< >: å x (xm )2 f if X is discrete R¥ Let X be the number of balls that end up in bin 1. Mathematically, given a random variable (r. v.), X, the variance is defined in terms of the Expectation (mean) of X: This equation tells us that the Example: If we roll 10 dice and sum them up, what is the expected value of the result? Theorem 2.2. Random variables are used as a model for data generation processes we want to study. De nition: Let Xbe a continuous random variable with mean . Linearity of Expectation: Let R 1 and R 2 be two discrete random variables on some probability space, then E[R 1 + R 2] = E[R 1] + E[R 2]. where the second equality can be obtained from the linearity property in (a). Abbott ¾ PROPERTY 2: Unbiasedness of βˆ 1 and . The rules can be proven from the definitions above, and are summarised here. For any random variables R 1 and R 2, E[R 1 +R 2] = E[R 1]+E[R 2]. This This depends on independence, whereas linearity of expectation always holds. (a) Let X i be the event that the ith ball falls in bin 1. This tutorial is divided into 4 parts; they are: 1. Expectation & Variance 1 Expectation 1.1 Average & Expected Value Theexpectationofarandomvariableisitsaveragevalue,whereeachvalueisweightedaccording to the probability that it comes up. The expectation is also called theexpected valueor themean of the random variable. If the linear model is true, i.e., if the conditional expectation of Y given X indeed is a linear function of the X j 's, and Y is the sum of that linear function and an independent Gaussian noise, we have the following properties for least squares estimation. You throw m balls into n bins, each independently at random. Remember what you get from the linearity of Expectation, if X is a random variable with expectation/mean µX and variance σ 2 X, and a, b are constants (i.e. The linearity of expectation states that: E[X + Y] = E[X] + E[Y] The core concept of the course is random variable — i.e. the expectation is defined by µX-E(X) = ∫xf(x) dx = ∞ ∞ D. Variance of X: The variance of a random variable X is defined as the expected (average) squared deviation of the values of this random variable about their mean. That is, µ µ σ2 V(X) = E[(X - )2] = E(X 2)− 2 … Tutorial 11: Expectation and Variance of linear combination of random variables Fact 1: For random variable X: a) E[aX+ b] = aE[X] + b b) Var[aX+ b] = a2Var[X] Fact 2: For random variables X 1;X 2;:::;X n: a) The following equation holds for arbitrary random variables X 1;X 2;:::;X n E[X 1 + X 2 + :::+ X n] = E[X 1] + E[X 2] + :::+ E[X n] b) If X 1;X 2;:::;X The expected value of a random variable is essentially a weighted average of possible outcomes. 1) 1 E(βˆ =βThe OLS coefficient estimator βˆ 0 is unbiased, meaning that . Theorem 8 (Conditional Expectation and Conditional Variance) Let X and Y be ran-dom variables. + is a random variable –it’s a function that outputs a number given an outcome (or, here, a combination of outcomes). 2.3. We often see in machine learning textbooks the image below describing the generalization (test) error and its connection to model complexity. Variance is a statistic that is used to measure deviation in a probability distribution. Definition: Let X be any random variable. Consider two random variables: X and Y with means and … 2. variable whose values are determined by random experiment. where the second equality can be obtained from the linearity property in (a). The variance of a random variable X, or the variance of the probability distribution of X, is de ned as the expected squared deviation from the expected value. Proof. As stated already, linearity of expectation allows us to compute the expected value of a sum of random variables by computing the sum of the individual expectations. Seahawks Schedule 2021-2022 Printable, Brach's Conversation Hearts Flavors Colors, A Capital Loss Can Be Quizlet Real Estate, Obsolete Sailing Vessel Crossword Clue, Female Quarter Sleeve Tattoosforearm, Rockwool Insulation Advantages And Disadvantages, Related Words For Profession, England World Cup Qualifying Group Table, " /> < >: å x (xm )2 f if X is discrete R¥ Let X be the number of balls that end up in bin 1. Mathematically, given a random variable (r. v.), X, the variance is defined in terms of the Expectation (mean) of X: This equation tells us that the Example: If we roll 10 dice and sum them up, what is the expected value of the result? Theorem 2.2. Random variables are used as a model for data generation processes we want to study. De nition: Let Xbe a continuous random variable with mean . Linearity of Expectation: Let R 1 and R 2 be two discrete random variables on some probability space, then E[R 1 + R 2] = E[R 1] + E[R 2]. where the second equality can be obtained from the linearity property in (a). Abbott ¾ PROPERTY 2: Unbiasedness of βˆ 1 and . The rules can be proven from the definitions above, and are summarised here. For any random variables R 1 and R 2, E[R 1 +R 2] = E[R 1]+E[R 2]. This This depends on independence, whereas linearity of expectation always holds. (a) Let X i be the event that the ith ball falls in bin 1. This tutorial is divided into 4 parts; they are: 1. Expectation & Variance 1 Expectation 1.1 Average & Expected Value Theexpectationofarandomvariableisitsaveragevalue,whereeachvalueisweightedaccording to the probability that it comes up. The expectation is also called theexpected valueor themean of the random variable. If the linear model is true, i.e., if the conditional expectation of Y given X indeed is a linear function of the X j 's, and Y is the sum of that linear function and an independent Gaussian noise, we have the following properties for least squares estimation. You throw m balls into n bins, each independently at random. Remember what you get from the linearity of Expectation, if X is a random variable with expectation/mean µX and variance σ 2 X, and a, b are constants (i.e. The linearity of expectation states that: E[X + Y] = E[X] + E[Y] The core concept of the course is random variable — i.e. the expectation is defined by µX-E(X) = ∫xf(x) dx = ∞ ∞ D. Variance of X: The variance of a random variable X is defined as the expected (average) squared deviation of the values of this random variable about their mean. That is, µ µ σ2 V(X) = E[(X - )2] = E(X 2)− 2 … Tutorial 11: Expectation and Variance of linear combination of random variables Fact 1: For random variable X: a) E[aX+ b] = aE[X] + b b) Var[aX+ b] = a2Var[X] Fact 2: For random variables X 1;X 2;:::;X n: a) The following equation holds for arbitrary random variables X 1;X 2;:::;X n E[X 1 + X 2 + :::+ X n] = E[X 1] + E[X 2] + :::+ E[X n] b) If X 1;X 2;:::;X The expected value of a random variable is essentially a weighted average of possible outcomes. 1) 1 E(βˆ =βThe OLS coefficient estimator βˆ 0 is unbiased, meaning that . Theorem 8 (Conditional Expectation and Conditional Variance) Let X and Y be ran-dom variables. + is a random variable –it’s a function that outputs a number given an outcome (or, here, a combination of outcomes). 2.3. We often see in machine learning textbooks the image below describing the generalization (test) error and its connection to model complexity. Variance is a statistic that is used to measure deviation in a probability distribution. Definition: Let X be any random variable. Consider two random variables: X and Y with means and … 2. variable whose values are determined by random experiment. where the second equality can be obtained from the linearity property in (a). The variance of a random variable X, or the variance of the probability distribution of X, is de ned as the expected squared deviation from the expected value. Proof. As stated already, linearity of expectation allows us to compute the expected value of a sum of random variables by computing the sum of the individual expectations. Seahawks Schedule 2021-2022 Printable, Brach's Conversation Hearts Flavors Colors, A Capital Loss Can Be Quizlet Real Estate, Obsolete Sailing Vessel Crossword Clue, Female Quarter Sleeve Tattoosforearm, Rockwool Insulation Advantages And Disadvantages, Related Words For Profession, England World Cup Qualifying Group Table, " /> < >: å x (xm )2 f if X is discrete R¥ Let X be the number of balls that end up in bin 1. Mathematically, given a random variable (r. v.), X, the variance is defined in terms of the Expectation (mean) of X: This equation tells us that the Example: If we roll 10 dice and sum them up, what is the expected value of the result? Theorem 2.2. Random variables are used as a model for data generation processes we want to study. De nition: Let Xbe a continuous random variable with mean . Linearity of Expectation: Let R 1 and R 2 be two discrete random variables on some probability space, then E[R 1 + R 2] = E[R 1] + E[R 2]. where the second equality can be obtained from the linearity property in (a). Abbott ¾ PROPERTY 2: Unbiasedness of βˆ 1 and . The rules can be proven from the definitions above, and are summarised here. For any random variables R 1 and R 2, E[R 1 +R 2] = E[R 1]+E[R 2]. This This depends on independence, whereas linearity of expectation always holds. (a) Let X i be the event that the ith ball falls in bin 1. This tutorial is divided into 4 parts; they are: 1. Expectation & Variance 1 Expectation 1.1 Average & Expected Value Theexpectationofarandomvariableisitsaveragevalue,whereeachvalueisweightedaccording to the probability that it comes up. The expectation is also called theexpected valueor themean of the random variable. If the linear model is true, i.e., if the conditional expectation of Y given X indeed is a linear function of the X j 's, and Y is the sum of that linear function and an independent Gaussian noise, we have the following properties for least squares estimation. You throw m balls into n bins, each independently at random. Remember what you get from the linearity of Expectation, if X is a random variable with expectation/mean µX and variance σ 2 X, and a, b are constants (i.e. The linearity of expectation states that: E[X + Y] = E[X] + E[Y] The core concept of the course is random variable — i.e. the expectation is defined by µX-E(X) = ∫xf(x) dx = ∞ ∞ D. Variance of X: The variance of a random variable X is defined as the expected (average) squared deviation of the values of this random variable about their mean. That is, µ µ σ2 V(X) = E[(X - )2] = E(X 2)− 2 … Tutorial 11: Expectation and Variance of linear combination of random variables Fact 1: For random variable X: a) E[aX+ b] = aE[X] + b b) Var[aX+ b] = a2Var[X] Fact 2: For random variables X 1;X 2;:::;X n: a) The following equation holds for arbitrary random variables X 1;X 2;:::;X n E[X 1 + X 2 + :::+ X n] = E[X 1] + E[X 2] + :::+ E[X n] b) If X 1;X 2;:::;X The expected value of a random variable is essentially a weighted average of possible outcomes. 1) 1 E(βˆ =βThe OLS coefficient estimator βˆ 0 is unbiased, meaning that . Theorem 8 (Conditional Expectation and Conditional Variance) Let X and Y be ran-dom variables. + is a random variable –it’s a function that outputs a number given an outcome (or, here, a combination of outcomes). 2.3. We often see in machine learning textbooks the image below describing the generalization (test) error and its connection to model complexity. Variance is a statistic that is used to measure deviation in a probability distribution. Definition: Let X be any random variable. Consider two random variables: X and Y with means and … 2. variable whose values are determined by random experiment. where the second equality can be obtained from the linearity property in (a). The variance of a random variable X, or the variance of the probability distribution of X, is de ned as the expected squared deviation from the expected value. Proof. As stated already, linearity of expectation allows us to compute the expected value of a sum of random variables by computing the sum of the individual expectations. Seahawks Schedule 2021-2022 Printable, Brach's Conversation Hearts Flavors Colors, A Capital Loss Can Be Quizlet Real Estate, Obsolete Sailing Vessel Crossword Clue, Female Quarter Sleeve Tattoosforearm, Rockwool Insulation Advantages And Disadvantages, Related Words For Profession, England World Cup Qualifying Group Table, " />
To Top
Kezdőlap
Close
Bemutatkozás
Szakterület
Partnerek
Kapcsolat
13
jún 2021
No Comments
Egyéb
Permalink

linearity of expectation variance

Chapter 2, variance of linear regression model when actual data relationship is linear. You should learn these rules and practice using them in questions. Since is a function, say , of , we can define as the function of the random variable . Theorem 1.5. Linearity of expectation: If Xand Yare random variables and a;b2R, then E[aX+bY] = aE[X]+bE[Y] 1. Variance The variance of a random variable tells us something about the spread of the possible values of the variable. ECONOMICS 351* -- NOTE 4 M.G. At this point, it is important to settle down some intuitive knowledge about the Variance itself. [This says that expectation is a linear operator]. Expected Value of sum of 2 dice throws = 2*(Expected value of one dice throw) = 2*(1/6 + 2/6 + .... 6/6) = 2*7/2 = 7 Expected value of sum for n dice throws is = n * 7/2 = 3.5 * n Then prove the next inequality about the variance $V(X)$. The expectation describes the average value and the variance describes the spread (amount of variability) around the expectation. A way to summarize a random variable Expectation is linear + = +[ ]. For example, in decision theory, an agent making an optimal choice in the context of incomplete information is often assumed to maximize the expected value of their utility function. The variance measures how far the values of X are from their mean, on average. www.cs.cornell.edu/courses/cs2800/2017fa/lectures/lec09-expect.html In such settings, a desirable criterion for a "good" estimator is that it is unbiased; that is, the expected value of the estimate is equal to the true value of the underlying parameter. 1. From Equation 3.6, we conclude that, for standard deviation, SD ( a X + b) = | a | SD ( X). We mentioned that variance is NOT a linear operation. But there is a very important case, in which variance behaves like a linear operation and that is when we look at sum of independent random variables. The expectation and variance operators obey certain very valuable rules. Variance of X † Consider the following three distributions: fX(x) = (1 x = 0 0 otherwise fY (y) = (1=2 y = ¡1;1 0 otherwise fZ(z) = (1=2 z = ¡100;100 0 otherwise † What are the expectations of these distributions? Now that we’ve de ned expectation for continuous random variables, the de nition of vari-ance is identical to that of discrete random variables. We are often interested in the expected value of a sum of random variables. 2. var(X) = E[var(X | Y)]+var[E(X | Y)]. These properties are useful when deriving the mean and variance of a random variable that … Linearity of Expectation Variance and Standard Deviation Random Variables Motivation In case of certain random experiments, we are not so much interested in the actual outcome, but in some function of the outcome, e.g., in the experiment of tossing two dice, we could be interested in knowing whether or not the the sum of the upturned faces is 7. The variance of X is Var(X) = E (X − µ X) 2 = E(X )− E(X) . So the variance is Var(X) = E (X 1 2)2 = Z 1 0 x 1 2 2 (1)dx = Z 1 0 x2 x+ 1 4 dx = x3 3 x2 2 + x 4 1 x=0 = 1 3 1 2 + 1 4 = 1 12 The standard deviation of X, denoted ˙, is the (non-negative) square root of ˙2. So to prove this statement first, consider that by linearity of expectation, we have that the expected value of a X must be why plus C is the same as eight times the expected value of X plus B times the expected value of why plus C. Therefore, it follows that the co variance of a X plus B y plus C Z. Prove the linearity of expectations described as \[E(X+Y) = E(X) + E(Y).\] Solution. not random), then: E(aX + b) = aµX + b. we can see more clearly that the sample mean is a linear combination of the random variables \(X_1, X_2, \ldots, X_n\). Linearity of expectation is the property that the expected value of the sum of random variables is equal to the sum of their individual expected values, regardless of whether they are independent. ts is, by linearity of expectation, n t 21(t 2) since each t vertices we pick has t 2 edges and there are only 2 ways to color them to form a monochromatic K t. Thus, there is a positive probability that the number of monochromatic K t is at most this number. Then, using linearity of expectation, = E ( a 2 X 2) + E ( − 2 a 2 X E ( X)) + E ( a 2 ( E ( X)) 2) scaling of expectation, and that the expectation of the mean is the mean itself, = a 2 E ( X 2) − 2 a 2 ( E ( X)) 2 + a 2 ( E ( X)) 2. If X has high variance, we can observe values of X a long way from the mean. Expected Let T ::=R 1 +R 2. Let X and Y be random variables, and let k be a constant. Properties of the data are deeply linked to the corresponding properties of random variables, such as expected value, variance and correlations. † Clearly Z is … 1. The variance{covariance matrix (or simply the covariance matrix) of a random vector X~ is given by: Cov(X~) = E h (X~ TEX~)(X~ EX~) i: Proposition 4. Then: (Law of Iterated Expectation) E(X) = E[E(X | Y)]. In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.In other words, it measures how far a set of numbers is spread out from their average value. edited Oct 23 '17 at 20:14. Given a random variable, we often compute the expectation and variance, two important summary statistics. 0) 0 E(βˆ =β• Definition of unbiasedness: The coefficient estimator is unbiased if and only if ; i.e., its mean or expectation is equal to the true coefficient β The variance is the mean squared deviation of a random variable from its own mean. This implies the probability $P(0 \leq X \leq c) = 1$. Deviation is the tendency of outcomes to differ from the expected value. Let X be a random variable that takes values only between 0 and c. This implies the probability P ( 0 ≤ X ≤ c) = 1. Then prove the next inequality about the variance V ( X) . V ( X) ≤ c 2 4. Proof. Recall that […] Linearity of Expectations E (X+Y) = E (X) + E (Y) Let X, Y be discrete random variables. (m) Variance of Independent Variables: If X is independent of Y, Var(X +Y) = Var(X)+Var(Y). 4 Variance. Let X 1;:::;X n be any nite collection of discrete random variables and let X= P n i=1 X i. Expectation and Variance. Variance & Standard Deviation Let X be a random variable with probability distribution f(x) and mean m. The variance of X is s2 =Var(X) =E h (X m)2 i =E h (X E(X))2 i = 8 >< >: å x (xm )2 f if X is discrete R¥ Let X be the number of balls that end up in bin 1. Mathematically, given a random variable (r. v.), X, the variance is defined in terms of the Expectation (mean) of X: This equation tells us that the Example: If we roll 10 dice and sum them up, what is the expected value of the result? Theorem 2.2. Random variables are used as a model for data generation processes we want to study. De nition: Let Xbe a continuous random variable with mean . Linearity of Expectation: Let R 1 and R 2 be two discrete random variables on some probability space, then E[R 1 + R 2] = E[R 1] + E[R 2]. where the second equality can be obtained from the linearity property in (a). Abbott ¾ PROPERTY 2: Unbiasedness of βˆ 1 and . The rules can be proven from the definitions above, and are summarised here. For any random variables R 1 and R 2, E[R 1 +R 2] = E[R 1]+E[R 2]. This This depends on independence, whereas linearity of expectation always holds. (a) Let X i be the event that the ith ball falls in bin 1. This tutorial is divided into 4 parts; they are: 1. Expectation & Variance 1 Expectation 1.1 Average & Expected Value Theexpectationofarandomvariableisitsaveragevalue,whereeachvalueisweightedaccording to the probability that it comes up. The expectation is also called theexpected valueor themean of the random variable. If the linear model is true, i.e., if the conditional expectation of Y given X indeed is a linear function of the X j 's, and Y is the sum of that linear function and an independent Gaussian noise, we have the following properties for least squares estimation. You throw m balls into n bins, each independently at random. Remember what you get from the linearity of Expectation, if X is a random variable with expectation/mean µX and variance σ 2 X, and a, b are constants (i.e. The linearity of expectation states that: E[X + Y] = E[X] + E[Y] The core concept of the course is random variable — i.e. the expectation is defined by µX-E(X) = ∫xf(x) dx = ∞ ∞ D. Variance of X: The variance of a random variable X is defined as the expected (average) squared deviation of the values of this random variable about their mean. That is, µ µ σ2 V(X) = E[(X - )2] = E(X 2)− 2 … Tutorial 11: Expectation and Variance of linear combination of random variables Fact 1: For random variable X: a) E[aX+ b] = aE[X] + b b) Var[aX+ b] = a2Var[X] Fact 2: For random variables X 1;X 2;:::;X n: a) The following equation holds for arbitrary random variables X 1;X 2;:::;X n E[X 1 + X 2 + :::+ X n] = E[X 1] + E[X 2] + :::+ E[X n] b) If X 1;X 2;:::;X The expected value of a random variable is essentially a weighted average of possible outcomes. 1) 1 E(βˆ =βThe OLS coefficient estimator βˆ 0 is unbiased, meaning that . Theorem 8 (Conditional Expectation and Conditional Variance) Let X and Y be ran-dom variables. + is a random variable –it’s a function that outputs a number given an outcome (or, here, a combination of outcomes). 2.3. We often see in machine learning textbooks the image below describing the generalization (test) error and its connection to model complexity. Variance is a statistic that is used to measure deviation in a probability distribution. Definition: Let X be any random variable. Consider two random variables: X and Y with means and … 2. variable whose values are determined by random experiment. where the second equality can be obtained from the linearity property in (a). The variance of a random variable X, or the variance of the probability distribution of X, is de ned as the expected squared deviation from the expected value. Proof. As stated already, linearity of expectation allows us to compute the expected value of a sum of random variables by computing the sum of the individual expectations.

Seahawks Schedule 2021-2022 Printable, Brach's Conversation Hearts Flavors Colors, A Capital Loss Can Be Quizlet Real Estate, Obsolete Sailing Vessel Crossword Clue, Female Quarter Sleeve Tattoosforearm, Rockwool Insulation Advantages And Disadvantages, Related Words For Profession, England World Cup Qualifying Group Table,

Vélemény, hozzászólás?

Az email címet nem tesszük közzé. A kötelező mezőket * karakterrel jelöljük.

0-24

Annak érdekében, hogy akár hétvégén vagy éjszaka is megfelelő védelemhez juthasson, telefonos ügyeletet tartok, melynek keretében bármikor hívhat, ha segítségre van szüksége.

 Tel.: +36702062206

×
Büntetőjog

Amennyiben Önt letartóztatják, előállítják, akkor egy meggondolatlan mondat vagy ésszerűtlen döntés később az eljárás folyamán óriási hátrányt okozhat Önnek.

Tapasztalatom szerint már a kihallgatás első percei is óriási pszichikai nyomást jelentenek a terhelt számára, pedig a „tiszta fejre” és meggondolt viselkedésre ilyenkor óriási szükség van. Ez az a helyzet, ahol Ön nem hibázhat, nem kockáztathat, nagyon fontos, hogy már elsőre jól döntsön!

Védőként én nem csupán segítek Önnek az eljárás folyamán az eljárási cselekmények elvégzésében (beadvány szerkesztés, jelenlét a kihallgatásokon stb.) hanem egy kézben tartva mérem fel lehetőségeit, kidolgozom védelmének precíz stratégiáit, majd ennek alapján határozom meg azt az eszközrendszert, amellyel végig képviselhetem Önt és eredményül elérhetem, hogy semmiképp ne érje indokolatlan hátrány a büntetőeljárás következményeként.

Védőügyvédjeként én nem csupán bástyaként védem érdekeit a hatóságokkal szemben és dolgozom védelmének stratégiáján, hanem nagy hangsúlyt fektetek az Ön folyamatos tájékoztatására, egyben enyhítve esetleges kilátástalannak tűnő helyzetét is.

×
Polgári jog

Jogi tanácsadás, ügyintézés. Peren kívüli megegyezések teljes körű lebonyolítása. Megállapodások, szerződések és az ezekhez kapcsolódó dokumentációk megszerkesztése, ellenjegyzése. Bíróságok és más hatóságok előtti teljes körű jogi képviselet különösen az alábbi területeken:

×
Ingatlanjog

Ingatlan tulajdonjogának átruházáshoz kapcsolódó szerződések (adásvétel, ajándékozás, csere, stb.) elkészítése és ügyvédi ellenjegyzése, valamint teljes körű jogi tanácsadás és földhivatal és adóhatóság előtti jogi képviselet.

Bérleti szerződések szerkesztése és ellenjegyzése.

Ingatlan átminősítése során jogi képviselet ellátása.

Közös tulajdonú ingatlanokkal kapcsolatos ügyek, jogviták, valamint a közös tulajdon megszüntetésével kapcsolatos ügyekben való jogi képviselet ellátása.

Társasház alapítása, alapító okiratok megszerkesztése, társasházak állandó és eseti jogi képviselete, jogi tanácsadás.

Ingatlanokhoz kapcsolódó haszonélvezeti-, használati-, szolgalmi jog alapítása vagy megszüntetése során jogi képviselet ellátása, ezekkel kapcsolatos okiratok szerkesztése.

Ingatlanokkal kapcsolatos birtokviták, valamint elbirtoklási ügyekben való ügyvédi képviselet.

Az illetékes földhivatalok előtti teljes körű képviselet és ügyintézés.

×
Társasági jog

Cégalapítási és változásbejegyzési eljárásban, továbbá végelszámolási eljárásban teljes körű jogi képviselet ellátása, okiratok szerkesztése és ellenjegyzése

Tulajdonrész, illetve üzletrész adásvételi szerződések megszerkesztése és ügyvédi ellenjegyzése.

×
Állandó, komplex képviselet

Még mindig él a cégvezetőkben az a tévképzet, hogy ügyvédet választani egy vállalkozás vagy társaság számára elegendő akkor, ha bíróságra kell menni.

Semmivel sem árthat annyit cége nehezen elért sikereinek, mint, ha megfelelő jogi képviselet nélkül hagyná vállalatát!

Irodámban egyedi megállapodás alapján lehetőség van állandó megbízás megkötésére, melynek keretében folyamatosan együtt tudunk működni, bármilyen felmerülő kérdés probléma esetén kereshet személyesen vagy telefonon is.  Ennek nem csupán az az előnye, hogy Ön állandó ügyfelemként előnyt élvez majd időpont-egyeztetéskor, hanem ennél sokkal fontosabb, hogy az Ön cégét megismerve személyesen kezeskedem arról, hogy tevékenysége folyamatosan a törvényesség talaján maradjon. Megismerve az Ön cégének munkafolyamatait és folyamatosan együttműködve vezetőséggel a jogi tudást igénylő helyzeteket nem csupán utólag tudjuk kezelni, akkor, amikor már „ég a ház”, hanem előre felkészülve gondoskodhatunk arról, hogy Önt ne érhesse meglepetés.

×