barclay capital inc This is a topic that many people are looking for. keralaplanningboard.org is a channel providing useful information about learning, life, digital marketing and online courses …. it will help you have an overview and solid multi-faceted knowledge . Today, keralaplanningboard.org would like to introduce to you Behind the scenes at the Global Head Office in Canary Wharf Barclays. Following along are instructions in the video below:
“550 to now and at this time. I should be on my way downstate to to go and change and ready to go home namaste starts at 7 00. We broccoli six of super people every month. Many lunches go international basically not every day is the same they get around about a hundred two hundred and fifty that they just registered less they re the ones who have a kind habit dave in a minute support get him up around about half nine.
So we can get on with their this is in the bank. I ll make although you re doing same thing every day you d see different people one time. Yeah. He s probably walk between ten and twenty miles are doing sometimes more everybody say morning dave and he s always happy and chatty yeah.
It s always nice to see it berries liked and some of the stuff comes in the wonder. Oh. It was all that it was conga in the lift. It s none stop face enjoyable at the same time.
Yeah. That s it join the gym. I must admit yeah. Thank you bit money.
Joining gym. Doing so much walking up and down. So yeah. Yeah and my role is taken from the hotel model.
Where i m the general manager. I have a head of catering head of security head of cleaning and all the other services. They reporting to me we meet on a daily basis at ten o clock. We review.
All the issues with the building and make sure we deliver the service for barclays. So now the catering assistants are doing that their final preparations for for lunchtime open images in about ten minutes time so absolute temp to detail and you ll see the attentive detail in terms of the presentation of the food and how they have to keep that up throughout the day as salable. We have sixteen different salads on on on a daily basis. They re all made fresh on on site by a team of four chefs that just makes salads all day.
Long and then they re prepared for the next day s of this service on their most popular dish across the board is a katsu chicken and we go through five hundred portions the last six weeks. We ve actually used seven to two thousand eight and that s just eight that we cracked open for frying and biking to work our practice. You have to have understanding a knowledge ability you always have to know to make the customers. Happy also she s from one till to the other she s absolutely excellent and she s a real character.
So that kind of brightens up your morning. Just say. Hello. Baby.
How are you she is full of energy happiness. Enjoy see. And she calls everybody baby and i love. It every time.
I come down for energy that come on baby. So sometimes they say to me..
I told you was my only baby that s a. Yeah. My only baby keeping a building of this size running it s a huge job. If you imagine that s a bit like the guy in cape canaveral.
Who says that he s helping to put a man on the moon. All of their staff from the facility s perspective. Or helping barclays to run their business for us. It s about keeping people happy found at ward and in the right environment.
So we we normally supply in about five small towns during a normal day. So that s quite a lot of people to support the environment that people working is an important environment to them they work better when they re more comfortable. I ve worked in this building 11 years now. There was no faculty staff in the building when i came here they were still building walls and installing equipment.
I ll see you tomorrow i started as a member to port which team then went on to the reception. What s in all the decimal. A ground floor went up to level 31. Four to two and a half years ten years promotions to build an opener.
If we if we didn t have floor manager like michael on our floor. Would be abdullah place changes literally. When i was presenting something the next thing. I know there was kind of a floating window cleaning unit with two guys kind of hanging out edyta whatever thousand foot.
We re up here and i kind of reset. So that was kind of funny moment for us cuz thinking about the guys that actually do that and the great work that they do for the building was like wow. We caught like a big village. You know what it really is it s a village for us that actually run the building.
There s things happening here. All the time having. Some people who are working for eilat and decided to just um go into a meeting room and fall asleep tonight usually fairly busy when when we come in and sort of a 7 00 in the evening and quietens down around about to what ten eleven. I love working this building home.
I ve worked in in security works in lot of the buildings. Whereas here things unless. Ten years old. But still no practical so yet love work a little bit a few few scares.
But nothing to feel like hearted. There s some people reported about political system. And how far is during the thirty six and stop there because you re part of working a night you d up one seven zero times. X.
Where x. Is the performance rating. You can write performance in here instead. If you want either way it s acceptable.
But that s our estimated regression equation. We have our tuco we have our two estimates plug those into this y hat formula that gives us our our equation all right..
So that s part a let s go back to the problem click in the problem tab. We can see part b determine the estimated. Regression equation that can be used to predict the pcw world printing using both the performance rating. And the features rating.
Okay we have more information than we used in this model. If we look at our data. We see that we did not include our features. I said an explanatory variable.
So part b is asking us to look at what it would look like if we did we can do this pretty straightforward same as we did before click on data analysis make sure you select regression and click ok our input range is already it stays the same. So that s going to still be in there for our x range. You can see that right now. It has just column b.
Selected. What we want is bnc and if you click and drag it automatically inserts you want to see. 11. Which is what we want and we still have labels and we won t going to change the name of the new worksheet and call this full model.
Because we have all of our explanatory variables. Included and i don t care about the rest of this stuff for now click ok and now we have our new output. Okay output table just widen these so that stuff fits and i ll scroll down a little bit just so it s a little bit more central okay again it s asking us to come up with an estimated regression equation. Which means that we want an equation that looks something like this y hat.
Equals b. 0. Plus. B.
1. X. 1. Plus.
B. 2. X. 2.
Because we have k. Regressors and k. Is 2. Right.
Here. Because we have one performance is 1. And features is 2 those are our two regressors so that means that k. Okay is equal to the number of aggressors.
We have okay now we have estimates for b0 b1. And b2..
They re down here b0. It s going to be thirty nine point nine eight b1. Is zero point 1 1. And b.
2. Is zero point three eight. So we can write our estimated regression equation. It s going to be y hat.
It s a terrible y. My apologies thirty nine point nine eight plus zero point one one three times performance or x. One whichever you prefer and plus. Take that and add zero point three eight two times features all right that s our estimated.
Regression equation. You can see just taking a look going to forty is the base score with the zero and performance and zero and features. It looks like features are about four times three to four times is important three and a half times is important as performance in deciding the ultimate the total score f statistic. Tells us that this regression is significant and then we have our individual t statistics.
All the p values are below oh five they re all significant yeah that s part b. There s a lot more we could do here you see the r squared is 84. So this explains quite a bit of variation. But the part that the question part b is just this thing right in here is what we really care about for now.
The regression equation. Okay let s go back to the problem. Stop inking go back to the problem part c. Wants us to predict the pcw world writing for a laptop computer with a performance rating of 80 and it features rating of 70 just going to copy this and paste it over here so that we can see it while we re working.
I m going to drag this so it s a little bit bigger. There we go okay so i scroll down a little bit just to give us some more room. We still have the coefficients that we need if you want to see the equation. It s right there this plus this times performance plus this times features and there are a couple ways to do this right so the one way to do it is to hard code.
It so we can do equals just tell us that it s a formula and we re going to type 39. 098. That s the coefficient. I m getting that from right here.
Plus and then our coefficient or that s our intercept. I m sorry 39 point nine eight. Sorry intercept plus our coefficient on performance zero point one one three times our performance rating. Eighty.
Plus zero point. Three eight to be two times our features rating and we press enter and we get an estimate pccw world writing of seventy five point seven six that s hard coding it we can also use cell references. Which will because it allows you to see where the data is coming from where your numbers are coming from so we re going to it s going to be equal our intercept. Which is b.
17. Plus performance coefficient times our performance rating..
Which comes from the question. I m going to hard type that that s right that s right here. I m going to type it in in numbers. And then plus our features rating.
Which is right here. Our coefficient features rating times. Seventy. Okay okay.
So that gives us the seventy five point seven nine four. If you don t round that s more what you ll get another way we can do this is we can actually enter do all cell references by turning our question. Into data. So performance rating.
Features rating. 80. And 70. And here.
We have pcw world. Rating. This is probably the best way to do it because then you can adjust things if you want here again. We re going to use cell references.
So this is equals b. 17. Plus. The 18 times performance rating.
Plus b. 19. Which is our features coefficient. Times.
Our features rating. Now you can see that in here. We have nothing but references and the color coding here shows us where they re coming from if we enter. It should give us exactly the same value as we add here.
But now it gives us the ability to adjust these so if we wanted to look at a performance rating of 75 hey that we can see how that changes things 80. If we had 90 and 90 100 perfect scores in both okay. It s still only an 89. So that tells us a little bit of something about the ways in which these will change if we adjust these numbers.
But to answer question c. What we want is a performance rating of 80 and a features rating of 70 and this would be the pcw world rating. All right thank you very much that s how you solve chapter 15 problem. 7.
And i ll be posting another ” ..
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