be the way to go for a bit. Yeah, we'll have to keep an eye out on that one too. And the baller, Silver, Benedict Matheran, Blue to 199, Zion, Notoriety of Steph, and Oshaik Baji, Mosaic, Notoriety. Straight on sharp.  Josh Green, Red Miles Turner, and Montage of Mr. Luka Donchich. Oshaik Baji, Mark Williams, Silver Franz, purple, Grant Williams to 99. Shade on, and John Morant breakaway. My gig was canceled, now I get to watch. Uh, we did select earlier, Brett. We did select earlier. Um, we had a full case, uh, divisional. Ty Ty, Keegan-Marie Rookie. Are you in a band? That's kinda dope, actually. Andrew Nemhart.  That's pretty sweet. Base, there's Jaylin Durin, Noora, Seth Curry Red, and Jayden Ivy. Yeah, 14-14 on that one. John Keepus updated on USC, er, on, uh, Notre Dame if we have to go back to it, but we'll keep it on this for a minute. Leslie Wendell Moore, Buddy Heel, Jaylin Durin, and Overdrive, Luka. Obli, Vince Williams, Silver Kawaii, Red Alphan, Shengun, Dame Dala, and Jason Taylor. Oh, that's sick, dude. Full-time musician. This one was a solo, solo acoustic.  That's awesome. That's awesome. Full-time musician, dude.  Hell yeah, man. Jaylin Williams, Ty Ty, Kelly Olenek to 149. James Sardin, and Anthony, they say, good ol', good ol' rock. So I can't, it's kinda for tricks. Bryce McGowan, Malachi Brand, I'm a nice one there for the Spurs on the Rookie Pick the Graphs Auto. Congrats, Spurs. Hey, thank you there, Saint, picking up a spot in Flawless. We're down to nine in the first one, everybody. Nine to go. Dame Dala, Andrew Wiggins, 299, and Paul George. Mosaic. Second box, oh, Mosaic. Now, we're having a Tate Martell, everybody. Where'd that guy go? Silver Aaron Gordon, Jaylin Brunson. Where is he?  Oh, my God, he was the worst, dude. Tate Martell. UNLV, huh. Cash. Well, he was supposed to be the next big thing, and he just never said an attitude. No, he did have a really bad attitude about everything.  He was, I think, his issue is he was like the, yeah, OSU. I remember, OSU, I remember him going to UNLV. I didn't know he was still there. He was in Silver Portis. Jaylin Green, blue, 2199, Keegan Murray, straight fire, and Lucca Donchich. He's now an entrepreneur. Entrepreneur with a babe sister. Yeah, Fields took his spot. God, that was so long ago, dude.  Walker Kessler. Silver Tyrese Martin. He was referred to 149, so ridiculous, dude. Bass and Zion Williamson. God, man. Thank you there, Paul, picking up the Jets and Patriots in number 16 of Gold Standard. Appreciate that, buddy. He was kind of the, in my opinion, a shot chucker. He was kind of the NFL equivalent of a shot chucker. Because, yeah, he was kind of like, he just like, he has such good highlights and like, he was just raw, but he didn't like care. You know what I mean? Yeah, exactly. He was like, he's an NFL shot chucker. There's Jayden Hardy. He's on his 10th year of college.  Are you kidding me, dude? How was that possible, but still a freshman? But still, I think that's just a joke, but that is kind of valid. Andrew with the Rams, down to 9 and NT.  Thank you, Glenn. Yeah, that's unbelievable, dude. Tyrese Martin, chat home. Are you actually serious about that? Pink James Wiseman at 149. It feels like that, though, to be fair. What's I'm saying? Hello, Rookie. Giannis, Red Chet, J-Dub, and D-Book. No, it does feel like it. It does feel like it, dude. Well, I mean, if he was still there when Fields was there, I mean, that Fields came out in 21. Keegan Murray. Silver Moody. Yeah, that's nuts, dude. Gold. First off, we have an auto of Bogdan Bogdanovich going to the Hawks. Boggy, baby. Let's go. What do we call him, the Croatian Sniper? I think is what his nickname is. Contamia's called Wellpope. Gold, Darius Garland. Nice one there. Cavs to 10. Five of 10. He is a stud, man. He is a stud. Palo and Lebron as well. Let me just auto him to gold now. Yeah, that's fine. So, Spectra now. Good music, but it seriously depends on the...anywhere. From dives to country clubs to venues, and they only want originals. That's sick, man. That's awesome, Brett. I'm still trying to keep my style. Guitar, yeah. That's fun, man. It is fun. It's very fun. That was one of my favorite classes, the high school piano. $2.99 is Max Drew. Yeah, it took two years of it, actually. Yeah, I played the piano for two years as well. Oh, yeah? I hated practicing it. It was... $49, Oshaik Baji. Nice one. Jazz on the RPI. That's on that. With his rich parents. Yeah, fair. Something like Josh Rosen. Seated to never start again.  He was one of those guys who never had the incentive to work hard because of his family.  There's Jalen Williams and Clay Thompson. Ooh, is that a marble? I think that is a marble. To five. Marjon Bochamp. Going to the box. Michael S. Congrats, buddy. Nice low-numbered Bochamp there. Again, nine to go on Flawless now. Nine to go on NT as well. Alan Iverson, $2.99. Levine and a Drew Holliday. Nice one here too. $49, Glenn Rice, Hornet and Heat. Charlotte is Jay Taylor and the Miami Heat is... Kenny K, so that'll be a random to those two teams right there. Heat and Hornet. I know, I'm kind of surprised he never started at Miami. $2.149, Ryan Rollins. Who is starting over him? Who is starting over him at Miami? Orlando Robinson and Cade Cunningham. Nice one here. Another RPA to $49, Jalen Duren. Congrats, Pistons. There you go Detroit. Nam Q. Good stuff, Nam Q. Very nice. Very nice. Thank you, Dylan. Kyle Kuzma to $149, Celestial. Thanks, sir. Got Orlando Robinson and Cade Cunningham. And then Max Christie to $25 on the aspiring Pink Rookie Auto. Nice one there for the Lakers. Congrats, LA. Max Christie. Ooh, nice.  Going to the Heat. Devontae Graham and Alfred Shingu. This is dope. $2.99, Adrian Dantley. Catalyst auto for the Jazz. So good. What's underrated players in NBA history? Adrian Dantley for the Jazz. Nice one there. Congrats on that, Utah. Jake LaRabbiah to $1.99. Got Rookie of Josh Monats, Paris Halliburton and $2.99. Kevin Harris, Rookie Auto for the Magic. There you go Orlando. Going to Alex. And last pack here. I think it's a redemption. I'm not mistaken. Tis a redemption. Darren Fox to $2.99. I need that Glen Rice back, by the way. Just because it's a random. Levine. There you go, yeah. And Drew Holliday. And then redemption is Christian Brown. Rookie jersey, autographs, supernova. Nice one there. Congrats on that. Nuggets. Going to Denver. Here you go, Denver. Shipe. What is the supernova number? $2.49. I think that's the 49, if I'm not mistaken. Rookie jersey. No, the 75. 75 on the supernova. Nice one there. No, no. You got three spots, right, Shipe? Nice one there. Am I an idiot? Did I miss? Shipe, did you have three spots on this? Please, God, tell me you did. No, yeah, yeah, you're good. You got three spots. And I didn't say Shipe for the first one, accidentally, because I didn't register. But yeah, let's do this random real quick. Yeah, yeah, no, no, you're good. Yeah, you got a third.  That would have sucked. Glen Rice, we've got Hornets. Heat. No, no, you're good, Shipe. No worries at all, man. Get it real nice. Whatever team's on top. After eight times, we'll get the Glen Rice auto. Hornets and Heat. One, two, three, four, five, six, seven, and eight. Going to Heat. Heat's going to get the Glen Rice. Going to Heat. So again, we got Glen Rice on the auto. Gold, Garland to 10. Christian Brown, Bogdan McDonovich, Malachi Branum, Adrian Dantley, Kevin Harris, Marjon Bochamp, Oshai Bajie, Jalen Duren, and Max Christie. That will do it for the break, everybody. Thanks again for the fill. Up next, Contender's Optic.


The study aimed to optimize the chitosan nanoparticles, CSNPs, to enhance the topical delivery and therapeutic potential of alpha-arbutin, alpha-arbutin, a skin whitening agent used for treating melasma. The study found that the pH of chitosan and tripolyphosphate, TPP, solutions affects the particle size, polydispersity index, and Zeta potential of the CSNPs. Additionally, the study showed that the particle size of CSNPs decreases when the pH of chitosan and TPP solutions is adjusted. Furthermore, the study demonstrated that the loading efficiency of alpha-arbutin into CSNPs is greater than that of TPP. This article was authored by Shima Hatem, Nada El-Hafi, Ria Melezebi, and others.


standing show here every trading day at 10 a.m. Eastern standard time also it's a great newsletter the opening call now It's very easy to get the opening call folks come over to our website at TFNN You're going to under newsletters You're gonna see it right on the left-hand side you can get the opening call for one month for $149 Six months for 695 which is a savings of $199 at 22% in one year for 1195 Which is a savings of $593 of 33% now they all come with a 30-day money back guarantee So the bottle and not only that on top of that what Basil has there. He's done many webinars He's got 12 live webinars on there. So the bottom line you're gonna get some great webinars You're gonna get a great newsletter and if it works for you great if it doesn't work for you 29th day Just cancel it and no problem Basil Chapman. What's going on? Hi, Tom. How are you? I'm doing great man yourself. It was great to hear you with Larry. It's amazing he's been trading forever and But you know, it's we always fascinated by his By his technique and what he discussed, but I don't know his memory for detail. He'll say when I was with Joe whatever on Was it a Tuesday? I was a Tuesday 1965 I know man Seriously, it's fabulous. It should be a wonderful workshop. That's for sure. No doubt. No doubt So we're looking here at something that's quite fascinating for me Okay, we could go when I spoke to you, you know It feels like a lot's happened, but if you look we're basically in a kind of a sideways move here Here's the down the left side. I'm showing the daily chart. Let's go back to last week We were around about here at the Dow 33,000 463 here. We are 35 35 463 here. We are thirty five thousand six twenty four To the markets having a little difficulty and if you got every time the market looks like it's about to decline sharply You get a new some kind of an earnings report today It's catapulted spirals up really sharply and helps the Dow the S&P is telling more of the story But this is what I'm looking at. Remember when I spoke to you last week. I said I've got a technique I call it my Technical instruments of last resort. It's like the Federal Reserve is the Bank of last resort So this is my technical tool of last resort This is where the nine-period moving average is over the 14-period moving average and look how many times since April when it crossed green The nine-period moving average showed that the rally in the show the rally in the down the down They made a left side a high and then a right side high and then came down sharply And then it crossed negative pink means that at the 90s under the 14 means that you've got a deeper cell mode And you can keep going down then what happened is it's flipped to positive in June and Once again, they got a left side and then a right side this this m shape pattern and the right side dropped sharp You know look for certain that that green line would change to pink and no it didn't it held green and look where we are now We're at In the same situation where you made a left side high now you've got the right side high What happens next is going to be very important and you can see the informeer At least the importance of this nine-period moving average holding so well says that a lot has to happen for this Price the great great line is the is the closing price of the down for the price to come down Very sharply to get that green to turn pink. That means you'd have to be down 35,000 oh probably in 34,800 to see it even getting close so it says to me a Couple of things have to happen. You have to see that I've got this technique that I call the dark news cloud cover and I'll just show this chart for a moment because there are a lot of things that I wanted to look at but I'll show this and We are now well above this whole resistance area since November of 2022 And what I call the dark news cloud cover is that like interest rates or Geopolitical whatever it is that the market will take seriously and decline There's always something harboring but all of a sudden it looked like oh those things were dissipating So the market actually the Dow itself went above that 30 I think with 34,700 level and now look where we are So this means you've got a huge containment area. So I just wanted to put that into perspective So I am anticipating that we're going to over the next it might take a little while either It's just really bad news and you know, I don't want to go into now I'll do to my show tomorrow at 10 o'clock when I Eastern time and I do my Tiger technicians hour. I did a little bit today, but you've got the TLT Actually, I should show it right now the TLT Which is trading underneath that rectangle and I said was so why is it not going? Oh, there it is There it is a TLT Underneath it's at 98.05 right now that's bonds underneath the rectangle Which made this arch formation within like an m-shaped formation now. It's taken that out So there's a chance yields go higher. You spoke about the doll. You know, I'm always looking at Leg D and a peak D in the Chapman wave methodology for turnarounds look Yes, the the TLT daties a peak D. Look how sharp we fell There's a peak D all the way at 179 back in March of 2020 now look at the dollar It makes a trough D at the bottom and it runs and it's in the leg D to the upside So that and it's gone back into the rectangle So that's the other thing and look at crude oil crude oil is starting to kind of push the upper limit of this rectangle formation So there are things sitting out there that say hey the market might start to worry about this in a moment But when I when I look at the overall market, I have to wait For the to get a confirmation of really a sharper move down for all those those nine-period moving averages in the daily Charts to turn down and so far. They're holding quite well So that's just kind of a warning to say yes The next move down could be a little bit longer and a little bit deeper But we haven't actually started that yet But in the meantime for my subscribers, we were very fortunate. We had a stock I was working to you about this actually for a couple of months now some some Bartek sym is the symbol and It had a sudden spiral yesterday was up 50 percent It's actually up 200 percent of that big rally from the way we entered it We don't have as much as I would like but still it's fantastic to have a big move like holy cow. Look at that thing. Isn't that something? Yeah And yeah, it wasn't take over anything I think this was purely on earnings and there was another company Added to its allotment that it owns part of of somebody some somebody is in the end-to-end AI robotic warehouse automation systems so These events are really important and being able to identify sectors that have this kind of potential like AI And I'm looking at others that I think if there is a pullback here We've started to enter some positions that could be I would say I wouldn't you can never tell whether it's market Unrelated but they kind of under the radar. They're in their own world So I think this is a time for select selectivity raise some cash But at the same time, um, there are these positions that have you know potential to do really well We've got you know, we've still got this box, which is the AI The etf the global ex robotics and AI etf that made a double top. It's pulling back But it's actually holding very well So there are a lot of things that I would like to look at for the next part of the next move up In the in the market after we have some kind of consolidation And folks It's very easy to get basil's newsletter to come over to our website at tfnn You're going to go into newsletters. You're going to see right now left hand side You hit that button and you are off to the races basil. You have a great night a safe night We look forward to show tomorrow morning at 10 o'clock. Thank you very much. Tom. You can thank you Have a great one. Stay right there folks who come right back


Also, in business reports, the Chartered Institute of Taxation of Nigeria, CITN, has hinged their low tax compliance in the country, the lack of education of payers, as they are sometimes ignorant of some requirement. The president of the CITN, Adesino Adebayo-Sentis, at the annual dinner and award of the Institute in Lagos, details in this report. As the year rolls to an end, it is not unusual for entities to unwind and reflect on the high point of the outgoing year. As the Institute relieved how it promoted these cuts geared toward enhanced intoxication as a sustainable tool for national development, the professionals broke on ways to ensure effective tax compliance. Tax is not something that is easily paid, even from biblical times. You find out that it is always in the state of Confucian, where people don't know what level of income do I need to declare, what part of income should I even declare. Is it the income I earn outside of the normal course of business? These are part of the ignorance that is there. The National Assembly and all politically-exposed persons should do their own bit to contribute to tax pay. And let us see, let us see what tax money is being used for. For example, you come to Lagos State, you see it, you're happy to pay tax. Tax administration is both persuasive and coercive because it's backed by the law. Therefore, you make people to be aware of the need to pay their taxes. Since the introduction of the nation's digital currency, the E-Nirre, reactions still trail how it can reduce tax evasion. This is just as some members of Cantor gave insight on the Finance Act, as well as corruption in the system. The implication of E-Nirre and tax evasion, until it is tested, you cannot talk about the effects. But the assumption that you have a quota or a miss whereby you have a way of actually assessing how people are spending and making money, it gives government the leverage. It has brought a lot of improvement. We introduced, you know, the transition of digital economy and among others. And it's a giant step in the right direction. The media pass burst into recounted the years spent, spoke excitedly about her reward.  A period where you beg people to come to council, to become part of the council.  So a period where people now fight to come to council, I think that is height of achievement. The giant strides made by the federal government and tax authorities on fiscal policy decisions were appreciated. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


I don't think it's going to get approved. And I know people will say, that's ridiculous, Rob, because they've had over 230 different ETF applications, and only one time have they been rejected. Simon is actually saying that here. He's like, it's a lock. I will just warn you of this. Nothing is guaranteed. These things could fall through. So just be aware of that. Even if this gets rejected, it doesn't matter. BlackRock, the largest fund in the world with 10 trillion assets that are management, is saying people want Bitcoin. What I did is I want to let you all in on the happenings that is going on. Simon said it's 100%.  And if it isn't approved, you have to wear an iHeart Moshinsky t-shirt on your next livestream. I'll do the exact same thing if it is approved. And that's what's happening with the BlackRock Bitcoin ETF.


Hello everyone, my name is Saurabh Deshmukh, working as an assistant professor in Department of Mechanical Engineering, Vulture Institute of Technology, Solapur. In this video, we are going to calculate the nodal temperature using stiffness matrix, the learning outcome. At the end of this session, the learner will be able to calculate the nodal temperature using stiffness matrix. So we are going to solve this particular problem in this video. So there are three walls, first is made up of the brick, the second is made up of the insulator and third one is again made up of the brick. So their length and the temperatures are given. So we will solve this problem using stiffness matrix. So first of all, we will convert that problem into a line problem. So this is our first node, this is first wall, this is second node, this is second wall made up of the insulator and this is third node and this is third wall made up of the brick. So this is K1, K2 and this is K3. This is first node, this is second node, this is third, this is fourth. The temperature of the first node will be T1, this is at T2, this is T3 and this is T4. So we will first write the given data here. So the T1 temperature at the first node, it is given as a 200 degree Celsius. The temperature at fourth node, it is given as T4, it is 20 degree Celsius. They are also given the K of brick, it is 80 into 10 to minus 3 Watt per mm degree Celsius. Also Ki equals to 1 into 10 to minus 3 Watt per mm degree Celsius. They are also given the area of the wall that is A1 equals to A2 equals to A3 equals to 1 mm square. Also the length of the brick wall, first brick wall it is L1, it is given as 10 mm. For the insulated wall it is 5 mm and for the brick wall, third brick wall it is L3 equals to again 10 mm. So now first we will calculate the K1 that is for first brick wall. It is K1 equals to it is A1 KB upon L1 into the distributed matrix it is 1 minus 1 minus 1 1. So we will substitute the values of A1 KB and L1 here. So A1 it is 1 into KB it is 80 into 10 to minus 3 upon L1 it is 10 into 1 minus 1 minus 1 1 equals to it is 80 into 10 to minus 3 into this matrix. So we will multiplied this matrix by 8. So it will be 8 minus 8 minus 8 8 into 10 to minus 3. Now the parameters of the first brick wall and the third brick wall it these are same. So we can say that it is K1 equals to K3 equals to it is 8 minus 8 minus 8 8 into 10 to minus 3. Now similarly we will calculate the value of K for the second wall that is insulating wall. So it is K2 equals to A2 KI upon L2 into 1 minus 1 minus 1 1. So we will substitute the values of the parameters of insulating wall in this equation. So it is K2 equals to A2 is 1 into KI is 1 into 10 to minus 3 upon L2 the length of the insulating wall it is given as L2 and it is 5 mm. So divided by 5 into 1 minus 1 minus 1 1. So it is equals to it is 0.2 into 10 to minus 3 into this matrix. So we will substitute the value as 0.2 or we will multiply by 0.2 into this matrix it will be 0.2 minus 0.2 minus 0.2 and 0.2 into 10 to minus 3. Now we will find the global stiffness matrix the global matrix we denote it by K so it is K equals to what is it? It is K1 minus K1 0 0 minus K1 K1 plus K2 minus K2 0 0 minus K2 K2 plus K3 minus K3 0 0 minus K3 and K3. So we will substitute the values of K1 K2 and K3 in this global stiffness matrix so we will get it as K equals to K1 is 8 into 10 to minus 3 K2 is 0.2 into 10 to minus 3 and K3 is also same as K1 it is 8 into 10 to minus 3. So we will substitute here and we will get the global stiffness matrix. So it is 8 minus 8 0 0 minus 8 8 plus 0.2 it is 8.2 minus 0.2 0 0 minus 0.2 it is again 8.2 minus 8 into the common is 10 to minus 3. Now we know that the equation it is Q equals to the stiffness matrix that is K global stiffness matrix into the temperature. So heat flow equals to K into temperature. So we will substitute the values here so what it will be? So I will just change the page here I will substitute the Q K and T matrices Q K and T here so it will be Q1 Q2 Q3 and Q4 equals to K11 K12 K13 K14 into the temperature the nodal temperatures are T1 T2 T3 and T4. Now we will apply the boundary conditions here so applying boundary conditions. What are the boundary conditions here? Here Q2 and Q3 are the 0. So Q2 equals to Q3 equals to 0. We know the global stiffness matrix we have calculated it and we also know the temperature at node 1 and at node 4. So it is T1 equals to 200 degree Celsius and T4 equals to 20 degree Celsius. So we will substitute these values here. We do not know the value of Q1 we know Q2 Q3 and we do not know the value of Q4. We will substitute the global stiffness matrix that we have just calculated it is 8 minus 8 0 0. We will also substitute the values of T1 and T4 here that is 200 degree Celsius. We do not know the value of T2 we do not know T3 but we know the value of fourth node temperature it is 20 degree Celsius. So we can convert this matrix form into the simultaneous equations. So it will be Q1 equals to it is into 10 to 2 minus 3. So we will write it here 10 to minus 3 for the global stiffness matrix. It is 8 into 200 minus 8 into T2 into 10 to minus 3. Also for the second row it is 0 equals to it is minus 8 into 200 plus 8.2 into T2 minus 0.2 into T3 into 10 to minus 3. Similarly, it is 0 equals to minus 0 0.2 into T2 plus 8.2 into T3 and minus 8 into T4 into 10 to minus 3. Also the fourth equation is Q4 equals to it is minus 8 into T3 plus 8 into T4 it is 20. So I will write here 20 instead of T4 it is 20 into 10 to minus 3. So this is equation 1 this is equation 2, 3 and 4. These are the four simple simultaneous equations. After solving this four simultaneous equations into the calculator, we will get the values of T2 and T3 that is T2 equals to it is 195.7 degree Celsius and T3 it is 24.28 degree Celsius. These are the references. Thank you.


So, before we go to R square, one basic concepts you can know the correlation coefficient as I told in regression correlation coefficient fits here also very well. If you look at this data sets, general just I am giving summary of correlation, if you see here this data says that simple between x and y if you fit the some regression and if you look at the relationship here you can see the perfectly correlated right. So, therefore R equals to 1 exactly 1, here you can see some data scattered here and there. So, therefore the observed data here you are there is a positive correlation your regression line is quite good positive correlation, but it is ranging between 0 to 1. Look at this particular example point number C, here the graph says that there is no correlation between x and y. So, here your correlation is 0 here also remember that they will try to link that with your R square now, the both definition of R square we have discussed we will illustrate now. So, here you can see there the you know before I go to R square I am giving the like the background information about the R square through correlation coefficient Pearson correlation coefficient. So, here you can see the correlation value here which is R equals to because there is no relationship between the data look at you cannot fit a good model here. So, you know look at the fourth example.  So, this is your you know correlation you have between x and y. So, this is what the essence actually negative example you might sense or where like you know there might not always exactly one negative minus one could couple of data could be falling like this also in that case also you can fit your regression or you can find a relationship. For example, suppose you can I can give one example say if you increase like during corona you can think that you know if you you know increase the mask or say vaccination drive probably your corona cases will fall down right. So, if y represent the current number of corona cases and x represents a the vaccination drive probably you know every day if you increase the vaccination probably your you know number of corona cases will fall down. So, that is a negatively correlated you might, but positive correlated correlated examples are too much in the practice one example of negative correlation also I have explained may not be fully negative, but to some extent say strongly negatively correlated, but there is a relationship. So, these are the four aspects of correlation coefficient and this is the formula using x l you may get to know or python you may get to know this is the formula of correlation coefficient. In regression analysis if you take the square of it this will become r square actually and that also represent the measure of strength or the explained relationship between the independent variable and the dependable here you can see that right. So, the strength you will get to know so, here you will get complete r square equals to 1 because all data falling here also you will get r square equals to 1 despite it is minus 1, but here you will get r square say you know say 85 percent or 95 percent quite good, but here you might get you might get r square also equals to 0. So, this is what the r square or says coefficient of determination or the strength of regression between independent variable and dependent variable it is derived from the it can be derived from the correlation coefficient also. So, this is what r square here I have derived you can see the r value for the data sets of the sales and advertisement I have put the data and I found r 0.9 the strength the correlation coefficient between the independent variable dependent variable are coming out to be 0.9 90 percent and if you take the r square it is coming out to be since it is a less than 1. So, it is square is between reduced not a matter, but r square is come to be 81 percent 0.81. So, that means it is to some extent it is a good strength the relationship are been there that means the y is been explained or derived by x very effectively. So, there is a strong relationship you can say above 80 percent we can consider it is a good relationship. If it is a 1995 then it is very strong r square and you can say that the relationship that regression that you have fit are very good fit. So, therefore r square is one of the most important measure for goodness of fit or you can say you can say that the regression model the basic definition of r square or element definition you can say that how the independent variable is explaining the dependent variable that measure are been done through r square value or you can see coefficient of determination. Now, we found that r square value now through correlation coefficient in regression it works, but in general what is the another definition the common definition in regression analysis what is the common definition of r square. Let us see this part it is nothing, but the ratio between that explain variation by total variation. So, now you might be confused what is explain variation and what is total variation. It is nothing, but the ratio between the sum of the square of regression sum of the square to regression look at it is nothing, but the I give I feel this is the better way to understand explain variation by total variation, but in general in book people use this terminology SSR by SSD what is SSD and what is this formula I will illustrate here now. So, this is nothing, but the ratio between sum of the square to the regression SSR which is nothing, but the deviation between the predicted value deviation between the predicted value and the mean value of the data. You might say that how come I will understand the mean value of the data of the entire Y dependent variable data this is the you know SSR the deviation between the predicted value and the mean value by the SSD the total sum of the error. So, this is the SSD how what is that it is nothing, but the deviation between the actual value and the mean. So, let us see how this R square can be derived through a graphical presentation of SSR by SSD. So, let us draw the graph here if you take a graph here suppose say this is your X and this is your Y say and you have the data say you have the data and you have fit a regression line.  Now what happens here if you look at the data in that case the dotted line is R say suppose couple of points are falling on the line also couple of points falls on the line because you have fit a regression line couple of point will fall on the line and the distance between that you know point from the line you have calculated right the deviation circle and you have the least square you have found this predicted value that we have done.  Y bar is nothing but the average of the data right average of the data average of Y i right.  So, if you take that deviation say from the mean the object value deviation from the mean and the square and the sum you will get the SSR now look at it is nothing but you know say SSR or nothing but you are taking the object value on the line on the line look at on the line on the line right minus your mean value and you are taking the square of them and the sum of them this is your SSR now note down and you calculate your SST this is nothing but the deviation between the actual value and the mean actual value where now these are your all actual value right these are all actual value including the values on the point also. So, including the SSR are less because only the point following on the line regression line you have taken and you have taken the deviation to the mean right and now you are calculating the SSR total sum of square total sum of square all data you are taking and you are taking the deviation your calculation deviation from the mean all data you are calculating in that case you are you know what will be SST SST will be look at all data you are taking all data of your you know data sets minus y bar you are taking right minus y bar you are taking the mean data that we have already calculated both you are subtracting from to the mean. So, therefore, you know you can take this as your SST and sum of them what is that this is the all sum of square total sum of square of all data to the mean and here in the numerator you are taking the only the point on that this is what your R square. Now think one aspect here if SSR becomes SST or SST become SSR what will happen your R square will become 1. So, you have a very strong fit all data are falling on the line when all data will fall on the line when let me put one you know small graph here you will be able to understand. So, that you know this calculations I can you know say now if you see here when you are look at the in the numerator you have the y hat of the data who are falling on the line and in the below you have all the data. Now imagine if y i becomes all y i hat. So, that means all points are falling on the line. So, this all point are falling on the line in that case what happens your numerator and denominator will become same. So, your R square will become 1. So, if you can fit a line where all points of observed value are falling on the line you will get the perfect correlation here you can see you will get the perfect correlation your R square will become 1 this is nothing but your R square which is been defined by you know explained variation by total variation SSR by SSD. So, you will find the perfect correlation whether positive or negative not a matter, but in practice what happens no all points may not fall in general it will not fall on the line only couple of point will fall on the line. So, therefore R square will be less than 1 therefore R square will be less than 1 right there you will try to find what is your R square value. If more point fall on the line or you have fit a line where the distances like these are the distances like the square of the distances like some of the square the deviations right. If the top deviations are very high like more point are falling on that in that case or both become closure. So, you will find the R square is coming closure to the 1. So, therefore the objective will be fit a line. So, that this SSR by SSD become almost same if you can get that kind of line your R square will be very high or almost closure to 1 and you have fit a good regression line that is it, but if it is not your R square you will know in case if it is there is a big deviation like most of the none of the point are falling on that probably your R square will be to some very less. Very less means your regression are not good, but if you more point fall on the line or can rest or closure to the line and most many points fall on the line your R square value will be high this is what the you know goodness of fit way of calculation through R square.  In general the higher the R square the better the model fit if you have done it that is it is your R square. Now, let us take one example and through these examples we will illustrate the excel and get to know whatever we have discussed today through the analysis also of reading the graph and the entire illustration of today's session of goodness of fit. Here the example says that a car manufacturer has recently three days of roadside exhibition on the introduction of a new model to its of a deluxe car. The number of sales personal employed at each of the sample obtain exhibition you will find 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. So, 10 exhibition they have and the number of sales person are been employed over there and the number of cars booked are given there based on the past data. Now, what you have to do estimate suppose if they would like to set up another stall say or another say exhibition center with 10 employee salesman in that case what would be your forecast of number of car booked will be booked. So, that you are going to predict through regression analysis. So, in that case what would be the regression output is not our objective because in regression excel you will get to know the immediate value of forecast of that. Our objective here is that what we have learnt today that illustration that is calculate the R square, calculate the standard error, calculate the prediction interval. Let us go to the excel and understand this. So, here we have come to the excel and the same data I have kept in the excel sheet now. Look at the number of sales person here like we have 10 sample that I have kept here and the number of car booked in the previous say exhibition of centers are been kept here. So, we will put the regression line suppose if you keep this let me copy this and suppose come here and if you paste the data go to data and use the regression. We all know it, but let me illustrate it. So, let us first fit the regression line. So, why output data cell are like this number of car booked as dependent variable. Now, your independent variable will be number of sales person and put it level because we have selected first row as your level value and say set here suppose. So, you found the regression line now. Now, what we will do? We will read this regression analysis the output cell. How the output cell can be read through regression analysis that we are going to study now. So, here you can see one by one. Let us first fit the regression line y equals to a plus bx or m plus mx plus c or alpha plus beta x. So, these two points intercept and slope. Then what would be your prediction line? Your prediction line will be say for 10 employee or person. It will be say y equals to mx plus c it would be say this intercept into 10 employee plus slope into x number of employee or person plus intercept. So, you get 185. So, this is done. So, your forecast of number of car booked for 10 employees or 10 sales persons will be 185. So, that is done now. This is not the objective to discuss because we found immediately through regression line, but we would like to read the other part. What are then? Look at here what we have studied R square. Look at R square. What is the R square value? You understood the R square now, right? R square is coming up to be 82.82, which is good. So, you can say that this data has a good relationship. What is adjusted R square? Adjusted R square are nothing but if you increase the sample size or say you know do more simulation in the data with similar pattern, you will get some different R square. So, actually it is a penalty that if you increase the sample size, in that case what could be your adjusted R square? So, that we are getting since you are increasing the penalty, you are increasing the data sets or you know you are doing more simulation in the data. So, therefore it gives a penalty and reduce the current R square value little bit. So, that R square is nothing but the adjusted R square, right? System will do different type of trial and error process and they will come up with the predicted value that is called adjusted R square. Sometimes people prefer adjusted because sample size is less here. So, therefore when you have a larger sample size you might say that your R square might be changed. So, that is in general for this type of data pattern what could be your adjusted R square? It is coming up to be 0.79. So, this is your adjusted R square. People sometimes take that rather than R square also. So, we can mark this also. What is multiple R? If in case if you have a multiple variable like similar one more variable if you add like you know and if you go to multiple regression in that case people use the multiple R square value. So, that we are not discussing. Now, if you come here the standard error. What is standard error? Standard error is nothing but the interval from the predicted value sorry the predicted value the deviation which I have discussed today. So, this is what standard error. How much it is? 10.47. So, now you can calculate say one standard error you can calculate the two standard error right. So, one standard error two standard everything you can calculate here now. So, let me put a different color because it is looking very big like high resolution. So, suppose you put this. So, you will find the standard error say. Now, if you look at the number of observations. How many observations we had? We had 10 observations. So, we can see the observations are 10. So, this first part of regression output statistic are being understood now discussed now come to the ANOVA line. So, here before I discuss this ANOVA part and this V value here. Let us go back to the you know the seed where I have already calculated to save the time come here. So, look at you found the standard error here 10. something and if you take say 95 percent confidence table. So, you can put two standard error like plus minus. So, you will get a range of 205 and lower bound 164. What does it mean the interval? I am talking about prediction interval. What does it mean? Your standard error esteem and predicted value is 185. So, it is nothing, but your prediction is 185 say for you know 10 sales percent 185, but your interval is how much say suppose if you take a 95 percent confidence interval predicted interval in that case what would be your value? It will be 205 like two standard error and below lower bound will be 164. So, this way you can calculate your you know through standard error and the prediction interval which also you have discussed. Now, let us come to ANOVA analysis. So, here ANOVA nothing, but it is a the overall analysis of variance of the data. This signifies your overall test for the fitness of your model right about the regression model. Here you can see here we use the F statistic here. So, here you can see the degree of freedom the first one. The regression you have only one degree one independent variable. So, the degree of freedom becomes 1 and residuals here we are taking k minus you know n minus k minus 1. So, it is n is 10, 10 minus k is 1 minus 1. So, it is n minus k minus 1. So, n equals to 10, k equals to 1 minus 1. So, effectively it is coming up to be 8. So, this is the residual. And then total your degree of freedom will be the degree of freedom of residual will be 8 and total degree of freedom will be 9 sum of these 2 right 8 plus 1. And then it is the sum of the square error. What is that? You will get for regression sum of the square to the regression you will get 4050 say and the residual sum of the square of residuals you will get 878. And if you add them you will get the total sum of square error. And if you take the mean of them mean of the data if you divide that this by this you will get same as it is here you will get 878 by 8 you will get 109 like you can see here equals to this by 8 say you will get 109. So, this way you will get the mean of the data.  This ratio is nothing but your F statistic, F test value. So, higher the F value better the model you have actually. If F value is very less say closer to 1 or 2 etc less than 2. something 5 etc your you are concluding that there is no relationship between independent will and independent will. You can cancel your regression analysis the trail line forecast. But if the F value is bit high this ratio between this say you know I can show you this is nothing but this is nothing but these value by this ratio right 36. So, this value if higher the F value better the model actually. Now, remember in general what happens let me you know this look at the next part the P value. The outcome of F statistic or F testing analysis are been measured through the P value which is calculated through you know chi-square test and the T data sets. Here we will use for since it is overall analysis you are doing overall test you are doing through you know the overall fitment of your regression model use the F test. But when you go to the individual variable level will and the corresponding P value we calculate we use the T test there. So, this is the difference for overall analysis you use the F test of regression model and for individual variable you will get T statistics and the corresponding P value. But remember for this difference you will be able to observe better in multiple regression because there you will have more independent variable in your list right like x1, x2, x3. So, in that case we will find the P value for individual independent variable with dependent variable. The lower the P value less than 0.05 is the major significance value if it is less than that then your null hypothesis you reject and you say that there is a relationship. But overall test you will get one significant value for overall regression that is also the significant cutoff point is P less than should be less than 0.05. Now, how you define it that F test or the individual test say both I am explaining at one time. Suppose if you suppose you have developed a y-caps to suppose alpha plus say beta x beta is your correlation could be the relationship between like the slope say. So, you propose a null hypothesis say beta equals to 0. That means there is no relationship between independent variable and dependent variable and you propose another hypothesis alternative hypothesis that beta not equals to 0 right.  If you accept your alternative hypothesis what does it mean that there is a relationship between beta not equals to 0 there is a relationship between independent variable and dependent variable. And your analysis suggest that your F test is significant and P value is quite less and you feed the regression. But if P value is quite high say 0.251 it is like very high value P in that case 0.75 etcetera in that case you can say that you know there is like null hypothesis is accepted and now alternative is rejected and you can see that there is no relationship between independent variable and dependent variable. Now this is the overall case now for individual also you can do individual test also like for P test analysis and the corresponding P value. But when you go to the multiple regression what happens when you go to the multiple regression in that case this changes this you know you will get say you know beta 2 x 2 etcetera we will discuss their detail. But just for your summary like in that case your null hypothesis will be all beta say beta 1 equals to beta 2 equals to say 0 suppose 2 variables I am considering and null alternative will be at least 1 beta i not equals to 0. Again you check your P value for overall test if P is less than 0.05 you reject your null hypothesis you will accept alternative at least one of them will be non-zero. So, that means there is a relationship and you can feed your regression. But how many of them how many of beta or say the coefficient the regression coefficient are non-zero that you can find from here from this individual test. Here you have only one variable you have only one variable here you will find that coefficient not do not see the intercept P value just see the sales person which is a representative of your independent variable. So, here you can see this is quite less than 0.05. So, it is significant. So, individual level also it is significant and the overall also it is significant. So, the regression is fit good fit. So, here is the measure of you know data sets and here you can see also 95 percent interval are also been predicted for the sales and the intercept. No need to think about that just think you see the interval for confidence interval for the sales representative. So, you will find that it is you know it is quite good. So, now what happens is that you found the overall test of your data through analysis and F test you find F is quite good 36 which is quite good the ratio and the corresponding P is good and for individual also you found the P value look at here which is also less than 0.05. So, both are ensuring that your regression analysis is quite good and also your R square is quite high 82 percent and standard error will be always there. Here you found the standard error in the corresponding confidence by a predicted interval also you have calculated and your intercept and slope also we found through regression value and you can make the prediction for the sales representative now. This is what you know the overall understanding of summary output of regression analysis. Now, we will extend this concept for multiple regression also in the session of multiple regression and we will understand how this table or the summary output works. Similar way I have already explained, but I will summarize that for multiple independent regression also. Now, come back to your PPT presentation and what we have done today. We have let us come back to the particular output cell now. So, we have discussed the different type of you know goodness of it approach. We have discussed the R square concept, we have discussed the standard error, we have discussed the F statistic and the significance value of your overall analysis and the reading of coefficients and like intercept and the slope and the corresponding P value which is very crucial here corresponding P value of your T test of individual variables. This should be less than 0.05 and here also the overall test should also be significant and the forecast are coming out to 185 and the corresponding 95 percent interval of predicted interval is coming out to be 2 standard deviation of that. Though you have to multiply not 2, but actually T value here say 10, say 10 sample it might be 2 point something say around 2.45 or maybe less than that also. So, that you have to multiply and you will get the predicted interval. So, this is what the summary of today's class. So, let us come back to the first slide. So, today what we have discussed? We have discussed this coefficient of determination, web statistics, concepts, standard error and predicted interval. These are the measure of goodness of it. How good your regression model are that are measured through this couple of estimation process or you know the measure of accuracy process. This helps you in establishing or being confident in establishing your regression analysis for any type of data with industrial data or in social science data. I believe you will be able to apply these concepts in your regression model whenever you fit some regressions and you will build your confidence whether your model is really good or bad or whether they have a good strength or not. So, with that let us conclude the measure of goodness of fit of regression analysis. In the next class we will enter into multiple regression.


The crew of USS John C. Stennis celebrated Thanksgiving Day while at sea November 24th. John C. Stennis entertained sailors with a tailgate party, football games, various competitions and a Thanksgiving meal. Command events such as this, they improve morale because it takes your mind off the work that we have to do and we're able to do kind of some fun things and enjoy a game, stuff that you'd be able to do when you're at home and really allows us to work harder I think because you get that time to relax. John C. Stennis sailors may not have been able to enjoy the holiday with their families, but the ship did make the best effort it could to make the sailors feel at home. Reporting from onboard USS John C. Stennis, I'm Seaman William Ford.