recursal/Fanatic-Fandom huggingface dataset:
Bryn Morris

Bryn Morris est interprété par Nathan Williams.

Bryn est le fils de Ceri Morris et Gerwyn Morris, ainsi que le petit-frère de Rhydian Morris.

## Histoire

Bryn est dans la meute de sa mère, qui se compose de Ceri Morris, Jana Vilkas, Aran, Meinir, Gwyn, Cadwr et précédemment Rhydian Morris et Alric.

Il a été élevé dans un monde où il croit que les humains sont dégoûtants, probablement l'influence de sa mère et sa haine des humains pour avoir volé son fils Rhydian Morris et les croyances d'Alric et Bryn est confondu par beaucoup de la technologie et des objets qu'il trouve dans la maison d'accueil de Rhydian (par exemple les lumières et les oignons) et il est également un peu effrayé du monde humain. Bryn est amer quand son frère dit qu'il ne partira pas et essayera de tout faire que son frère part avec lui. Nous découvrons que c'est parce que sa mère est triste et nous voyons que bien que Bryn peut ne pas le réaliser, il est extrêmement attentionné envers sa mère et est conduit par sa tristesse. Rhydian Morris est très affectueux et protecteur de lui et pense toujours à son petit frère, nous pouvons voir cela du meilleur des deux mondes, où Rhydian demande à Ceri Morris de donner son amour à Bryn.

Bryn ressemble à sa mère principalement, aux cheveux marrons et aux yeux marrons. D'après ce que nous savons, ils vivent principalement dans la nature, avec peu ou pas d'interaction humaine. Bryn n'est pas encore assez vieux pour se transformer en son loup (il est encore un "petit") parce qu'il a 15 ans. Dans l'épisode "Jana Bites" nous découvrons qu'il a quitté la meute en raison de quelques leçons de lecture humaine.

## Notes

* Dans la mini série de Jana Bites après la Saison 3, Bryn est mentionné pour avoir fui parce qu'il n'a pas aimé la manière que Jana Vilkas menait de la meute.
* Dans l'épisode "Les Liens du sang" il est mentionné aussi qu'il a fui la meute des Wolfblood sauvages.
Bobby Lockwood

Bobby Lockwood est un acteur Anglais, né le 25 mai 1993 à Basildon, en Essex au Royaume-Uni. Il est connu pour avoir joué le rôle de Mick Campbell dans Anubis et Rhydian Morris dans Wolfblood.

## Films

* 2003: Les 101 Dalmatiens 2 - Patch (voix)
* 2013: Ups & Down - Harry
* 2016: Honey 3 - Laser

## Télévision

* 2006: The Bill - Taylor (enfant) (3 épisodes)
* 2011 - 2012: Anubis - Mick Campbell
* 2012 - 2016: Wolfblood - Rhydian Morris
* 2013: Dani's Castle - Conall Connor (3 épisodes)
* 2015: Inspecteur Lewis (épisode *Magnus Opus)* : Sam Langdon

## Notes

* Il possédait autrefois un animal de compagnie, l'escargot terrestre africain, qui s'appelait Gary.
* Il est propriétaire d'un chat de compagnie appelé Alfie.
* Il est un enfant du milieu, Bobby a un frère aîné appelé James et une sœur plus jeune appelée Abbie.
* Regarder 'Billy Elliot', quand il choisit ses options, à l'âge de 14 ans ça l'a inspiré pour être un acteur.
* Bobby et Aimee ont présenté l'un des prix de la RTS (Royal Television Society) en 2012.

## Liens Externes

* Bobby Lockwood sur Internet Movie Database
* Suivez Bobby Lockwood (@bllockwood) sur Twitter
Saison 1

La Saison 1 de Wolfblood contient 13 épisodes et à été diffusée à partir du 10 Septembre 2012 et s'est terminé le 22 Octobre 2012 sur CBBC, au Royaume-Uni.

En France, la série à été diffusée à partir du 8 Octobre 2013 et s'est terminé le 19 Novembre 2013 sur Disney Channel.

## Histoire

Rhydian Morris, le nouvel arrivant à l'école et Maddy Smith, les deux Wolfblood qui ont essayer de garder leur secret toute leur vie. Avec leurs meilleurs amis Shannon Kelly et Tom Okanawe sur leurs talons, ils feront tout leur possible pour garder leur secret.

## Casting

### Casting Principal

* Aimee Kelly - Maddy Smith
* Bobby Lockwood - Rhydian Morris
* Louisa Connolly-Burnham - Shannon Kelly
* Kedar Williams-Stirling - Tom Okanawe

### Casting Récurrent

* Marcus Garvey - Daniel Smith
* Angela Lonsdale - Emma Smith
* Gabrielle Green - Katrina MacKenzie
* Shorelle Hepkin - Kay Lawrence
* Rachel Teate - Kara Waterman
* Jonathan Raggett - Jimi Chen
* Niek Versteeg - Liam Hunter
* Nahom Kassa - Sam
* Bill Fellows - Bernie
* Siwan Morris - Ceri Morris
* Nathan Williams - Bryn Morris
* Mark Fleischmann - Mr. Jeffries

## Episodes

2. „*Quand on parle du loup…*”
3. „*Les Liens qui nous unissent*”
4. „*Première Métamorphose*”
5. „*L'Explication la plus simple*”
7. „*Nuit sans Lune*”
8. „*La Soirée pyjama*”
9. „*Le Lycanthrope*”
11. „*Le Pouvoir d'Eolas*”
12. „*En cage*”
Tangent Hyperplane and Optimal Bundle

Instead of lines being tangent, the planes are tangent now. Three-dimensional graph. Curvy graph thing. The plane intersects with the graph thing at one point. Kinda like indifference curve and budget constraint but in 3-dimensional form.

https://math.oregonstate.edu/home/programs/undergrad/CalculusQuestStudyGuides/vcalc/tangent/tangent3.gif

Indifference surface instead of indifference curve. Budget plane instead of budget curve. 3 DIMENSIONS. Budget hyperplane is tangent to the indifference surface at the optimal bundle.

Finding the partial derivatives and putting them in the vector is the same. This is for the Indifference surface.

Make this equal to the vector of the coefficients of the budget plane equation multiplied by lambda(some arbitrary constant).

Find x and y in terms of z or more generally, 2 variables in terms of 1 variable.

The vector extending from the tangent point between the plane and the surface is normal to the plane.

Intuition:

http://math.stackexchange.com/questions/510420/what-is-the-intuition-behind-the-unit-normal-vector-being-the-derivative-of-the

http://mathoverflow.net/questions/1977/why-is-the-gradient-normal
Gabrielle Green

Gabrielle Green joue Katrina MacKenzie dans Wolfblood de CBBC. Comme les autres K, elle n'a pas beaucoup agi avant, même si, elle a eu une certaine expérience d'acteur antérieure à Wolfblood.

## Films

* 2012: Cuckoo - Kerry
* 2013: House of Forbidden Secrets - Brothel Lady
* 2015: Tripped - Stacey Flatley
* 2012 - 2016: Wolfblood - Katrina MacKenzie

## Notes

* Les anniversaires d'Aimee Kelly et de Gabrielle sont le même jour.
Wild

Wild est le 2ème épisode de la web-série dans Wolfblood Secrets qui a été diffusé le 28 Septembre 2016, au Royaume-Uni.

## Résumé (A vérifier)

Smith adopte une approche différente pour remettre en question la motivation de Jana Vilkas pour exposer le secret et il décrit tous les Wolfblood comme des bêtes féroces qui ne croient pas à la coexistence.

## Personnages apparus

* Leona Kate Vaughan - Jana Vilkas

* Simon Trinder - Smith

* Samantha Power - Jones

* Siwan Morris - Ceri Morris
Eigenvalues and Eigenvectors

How to find Eigenvalues. Matrix = A. Arbitrary variable = k. Determinant = Det(). Identity matrix = I.

Det(A - kI)

Get Det and solve for k when it is equal to zero. The answer is the eigenvalue. The answer to this are the only eigenvalues of matrix A.

When there are multiple eigenvalues of the same value, it is only counted as 1. eg: if there are eigenvalues 1, 1 and 2, there are only two eigenvalues of 1 and 2. We say eigenvalue 1 has a multiplicity of 2.

How to find Eigenvectors.

Solve for (A- kI)x = 0 for solutions other than the zero vector. The value k is the eigenvectors that are calculated previously(above).

You need eigenvalues to calculate the eigenvectors. Any scalar multiple of the eigenvector is a valid eigenvector of eigenvalue k.

http://math.stackexchange.com/questions/243533/how-to-intuitively-understand-eigenvalue-and-eigenvector

https://en.wikipedia.org/wiki/Invariant\_(mathematics)

https://en.wikipedia.org/wiki/Linear\_map
Laura Greenwood

## Série

### Première appartion

The Once and Future Alpha (Saison 5 - Episode 2)

### Dernière apparition

United We Stand (Saison 5 - Episode 10)

Laura Greenwood joue le rôle de Hafren dans la série Wolfblood.

Elle est née à Manchester, en Angleterre, au Royaume-Uni.

## Films

* 2005: Les frères Grimm - Sasha
* 2005: Chantage d'amour - Petra
* 2005: V pour vendetta - Sarah
* 2007: My Life as a Popat - Holly Saviour
* 2007: The Dinner Party - Lucy
* 2008: Messiah 5, le ravissement - Leah Wallace
* 2008: Echo Beach - Grace Penwarden
* 2008: Holby Blue - Jessica Glover
* 2009: Pinprick - Charlotte
* 2009: The Bill - Sarah Brewer
* 2010: Identity - Vicky Brolin
* 2011: Kidnap and Ransom - Tess King
* 2011: Silk - Emma
* 2012: Hit & Miss - Sophie
* 2015: Casualty - Anya Hollins
* 2017: Wolfblood - Hafren
Linear Indpendence

Can we make one vector using two vectors?

If yes = Linear Dependence (LD)

If no = Linear Independence (LI)

If one vector can be written as a linear combination of the others, there is linear dependence.

If there is only a trivial solution, ie the vectors can only be linearly combined by multiplying zero to all of them, there is linear independence,

How to find if there is LD or LI

1. put all vectors in one matrix A
2. multiply column of x1, x2, .... xn, X
3. AX = 0
4. Solve for x1, x2, .... xn. (make the diagonal be 1)

If answer exists = LD

If no answer exists = LI
Market Structure

Market structure refers to # and distribution of seller

* But collusion can occur
* But there can be cutthroat competition with only a few firms

N-firm concentration index: combined market share of N largest firms in the industry

Herfindahl Index: the sum of the squared market share. Less than 0.2 is fierce comp. Between 0.2 and 0.6 is moderate. Over 0.6 is low comp.

Structural Barriers to Entry

* Control of Essential Resources
* Economies of Scale and Scope
* Marketing Advantages of Incumbency

How to identify competitors?

* Qualitatively?
	+ similar products and characteristics?
	+ usage similarity?
	+ same selling area?
* Quantitatively?
	+ cross price elasticity of demand
* SSNIP
* https://en.wikipedia.org/wiki/Small\_but\_significant\_and\_non-transitory\_increase\_in\_price
Victoria Sweeney

Victoria Sweeney est interprété par Jacqueline Boatswain.

Victoria Sweeney est une Wolfblood qui travaille pour Segolia.

## Histoire

Dans la Saison 4, Victoria apparaît dans l'appartement pour parler à Jana Vilkas dans la dernière scène de l'épisode 11.

Dans l'épisode suivant, elle parle à Jana au sujet de la vidéo virale jusqu'à Selena entre en bas. Victoria demande à Jana Vilkas de l'élever, mais Jana et Selena s'enfuient. Elle essaie de donner le sérum d'Alex Kincaid à la meute de Jana mais, avec l'aide d'Imara Cipriani, tous sauf Jana s'échappent. Victoria commence la conférence de presse avec humour et esprit, mais elle ne savait pas que Jana avait reçu le mauvais sérum et elle est choqué quand la meute entrer sous forme de loup, révélant le secret.

Dans la Saison 4, du dernier épisode,Victoria et Jana Vilkas se rencontrent sur un toit pour discuter de la question à portée de main. Ses derniers mots de conseils à Jana sont de la regarder de retour.
Resource Allocation and Pareto Efficiency

https://en.wikipedia.org/wiki/Edgeworth\_box

^^^This is where the allocation of resources are decided between two people

https://en.wikipedia.org/wiki/Contract\_curve

^^^This is how we find the optimal allocation of resources that will satisfy the two people

The allocation of goods between two people is the issue that will be dealt with here.

A similar method from previous will be used.

Two people need to agree on the allocation of resources. If only one is better off in the allocation of resources, it can be considered Pareto efficient. When the other does better off, the other has to be at least as well off.

We will try to find the tangent vectors of two equations in the Edgeworthbox.

If A takes resources x and y of resources K and T, B will take the resources total\_K - x and total\_T - y.

The will be put in their respect utility functions.

The partial derivative of uA in vector form = lambda\*The partial derivative of uB in vector form

Equate the corresponding, eliminate lambda and get the equation of the contract curve.

If a point satisfies the contract curve, the point is pareto efficient.

Every one of the points in the contract curve is pareto efficient. No specific answer. Need constraints to find a specific answer.

Put the specified points in the corresponding utility function to get limits. The utility function may be equal to or larger than the limit. Now that the utility function has limits whereas before you did not, you can plot it.
Matrix methods in portfolio analysis

How much money will you make in the future when you make an investment?

YR = assets\* returns matrix

Y = asset = single row of the value of assets

R = returns = column x row = state x assets

YR = return of assets in state 1, state 2, ... state n

Arbitrage Portfolio

* If the portfolio cost nothing eg: the sum of all assets in Y = 0
* If none of returns >= 0, no loss
* If there is at least one asset > 0, at least some profit

State Price Vectors

* if SPV == true,
	+ then Arbitrage == false
* if SPV == false,
	+ then Arbitrage == true

Finding SPV

* calculate YR = column of 1
* but asset is p1, p2, .... pn
* solve for p1, p2, ... pn
* If answer exists, SPV exists and Arbitrage does not
* If answer doesn't exist, SPV doesn't exist and Arbitrage exists
Wrong Answer Notes MA207

Use the top most line of matrix to find echelon form. Take away multiple of it from all other lines.

Double check signs.

If x is an eigenvector of M, then Mx will be a scalar multiple of x with the multiple being the corresponding eigenvector.

3x3 matrix inverse. Remember to transpose the cofactor matrix. Video below.

https://www.youtube.com/watch?v=g7TFJUJXErU

If the quadratic equation is in symmetrical matrix form then use the matrix method of finding stationary points

If not then use the other method(week 10 method) of finding stationary points

Orthogonal matrix is the unit eigenvectors of eigenvalues put together

Remember x = Pz/x = Pu always

Row operations = use matrices

If all principal minors and eigenvalues are positive, then the equation is positive definite

2014 Summer

* multiply eigenvector by matrix to see if eigenvector. scalar multiple result means eigenvector. the scalar multiple is the eigenvalue
* convert eigenvectors to unit vectors then put them into P
* always x = Pu
* find the lambda
* find the contract curve using the utility curves equate hessian and find lambda
* don't forget about finite quantity of goods in the utility curve
* put bundle in contract curve to see if it is optimal
* use quantities to find the equation connecting the two goods
* differentiating x=Pu version questions are not too different
* use e^x instead
* alternating signs or only + signs on principal minors matter
* directional directive is unit vector of point x gradient vector subbed with point
L'Explication la plus simple

L'explication la plus simple est le 5ème épisode de la Saison 1 de la série Wolfblood sur Disney Channel, qui a été diffusé pour la première fois le 22 Octobre 2013.

## Résumé

La classe de Maddy Smith part en excursion sur une île. Malheureusement, Rhydian Morris se blesse, la marée remonte et le groupe est coincé sur l'île pendant la pleine lune. Les deux Wolfblood sortent en cachette de l'hôtel et Maddy se transforme avec Rhydian pour la première fois.
Cross tabulation and Hypothesis Testing

Hypothesis testing is self-explanatory. You did this so many times. You should know.

There is a null and there is an alternative hypothesis. You are given a significant level at which you check the hypothesis at. It is most likely 0.05 unless stated otherwise. You either accept or reject the null hypothesis. Null is never accepted.There is a 1 tailed and 2 tailed test. If > or < sign is used then it's a 1 tailed test. If =/= sign is used then it's a 2 tailed test. You use the normalised significance value. At 0.05 it's 1.645. At 0.01 it's 2.33. Get the p-value or the probability of obtaining a z-value. If the obtained p-value lies BEYOND the specified significance level then null is rejected. If not null is failed to be rejected.

Cross tabulation reflects a joint distribution of two or more variables. We use a chi-square test for this. Can include a third or more variables.

Chi square is fucking bullshit. So much shit. God fucking dammit.

why chi square is used

* ease of comprehension - can be easily interpreted and understood by people with little statistical knowledge
* versatility - may provide better insight into complex phenomenon than a standard multivariate analysis
* clarity - clarity of interpretation allows better link between research result and managerial action
* simplicity - used for finding relationship between 2/3 variables only as it becomes too complicated with 3+ variables and it becomes hard to have enough respondents for each variable.
Macroeconomic Models

Macroeconomic Models

* Ct = c + dYt-1 c,d>0
* It = i + v(Qt-1 - Qt-2) i,v>0
* Yt = Ct + It and Qt = Yt
* Yt = (c + dYt-1) + (i + v(Qt-1 - Qt-2))
* Yt = c + dYt-1 + i + v(Yt-1 - Yt-2)
* Yt = (c + i) + (d + v)Yt-1 - Yt-2
* Yt - (d + v)Yt-1 + Yt-2 = c+i

Non-homogenous Equations

* in yt + byt-1 + cyt-2 = k there is a particular solution
* sub y\* into all the yt, yt-1 and yt-2 and so on y\* + by\* + cy\* = k
* y\* = k/(1+b+c) is the particular solution
* General Solution for non homogenous equations is Particular Solution + General Solution
* General Solution is above equation solved as if k was 0 and substituting yt, yt-1 and yt-2 with lamba^2, lambda and zero.
* Solution to non homogeous equations is Particular Solution + General Solution

No real roots in auxiliary equation?

* when lambda^2 + b\*lambda + c = 0 has no real solutions as b^2 - 4ac < 0, the solution is of the form:
* r^t\*(Ecos(theta\*t) + Fsin(theta\*t))
* r = sqrt(c)
* Find out theta using -b/2r. cos theta = -b/2r. Find our which value of theta gives -b/2r.

Behaviour of Solutions

* For solutions of the form similar to Yt = a^t ± b^t, the behaviour of the solution can be figured out easily
* Yt = a^t(1 ± (b/a)^t)
* Depending on the size of a and b comparatively and sign, the behaviour of solution can be figured out
* For r^t\*(Ecos(theta\*t) + Fsin(theta\*t)), the solution will always have a oscillatory behaviour due to cos always being between -1 and 1
* Oscillations will become greater in magnitude if r > 1 that is if c > 1 and if c < 1 the magnitude will decrease

Business Cycles

* Can apply behaviour of solutions to Macroeconomic model solutions to figure out the business cycle
Orthogonal Matrices and Quadratic Forms

Transposing is when the rows become columns(or columns become rows) in a matrix, https://chortle.ccsu.edu/VectorLessons/vmch13/vmch13\_14.html.

(A+B)^T = A^T + B^T

(AB)^T = B^T\*A^T

x^T\*y = x dot y

if x is a column vector

x^T\*x = IIxII^2 or length of x squared

http://mathworld.wolfram.com/OrthogonalMatrix.html

1) The matrix P is orthogonal if P^T\*P = I or P^T = P^-1

2) A matrix is orthogonally diagonalisable if P^TAP = P^-1AP = D

A symmetric matrix is when A = A^T

3) If A is a symmetric matrix and v,w are the corresponding eigenvectors, v&w are orthogonal

Quadratic forms

x^TAx with x being column vectors of x1,x2 and A being a symmetric matrix.

x^TAx = ax1^2 + 2cx1x2 + bx2^2
Vector Geometry

Points, Lines and Angles

* Dot product = https://www.mathsisfun.com/algebra/vectors-dot-product.html
* IIvectorII = length of vector
* a dot b = IIaII\*IIbII\*cos theta
* (1/IIaII)\*a = unit vector of a in direction a

Planes and Hyperplanes

* a\*x = a\*p
* a is given vector. p is given point
* normal vector to any plane is given by the coefficients of x,y,z of the cartesian equation for the plane
* to find a point on the plane, make two values (eg, x and y) zero and find the remaining value

Distance from point to hyperplane

* lambda\*IIaII = (a\*z - k)/IIaII

Curves and Surfaces
More Decentralisation

Allocating DM authority

* Centralisation and Delegation
	+ Decentralising is a way to get lower level managers engaged in decision making
	+ Workers may have more information but not the best decision-making skills for the organisation due to: externalities, private agendas, private benefits from organisation
	+ Question solving
		- it is all about comparing expected profits calculated from probabilities
		- use probabilities of different scenarios occuring to calculate expected profits
		- make E(profit) = 0 to find conditions for action/inaction
		- compare action outcomes to inaction outcomes to see what are the conditions for action
		- compare loss (my ideal action) - (my actual action given the probablities)
		- compare outcomes of centralisation to decentralisation to find out how different alignments affect profits
* Constrained Delegation: Delegating Price Decisions
	+ Workers are given decisions they can make but are also given restrictions
	+ They can only make decisions on certain aspects of the organisation
	+ Question solving:
	+ same as the above but there are options for the worker to take but the worker is only allowed some/one of the options of action

Fostering Communication

* Communication is key in decentralisation as it coordinates individually motivated workers from overlapping on tasks and other inefficiencies
* But communication can be difficult and it may not solve all coordination problems
* Credulousness
	+ central management may or may not trust the information given to them by workers
	+ central management may need to know about personal biases of workers to accurately judge information
	+ depends heavily on whether worker goals and organisation goals align
	+ Communication can break down if CEO is aware of personal agendas
	+ information distortion is a high possibility
	+ need to reduce internal conflict
* Communication costs
	+ Technology, more frequent meetings, regulation, etc all costly
	+ Incentivising information sharing is also costly
	+ Not trusting the information given can also be costly
* Incentives to share information
	+ Communication causes externalities so workers share less info
	+ We have to motivate instead of only enabling
	+ Need incentivisation
Alric

Alric est interprété par Alun Raglan.

## Histoire

Alric était le mâle Alpha de la meute Wolfblood sauvages et il est le père de Jana Vilkas. Il est d'abord vu dans la Saison 2, l'épisode "Le Retour de Rhydian" quand il chasse Rhydian Morris.

Alric est également un personnage principal dans "Le Bouc Emissaire" qui explique pourquoi il chassait Rhydian. Alric a une peur très profonde du monde humain et croit que le mélange avec les humains rend les Wolfblood faibles, menant à une haine de Wolfblood domestiqués. Il était un loup solitaire.

Il va retrouver Aran qui était à la recherche de la meute sauvage. Puis ils vont croiser Rhydian Morris, Ceri Morris, Gerwyn Morris et Jana Vilkas. Ils vont les aider a contrer les plans de Segolia et Victoria Sweeney avec l'aide de Alex Kincaid. Mais il va s'avérer que c'était lui qui manipuler tout le monde. Ils se retrouvent drogués et enfermer dans une cellule où ils retrouvent la meute sauvage. Alex leur montre Meinir qui est devenue humaine a cause d'un sérum, qui a fait effet à la plein lune. Alex a injecter le produit à tout les membres des Wolfblood sauvages, la prochaine plein lune et le soir même. Victoria Sweeney, Shannon Kelly, Tom Okanawe et le Dr. Whitewood vont alors monter un plan pour retrouver leurs amis et vont chercher un remède au sérum avant qu'il ne soit trop tard. Tom aura l'idée d'utilisé le vaccin qui donne des pouvoirs de Wolfblood aux humains, cela va contrer les effets de l'autre sérum. Tous les Wolfblood vont se transformer, sauf Meinir parce qu'il est bien trop tard, dans la Saison 3, les épisodes "Cerbère" et "Dernier Lever de Lune".

Plus tard, alors que Jana Vilkas et le Dr. Whitewood vont en forêt retrouver la meute sauvage pour la pleine lune. La meute de Jana va avec Katrina MacKenzie et la suivre pour retrouver aussi la meute sauvage. Ils vont découvrir qu'Alric est de nouveau l'Alpha et qu'il est très malade, il va demander a Jana de rester pour devenir l'Alpha des Wolfblood sauvages. Mais elle refusera et plus tard ils vont découvrir que le guérisseur Madoc a empoissonné Alric pour prendre sa place d'Alpha. Madoc sera alors banni de la meute et le Dr. Whitewood va aider Alric à aller mieux.

Dans la Saison 4, l'épisode "The Wild At Heart", il laissera Jana Vilkas repartir en lui disant qu'il est fier que même en ville elle n'a pas perdue son esprit sauvage.

## Caractère

Alric peut se montrer malin et impitoyable et il est sous son agression, il aime vraiment Jana Vilkas et il a peur qu'elle soit blessée dans le monde humain. C'est la raison pour laquelle il déteste Rhydian Morris, c'est lui qui a présenté Jana au monde humain. Tout ce qu'Alric avait fait était de protéger sa fille, mais en essayant de la protéger, il lui causait plus de dégâts.
ST201

General Stat Notes/Wrong Answer Notes

Topics:

Simple Linear Regression

Multiple Regression

Omitted Relevant Variables, Multicollinearity and Dummy Variables (and more?)

Stepwise modeling, Missing Values, Model Validation (and more)

Logistic Regression

Time Series

Intro stuff

Statistical ethics: Don't use misleading diagrams, notation, etc when presenting statistics

Two types of models: Mathematical and Statistical

Mathematical is theoretical and exact

Statistical is empirical and allows uncertainty

Elements of a statistical model: Regression coefficients, Constant, Error Term(residual), Dependent variable

Model has to be uncomplicated, include important factors, describe data well and have realistic assumptions.

Modelling steps:

* Formulate Research Question
* Choose relevant variables (use influence diagrams)
* Find suitable Functional Form(linear, quadratic, exponential etc)
* Analysis(using currently possessed empirical data)
* Model Checking(against other empirical data, multicollinearity, homoscedasticity, normality indepence),
* Interpretation (what does it mean)

Confounding variables: Variables we don't account for in statistical models that screw up the accuracy of models, related to both the independent and dependent variable
ST307

Lots of waffle. Some quant stuff.

Wrong Answer Notes

Market Research Question

Research Design

Internal Secondary Data

Focus Groups

Survey and Quant Observation Techniques

Measurement and Scaling

Questionnaire Design

Sampling Design and Procedures

Sampling size determination

Cross tabulation and Hypothesis Testing

ANOVA

Correlation + Regression

Discriminant Analysis
Industry Analysis

Value creation and value capture

* A firm's profit depends on its industry and position in industry
* Industries differ in average profitability
* Most of the difference is due to fundamental differences in economic characteristics. Some of it is due to business cycle fluctuations
* Need industry analysis
* Individual firm profits can vary greatly within an industry.
* This is due to differences in strategic choices and resources

Total Surplus, Value Creation and Capture

* MWP = Maximum Willingness to Pay
* Consumer Surplus = MWP - Prevailing Market Price
* Producer Surplus = Prevailing Market Price - Cost of Production
* Total Surplus = Consumer Surplus + Producer Surplus
* Value Creation = Ability for firms to create value from inputs
* Value Capture = Ability for firms to capture the value created in form of profits

Porter's 5 Force Analysis

A general analysis of the industry, not a specific detail oriented analysis. A QUALITATIVE approach.

* Need to first define the industry. Don't be too broad. The right definition of the industry depends on the question you are trying to answer. Consider geographic element.
* Framework for constructing a background image. Expansive checklist of industry characteristics. A tool to identify one particular aspect.

Substitutes and Compliments

* What substitutes are there? How many are there? How differentiated are they?
* More substitutes means less profit for each firm. Depends on cross-price elasticity.
* Compliments boost industry profits and increases value captured by consumers/complimenters. Value captured by complimentees are reduced if bargaining power is large.
* How are the compliments networked? How can it increase the ability to use the product? What's the reputation and/or quality of the compliments?

Internal Rivalry

* Internal Rivalry is not substitute products. They can be similar but not the same.
* Size of industry. Number of competitors. Cost structure. Homogeneity of products. Brand Loyalty. Buyer switching costs. Size and frequency of sales. Buyer willingness to search.

Threat of Entry

* Do entry barriers exist? If so how large are they? What type is it?
* Pricing strategies of incumbents as entry barriers.
* Entry erodes incumbent profits so incumbents want to prevent entry.
* Economies of scale. Economies of learning. Access to input/logistics. Availability of tech. Brand reputation.

Consumer Bargaining Power

* How much bargaining power do the consumers have?
* Is there a consumer union? Is there independent consumer quality checking? Can they buy in bulk? Is there a way for consumers to communicate with each other? Can they leave reviews? What are the buyer switching costs? Can the buyers integrate backwards?

Supplier Bargaining Power

* How much bargaining power do the suppliers have? To what extent can suppliers extract value from the value chain. Higher supplier bargaining power means less value captured by the firm by eroding profits.
* How many suppliers are there? How much is being bought? How many firms are there? What is the supplier's ability to integrate forward? What is the firm's ability to integrate backward.
Decentralisation

Decentralisation - Spreading out the decision making throughout the organisation

Why Decentralise?

 * Faster decision making
* More informed decision making
* Empowers workers
* Frees up time for management
* Screening and career development allowed
* Uncoordinated
* Less engaged

Making decentralisation good

* Decision making bad usually because people only concerned with their own division/unit/shop
* Aligning objectives of decision makers and others
* Fostering communication
* Firm-wide performance measures to foster cooperation may cause free riding but improve decision making

* Internalising Externalities
	+ Externalities = how my actions affect other's actions
	+ Have to make sure that my actions affect others the least
	+ Can have vertical and horizontal externalities
	+ Vertical relates to upstream divisions and downstream divisions
	+ Horizontal relates to divisions in the same level

* Being aware of interdependencies
	+ Interdependencies = how other's actions affect my actions
	+ Have to be aware that different parts/divisions/etc of organisation have to work together

Improving Decision Making 1

* Managing externalities
	+ How to achieve this: make firm see total profits -> aPi + bPi
	+ Don't make them see Profit of each division individually
	+ maximise profit with combined profit function above to make organisation see externalities of decision they make
* Improving Referrals
	+ Liner has to decide whether to turn over customer to Closer who can close the deal
	+ Depends on whether the customer is easy or tough
	+ Depends on how well they get compensated or not for closing the deal themselves or turning over the customer to the Closer
	+ Remember, the more compensation towards one thing means he will do more of that action. More compensation for transfer, do more transfer. More compensation for close, more close.

Improving Decision Making 2

* Pricing decisions
* Paying based on firm-wide performance can align choices of all managers
* Sometimes may want to correct decision of only one particular decision, can be done through putting cost/subsidy on one decision making task
	+ Transfer pricing - The amount paid by one division to another for goods and services exchanged between them
		- Can redistribute profits across divisions
		- Tax implications
		- Managers are evaluated and rewarded for their efforts
		- Upstream sells products to downstream if selling to downstream gives revenue greater than sum of production cost and opportunity cost(what they would have gotten from selling to market) = c + p
		- no opportunity to sell outside of market for upstream TP = c
		- if perfectly competitive market for input then TP = p
		- if imperfectly competitive market for input then TP = marginal revenue if capacity constrained
	+ Rent seeking, a pitfall of TP
		- figuring out opportunity cost to find TP is hard
		- may be hard to maintain upstream profits and do TP
		- TP may lead to distortion of incentives
		- If upstream is a profit center ie selling directly to market > selling to downstream, then TP will not work as there is low incentive to lower costs and low profits
СмешРайс (прошлая версия игры)

### Начало

   Игра появилась в 2017 году, тогда особой популярности не получила. Имела в себе такие локации как:

* Домик Смешарика (Изменить мебель было нельзя)
* Главная площадь
* Клубный квартал
* Площадь у больнички

   Также в игре был квест "Потоп в СмешРайсе", который остался аж до закрытия игры.

### Популярность

Всё началось с того, как Миксмакс выложил видео на ютуб с названием: "Шарарам 3!? Ещё одна копия Шарарама!" которое в сумме собрало 26, 291 просмотр. Из-за чего популярность игры взлетела вверх, но быстро начало падать из-за малого контента в ней. В 2019 году, видео было скрыто, так как автор посчитал его ужасным.

Далее было видео просто с названием игры, которое выпустил Mars Marsovich,

видео собрало 1161 просмотр.

   Позже канал TheDanMark выложил видео со своим обзором СмешРайса, который после этого попал на официальный канал и собрал 395 просмотров.

Видео с оригинального канала было скрыто, но на канале РеРайса присутствует.

### Инвентарь Смешарика

В инвентаре была:

* Майка лидера
* Синяя кепка
* Зелёный ирокез

* Шляпа тыква

### Закрытие

Позже в 2018 году СмешРайс был закрыт и была выпущена обновлённая версия - РеРайс.

РеРайс - должно быть, странное название? Нет. Окончание Райс сохранилось как память о СмешРайсе, а приставка РЕ является так же приставкой слова (ре)конструкция, (пере)работка"

Концепт СмешРайса взят из Шарарама (детская многопользовательская интернет игра, с ходьбой персонажа, приключениями и кастомизацией игрока).

> Кстати, поиграть в старую версию РеРайса (СмешРайс, или Шарарам9) можно поиграть по этой ссылке : http://shararam9.4mir.net/[1]
>
>
Even More Decentralisation

Minimizing adaptation costs

* Communication between decentralised units is good

Knowledge specialisation

* Epistemological knowledge is justified true belief. Two types: Explicit knowledge, Tacit knowledge.
* Other type of knowledge is practical knowledge which is knowledge used to solve problems.
* Explicit knowledge is knowledge that can be recorded and replicated and understood/communicated easily by others.
* Tacit knowledge is knowledge that is hard to put in words, difficult to transfer, comes from experience.
* A model of knowledge acquisition is needed
* Pr(z<=x) = 2x - x^2 (cos Pr is probability geddit?)
* z is the problem variable
* [0,x] is the time period and 2x - x^2 is the probability that the worker will face the problem
* The likelihood of getting a problem similar to above is the differentiation of the Pr function
* If individual knows how to solve z=x, they can solve problems for z<x. This is cumulative knowledge point of view.
* When there is no linkage between problems, there is no benefit from being able to 'high' problems because the knowledge related to solving 'high' problems are unrelated to solving 'low' problems.
* The cost of learning knowledge is C(A) = czi^2, under the condition that knowledge base is set at A[0,zi].
* Optimal knowledge base? max Pr(z<=x) = 2zi - zi^2 - czi^2 -> 2 - 2zi -2czi = 0 and thus z = 1/(c+1)
* This is the optimal amount of knowledge to learn/gain in this case.

Horzontal knowledge specialisation

* Assuming knowledge is easily written, transferred, explicit, accesible and costless to transfer.
* For this case, non-overlapping knowledge is preferred because knowledge transfer is costless
* For cumulative knowledge in an organisation, only one member specializes in knowledge acquisition
* For loosely-coupled knowledge in an organisation, have several independent experts

Vertical knowledge specialisation

* For the case when knowledge is tacit instead of explicit.
* Islands of knowledge. Workers all have the same knowledge base. They become self sufficient.
* Knowledge hierarchies. Workers refer to other workers problems they can't solve. Using manager's knowledge expensive as it is costly to communicate. Reduces communication as a result.
* Workers have to gain more knowledge if communication is costly and when knowledge of worker increases, communication costs will lessen.
* Referring problems to managers by workers increases knowledge for both parties leading to vertical complimentarity.
SRS

Simple Random Sampling. Sampling without replacement of every possible sample with equal probability.

Taking a random sample from a population with every sample having the same probability of being chosen. Use RNG.

Probability of a sample being chosen = N!/n!(N-n)!

Total Estimate = mean\*N

Variation of sample mean = S^2(1-f)/n

* Std error = sqrt(S^2(1-f)/n)

f = n/N = sampling fraction

S^2 = population mean variance = 1/(N-1)\*sum(yi-y(mean))^2

Finite population correction =
General Stat Notes/Wrong Answer Notes

Write down the topic to trigger the memory

SPSS

* Better handling of subset regression
* Better control of output

EXCEL

* Suspect residual calculations
* Can't do logistical regression
* Limited diagnostics
* Can't handle missing values

Heteroskedasticity - the error variances of a model is not constant

Better than multiple regression/linear regression for binary cases due to assumptions of multiple regression not applying to binary logistic and when interpreting outcome as probability for logistic regression, ordinary regression can give probability of more than 1 or less than 0. Logistic allows right hand side of equation to go from -infinity to +infinity. Logistic function can transform any value into a (0,1) scale.

Types of Time series

* Seasonal - cycles of less than a year
* Cyclical - cycles of more than a year
* Stationary - no trend, seasonal or cyclical effect

The smaller the -2LL the better

The default chi squared(USE THIS FOR -2LL) is 1df

The bigger the difference of -2LL the better

When comparing odd ratio of two things, subtract the e^x from each other and calculate it.

Cut value is when the probability is more than 0.x, it is treated as 1 but less than it it will be treated as zero.

The percentages in the classification table are straightforward. Don't overthink it.

A HLP is a point that has an extreme value of the on or more of the explanatory factors

p value = the probability of observing a value more extreme for an estimate of Beta.i than the one observed

Goodness of fit: R^2 and adjusted R^2, F test, significance of variables

Histogram = normality of model

pp-plot = normality of model

pred vs resid = linearity and contant variance and normality

Residual deviance differences have to be checked against chi^2 value at 1df to see if significant or not. If bigger than chi^2 value, then significant difference.

Scatterplot = shows residual behaviour/homoskedasticity

Histogram = normality of errors

P-P plot/Q-Q plot = residuals normality shown

adjusted r^2 = 1- (residual/resdf)/(total/totdf)

when simplifying for normies, put numbers in and interpret for ease of explanation

collinearity = subset of variables are linearly dependent

leads to inflated standard errors and coefficients ie making the model be meaningless

heteroskedasticity leads to reduced precision of estimates and unreliable prediction

autocorrelation fixed with autoregression, autocorrelation leads to t and F tests not being valid

plots to test assumptions should show outliers if exists

outliers can be recording errors

things to improve model

more variables - think qualitatively and common sense

assumption checking

significance of variables checking

checking model with new data

check for other problems like collinearity and autocorrelation

SPSS uses maximum likelihood estimation to obtain parameter estimates
Weighting

Unit Level Weights

Nh/nh

Class Level Weights

Nh/N

h being stratums

N is total population

nh being the sampled stratas

Nh being the population stratas

Weighting can be used to adjust for nonresponse if nonrespondents have similar opinions to respondents within weighting classes. Can do this by choosing weighting classes which are strongly related to opinions. Past voting behaviour could also be used.
MA207

Math stuff

Do questions extra slowly. One by one. You make arithmetic errors and sign errors.

Wrong Answer Notes MA207

Topics:

Matrix methods in portfolio analysis

Linear Indpendence

Matrix Rank

Matrix Determinant

Eigenvalues and Eigenvectors

Diagonalisation

Linear system of recurrence equations

Second-order recurrence equations

Macroeconomic Models

Quick look at complex numbers

Vector Geometry

Directional Directives and the Gradient Vector

Tangent Hyperplane and Optimal Bundle

Resource Allocation and Pareto Efficiency

Orthogonal Matrices and Quadratic Forms

Quadratic Forms

Stationary Points of Quadratic Functions

Tailor Expansion

Optimisation of function of n variables
Questionnaire Design

Length

Number of questions

Ability to extract sensitive/embarassing information

Cost

Method of distribution

Can it show interviewer bias?

Force a choice or allow neutral?

Start with easy questions. Then move on to asking more difficult questions and asking for personal info.

Hide the sensitive questions in a group of questions.

Provide response categories instead of specific figures.

No loaded questions ever. Never lead the respondent into answering in a way you want. Leading questions give the respondent clues as to what the answer should be.

Is the question necessary?

Can you get the same information from the respondent with less questions?

is the respondent informed?

Can the respondent remember?

Can the respondent articulate?

How much effort does the respondent need to put in?

Is the purpose of the questionnaire clear?

Do the respondents know the context?

Question types to consider: Unstructured, Structured, Multiple Choice, Dichotomous, Scale

Define the issue, who what when where why how

Be unambiguous. Use simple, ordinary words.

Avoid implicit alternatives and assumptions

Avoid generalisations and estimates

Avoid implicit assumptions in the questions. Make clear all the assumptions.

Pilot testing a questionnaire is a good idea.

Also good to have a template or grid of the questionnaire.

Should look professional.
K

K is the eleventh letter of the alphabet.

## Design

K's color is a nearly pure yellow

In Season 2, K's color was darkened in order to make the text easier to read.

## Quotes

"What is going on?" (Comic 1)

"NOOOOO" (Comic 8)

"BLABLA" (Comic 44)

## Trivia

* K is the second character to appear in Alphabex.
* In Comic 40, K's text was darkened, but K wasn't.
Matrix Determinant

Finding determinants of 3x3 and more matrices

Cofactor Method

+-+-+-+-.....

-+-+-+-+.....

.....

1. Choose a row/column
2. Cross it out corresponding column(if row picked) or row(if column picked) to create smaller matrix(2x2 if 3x3)
3. Find determinant of smaller matrices
4. Multiply determinants with +-+/-+- signs as shown above and also multiply is by the elements of row/column cross out(if row/column crossed out was 1,2,3,4 multiply each of the determinants with 1,2,3,4)
5. Sum the above.

Practical Row Operation method

Get the row echelon form. Achieve upper triangular form, meaning anything below the main diagonal is a zero. The diagonal doesn't have to be only composed of ones.

Factors affecting determinant value

Multiplying a row by a non zero number multiplies the determinant by the same number. Multiply row by k -> multiply determinant by k.

Switching the rows around multiplies the determinant by -1.

1. Reduce the matrix to row echelon form. Just form a diagonal.
2. Make appropriate changes to matrix (If row is multiplied by 5, divide entire matrix by 5. If row is switched around, multiply entire matrix by -1)to make up for shifting rows around and multiplying rows by non-zero values.
3. Multiply the value of the diagonal and the values outside the matrix to find the determinant

if determinant == zero ~> Matrix =/ invertible

if matrix =/ invertible ~> unique non-trivial solution

if determinant =/ zero ~> Matrix == invertible

If matrix == invertible ~> trivial solutions and linearly independent
Quadratic Forms

x^TAx = (Pz)^TA(Pz) = z^T(P^TAP)z = z^TDz = lambda1z1^2 + lambda2z2^2 + .... lambdanzn^2

This means we can express matrices in quadratic form. Lambdas are eigenvalues of A and z1,z2 are linear functions of x1,x2 determined by z = P^Tx. x = Pz.

Positive Definiteness: quadratic form of x^TAx is a positive definiteness if x^TAx > 0 for all non-zero vectors of x. As x^TAx = z^TAz, all eigenvalues of x are positive.

If x^TAx < 0, then it is negative definite. If some vectors are positive and some are negative, then it is indefinite.

The eigenvalues of A are roots of the characteristic equation.

matrix A is a symmetric matrix a,c,c,b

lambda1lambda2 = ab - c^2 lmabda1 + lambda 2 = a+b

if IAI > 0 eigenvalues have the same sign

* if a > 0 and lambda1,2 > 0 then + definite
* if a < 0 and lambda 1,2 < 0 then - definite

if IAI < 0 indefinite
Scott Turnbull

On ne sait pas grand chose sur Scott Turnbull. Il n'a pas eu beaucoup de rôles à venir et il était seulement dans un épisode de Wolfblood, apparaissant comme L'assistant Paul du Dr. Whitewood.

Scott a commencé sa carrière dans la dernière série de la BBC une série de télévision pour enfants "Byker Grove" en 2006. Plus tard en 2007, il a joué dans "Better Off Dead", le 41ème épisode de la 23ème série ITV police procédurale Série, le projet de loi. En 2013, il a joué le rôle de L'assistant Paul dans la série de télévision fantastique / surnaturelle CBBB Wolfblood.

## Films

* 2007: The Bill - Jamie Royce
* 2008: The Royal Today - Liam Dooley
* 2013: Wolfblood - L'assistant Paul
Ratio and Regression Estimation

(sample sum of y / sample sum of x)\*total sum of x = ratio estimator \* total sum of x = Btx = total sum of y = ty

ratio estimator of total population y average is = sample y average \* (total population x average/sample x average)

Ratio Estimator = R = sample covariance / (sample s.d. of x \* sample s.d. of y)

Coefficient of Variation estimate = sample s.d. of x / mean of x

If R > CV(X)/CV(Y)\*1/2 then ratio estimator is better

Confidence Interval = memorise the Var(total estimate of y ratio)

The fitted regression line can be calculated using ratio estimates

Intercept B0 = mean y - ratio estimator \* mean of x

Coefficient = covariance(x,y) / variance(x)

Regression estimation applies in a similar setting to ratio estimation but can provide more precise estimation if y is linearly related to x, and the regression intercept is **non-zero**.

Ratio regression works well when there is high correlation between the two things and when the linear regression passes through the origin
Internal Secondary Data

Primary data = data you collect yourself for your research

Secondary data = data that has already been collected by others that you use for your research

Quicker, easier and less expensive to get.

Can help understand consumer data very well. Can use secondary data to refine marketing processes, fill in gaps in knowledge the firms have about consumer. Use various secondary data to build a profile of the consumer.

Things to consider

* Why was the data originally collected?
* What is the accuracy of the data?
* What is the currency of the data?
* How relevant is the data to our purposes?
* How was the data collected?

Examples/Methods

Customer data from: Loyalty Cards, Scanning devices. Mainly Behavioural, Psychographic and Pyschological data.

* lab to experiment consumers
* refine marketing techniques
* fill gaps in firm's knowledge of its consumers
* link behaviour and attitude data

Geodemographic data(age, location, marriage status, kids, occupation, income, social class, etc)

Databases with data

Datamining - discovering meaningful correlations, patterns and trends in data