CREDIBILITY - PROBLEM SET 1 Limited Fluctuation Credibility

Size: px
Start display at page:

Download "CREDIBILITY - PROBLEM SET 1 Limited Fluctuation Credibility"

Transcription

1 CREDIBILITY PROBLEM SET 1 Limited Fluctuation Credibility 1 The criterion for the number of exposures needed for full credibility is changed from requiring \ to be ithin IÒ\Ó ith probability Þ*, to requiring \ to be ithin 5IÒ\Ó ith probability Þ*& Find the value of 5 that results in no change in the standard for full credibility for number of exposures of \ A) 0524 B) 0548 C) 0572 D) 0596 E) Total claim amount per period W follos a compound Poisson claims distribution The standard for full credibility for total claims in a period W based on number of claims is 1500 claims It is then discovered that an incorrect value of the coefficient of variation for the severity distribution ] as used to determine the full credibility standard The original coefficient of variation used as Þ', but the corrected coefficient of variation for ] is Þ&!! Find the corrected standard for full credibility for W based on number of claims A) 1300 B) 1325 C) 1350 D) 1375 E) The partial credibility factor for random variable \ based on!! exposures of \ is ^ Þ%! Ho many additional exposures are needed to increase the partial credibility factor to at least Þ&!? A) 55 B) 56 C) 57 D) 58 E) 59 4 Total claims per period W follos a compound Poisson distribution and claim severity has the ' pdf 0ÐCÑ&C, for C A full credibility standard based on number of exposures of W needed has been determined so that the total cost of claims per period is ithin 5% of the expected cost ith a probability of 90% If the same number of exposures for full credibility of total cost is applied to the number of exposures needed for the frequency variable R, the actual number of claims per exposure period ould be ithin 100 < % of the expected number of claims per exposure period ith probability 95% Find < A) 054 B) 058 C) 062 D) 066 E) An analysis of credibility premiums is being done for a particular compound Poisson claims distribution, here the criterion is that the total cost of claims is ithin 5% of the expected cost of claims ith a probability of 90% It is found that ith 8'! exposures (periods) and \ )!Þ!, the credibility premium is )*Þ%( After 20 more exposures (for a total of 80) and revised \ )&, the credibility premium is *!Þ)) After 20 more exposures (for a total of 100) the revised \ is )(Þ& Assuming that the manual premium remains unchanged in all cases, and assuming that full credibility has not been reached in any of the cases, find the credibility premium for the 100 exposure case A) 1915 B) 1925 C) 1935 D) 1945 E) 1965

2 6 Total claims per period W has a compound Poisson distribution You have determined that a sample size of 2670 claims is necessary for full credibility for total claims per period if the severity distribution is constant If the severity distribution is lognormal ith mean 1000 and variance 1,500,000, find the number of claims needed for full credibility of total claims per period A) 6650 B) 6675 C) 6700 D) 6725 E) You are given the folloing: The number of claims follos a Poisson distribution The variance of the number of claims is 10 The variance of the claim size distribution is 10 The variance of aggregate claim costs is 500 The number of claims and claim sizes are independent The full credibility standard has been selected so that actual aggregate claim costs per period ill be ithin 5% of expected aggregate claim costs 95% of the time Using the methods of limited fluctuation credibility determine the number of claims required for full credibility of aggregate claim costs per period 8 You are given the folloing: The number of claims per period follos a Poisson distribution Claim sizes follo a lognormal distribution ith parameters (unknon) and 5 % The number of claims and claim sizes are independent 6,600 expected claims are needed for full credibility of aggregate claims per period The full credibility standard has been selected so that actual aggregate claim costs per period ill be ithin 10% of expected aggregate claim costs per period T % of the time Using the methods of limited fluctuation credibility to determine the value of T 9 W has a compound distribution ith frequency R and severity ] R and all claim amounts are independent of one another Limited fluctuation credibility is being applied to W, ith the full credibility standard based on the sample mean of W being ithin 5% of the true mean of W ith probability 90% The folloing information is given regarding the three equivalent full credibility standards for W The expected number of exposures of W needed for full credibility is The expected aggregate amount of claim needed for full credibility is 10,824 The expected total number of claims needed for full credibility is 5412 Find all of the folloing quantities: ß Z +<ÐWÑß IÐRÑ and IÐ] Ñ Þ Sho that R cannot have a Poisson distribution and ] cannot have an exponential distribution

3 10 R is the distribution of the number of claims occuring per eek R has a Poisson distribution ith an unknon mean The standard for full credibility for R is based on the sample mean of R being ithin 5% of the true mean of R ith probability 90% With 400 observed claims in 20 eeks, the credibility premium based on partial credibility is T With 500 observed claims in 30 eeks, the credibility premium based on partial credibility is T Þ* Find the credibility premium based on partial credibility if there are 550 observed claims in 35 eeks Assume that the same manual premium is used in all cases 11 You are given: (i) \:+<>3+6 pure premium calculated from partially credible data (ii) IÒ\ :+<>3+6Ó (iii) Fluctuations are limited to 5 of the mean ith probability T (iv) ^credibility factor Which of the folloing is equal to T? (A) T<Ò 5 Ÿ \:+<>3+6 Ÿ 5 Ó (B) T<Ò^ 5 Ÿ Z\ :+<>3+6 Ÿ ^ 5Ó (C) T<Ò^ Ÿ Z \:+<>3+6 Ÿ^ Ó (D) T<Ò5Ÿ Z\ :+<>3+6 Ð^Ñ Ÿ5Ó (E) T<Ò 5 Ÿ Z\ Ð^Ñ Ÿ 5 Ó :+<> (SOA) You are given: (i) Claim counts follo a Poisson distribution (ii) Claim sizes follo a lognormal distribution ith coefficient of variation 3 (iii) Claim sizes and claim counts are independent (iv) The number of claims in the first year as 1000 (v) The aggregate loss in the first year as 675 million (vi) The manual premium for the first year as 500 million (vii) The exposure in the second year is identical to the exposure in the first year (viii) The full credibility standard is to be ithin 5% of the expected aggregate loss 95% of the time Determine the limited fluctuation credibility net premium (in millions) for the second year (A) Less than 55 (B) At least 55, but less than 57 (C) At least 57, but less than 59 (D) At least 59, but less than 61 (E) At least An insurer has to separate classes of policies The characteristics of the loss per insured in each of the to classes during a one year period are as follos: Class I: Expected claim per insured is 100 To be ithin 5% of expected loss 90% of the time, the standard for number of insureds needed for full credibility is Class II: Expected claim per insured is 200 To be ithin 5% of expected loss 90% of the time, the standard for number of insureds needed for full credibility is Class I has tice the number of insureds as Class II The to classes of insureds are combined and regarded as a single class ith the appropriate adjusted loss per insured during a one year period Find the full credibility standard for the minimum number of insureds required in the combined portfolio, here the full credibility is to be ithin 5% of expected loss 90% of the time (A) (B) (C) (D) (E) 13530

4 14 The partial credibility approach is applied to a data set of 50 claim amounts It is assumed that the claim amount distribution is uniform on the interval Ò!ß ) Ó The full credibility standard is to be ithin 5% of the expected claim amount 90% of the time The partial credibility factor ^ is found After 25 additional claim amounts are recorded, the claim amount distribution is revised to be uniform on the interval Ò!ß Þ) Ó The revised partial credibility factor ^ is found Find the ratio ^Î^ (A) (B) É Þ& Þ& (C) 1 (D) ÈÞ& (E) Þ& 15 Number of claims per year follos a Poisson distribution A number of claims are recorded over a specified period of time A full credibility standard is set so as to be ithin 5% of expected claims per year 90% of the time Based on the observed number of claims, the full credibility standard is not met, but the partial credibility factor is ^Þ') Find the maximum value of 5 so that this same number of claims satisfies a full credibility standard ithin 10% of expected claims per year 5% of the time (A) 995 (B) 99 (C) 98 (D) 975 (E) W has a compound distribution ith frequency R and severity ] R and all claim amounts are independent of one another Limited fluctuation credibility is being applied to W, ith the full credibility standard based on the sample mean of W being ithin 5% of the true mean of W ith probability 90% The folloing information is given regarding the three equivalent full credibility standards for W The expected number of exposures of W needed for full credibility is The expected aggregate amount of claim needed for full credibility is 10,824 The expected total number of claims needed for full credibility is 5412 Find all of the folloing quantities: ß Z +<ÐWÑß IÐRÑ and IÐ] Ñ Þ 17 The aggregate loss in one eek, W, follos a compound negative binomial distribution, and the severity distribution is exponential Limited fluctuation credibility is being applied to W so that the full credulity standard is to be ithin 5% of expected aggregate losses 95% of the time It is found that the expected number of claims needed for full credibility is 5,412 Suppose that the frequency distribution is modified (but still negative binomial) so that mean and variance of the frequency both increase by 20% Find the full credibility standard for the number of claims needed for the ne compound negative binomial distribution (severity is the same exponential distribution as before)

5 18 A compound distribution ÐWÑ has a Poisson frequency distribution ÐR Ñ ith mean For parts (a) and (b), assume that the severity distribution Ð] Ñ is uniform on the interval Ò!ß ) Ó (a) Limited fluctuation credibility is applied to ] based on the sample mean of ] being ithin 5% of the true mean of ] ith probability 90% Find expressions for (i) the expected number of observations of ] needed for full credibility, and (ii) the expected sum of the observed values of ] needed for full credibility (b) Limited fluctuation readability is applied to W based on the sample mean of W being ithin 5% of the true mean of ] ith probability 90% Find expressions for (i) the expected number of observations of W needed for full credibility, and (ii) the expected sum of the observed values of W needed for full credibility, and (iii) the expected total number of claims needed for full credibility 19 [ is a random variable ith mean IÒ[ Ó and variance Z +<Ò[ Ó In a partial credibility analysis of [, the manual premium used is Q!!! A sample of 350 observations of [ is available and the sum of the observed values is 300,000 Partial credibility is applied to determine a credibility premium based on the 5% closeness and 90% probability criteria If the credibility standard used is the one based on the expected number of observations of [ needed, then the partial readability premium is If the credibility standard used is the one based on the expected sum of the observed values of [ needed, then the partial readability premium is Using this information, find the mean and variance of [

6 CREDIBILITY PROBLEM SET 1 SOLUTIONS 1 Since IÒ\Ó and 5 Z+<Ò\Ó, are unchanged, the standard for full credibility ill be C unchanged if 8! Ð 5 Ñ is unchanged With 5 ß T Þ* e have CÞ* Þ'%&, and C Þ'%& Þ*& 5 $Þ* With : Þ*&, CÞ*& is the Þ*(& percentile of the standard normal distribution, so that CÞ*& Þ*' In order for 8! to remain unchanged, e must have Þ*' 5 $Þ* p 5 *' Anser: D 2 For the compound Poisson distribution ith Poisson parameter (frequency distribution or number of claims per period) and claim amount distribution ] (severity distribution or amount per claim), the standard for full credibility for expected number of claims is 5 8! Ò ÐIÒ] ÓÑ ] 5] Ó 8! Ò Ð Ñ Ó Thus, ith Þ' ß ] ] 5 8 Ò Ð Ñ Ó Þ$)&)8 &!! With the coefficient of variation of ] changed to! ]! ] 5] 5]! ]! ] Þ5200, e have 8 Ò Ð Ñ Ó Þ(!%8 $(& Anser: D 3 The partial credibility factor ith 8!! is ^ Þ%! É!! 8, here 8J is the full J credibility standard for number of exposures needed The credibility factor ith 8!! 5 is ^ Þ&! É!!5 Then, É!!5 Þ&!! Þ% Þ& p 5 &'Þ& p &( Anser: C 8 J 4 The severity distribution has mean IÒ] Ó ' ' & C &C C % ß and IÒ] Ó ' ' & & & & C &C C $ ß so that Z +<Ò] Ó % Ð $ Ñ %) Þ Þ'%& With < and : Þ*, e have 8! Ð Ñ!)Þ% Þ The full credibility standard for 8! number of exposures needed for the compound Poisson distribution is Ò ÐIÒ] ÓÑ Ó The full credibility standard for the number of exposures needed for the Poisson frequency 8! distribution only is 8 Since e are considering the same Poisson frequency distribution, the value of (hich is not knon) stays the same If the same value of 8 for full credibility from the aggregate compound Poisson distribution is applied to the Poisson frequency 8! 8! distribution alone, then e set Ò ÐIÒ] ÓÑ Ó and the 8! for the Poisson frequency credibility standard must change, hich is hy it has been denoted 8! 8! 8! Z +<Ò] Ó!)Þ% &Î%) Then Ò ÐIÒ] ÓÑ Ó Ò Ð&Î%Ñ Ó p 8! &%Þ&( With T Þ*&, C: Þ*', and then in order for this to be the proper 8! for T Þ*&, e must Þ*' have &%Þ&( Ð < Ñ p < (( Anser: B

7 5 For the 60 exposure case, the credibility premium is )*Þ%( )!^'! QÐ ^'! Ñ, and for the 80 exposure case, *!Þ)) )&^)! QÐ ^)! Ñ We ish to find )(Þ&^!! QÐ ^!! Ñ In going from 60 to 80 exposures, the credibility factor changes from '! )! ^'! to ^)! (here ] is the severity distribution) 8 8 Ë! Ë! Œ Œ ÐIÒ] ÓÑ ÐIÒ] ÓÑ ^)! Thus, ^ É )! '! Þ&%(, and the to credibility premium equations become '! )*Þ%( )!^'! QÐ ^'! Ñ, *!Þ)) $Þ'^6! QÐ Þ&%(^'! Ñ )*Þ%()!^'! ^'! Juggling these equations results in *!Þ))$Þ'^ '! Þ&%(^ ß '! hich results in the quadratic equation &Þ((%^'! &Þ(*!^'! Þ%! p ^'! Þ&) ß Þ%'( Using ^'! Þ&) ß and substituting into the equations above, e get Q!!, and using ^'! Þ%'(, e get Q *(Þ' With ^'! Þ&), e get ^!! ^ '! É!! '! Þ'), and the ne credibility premium is ÐÞ')ÑÐ)(Þ&Ñ Ð Þ')ÑÐ!!Ñ *Þ& Þ With ^'! Þ%'(, e get ^!! ^ '! É!! '! Þ'!$, and the ne credibility premium is ÐÞ'!$ÑÐ)(Þ&Ñ Ð Þ'!$ÑÐ*(Þ'Ñ *Þ& Þ Anser: A 6 If the severity distribution has variance Z +<Ò] Ó!, then '(! 8! ÐIÒ] ÓÑ 8! If Z +<Ò] Ó ß &!!ß!!! ß IÒ] Ó ß!!! ß then the standard for full credibility of aggregate ß&!!ß!!! claims based on number of claims is 8! ÐIÒ] ÓÑ '(! Ð!!!Ñ ''(& Þ Anser: B 7 If the claim number distribution is Poisson, the full credibility standard for aggregate claim costs based on number of claims is 8! Ò ÐIÒ] ÓÑ Ó, here ] is the claim size distribution, and Þ96 8! Ð Ñ &$'Þ'%Þ We are given Z +<Ò] Ó! For the compound Poisson aggregate claims distribution, Z +<ÒWÓ IÒ] Ó p &!!!IÒ] Ó p IÒ] Ó &! p ÐIÒ] ÓÑ IÒ] Ó Z +<Ò] Ó &!! %!! p 8! Ò ÐIÒ] ÓÑ Ó Ð&$'Þ'%ÑÐ %! Ñ * Þ

8 8 When the claim number distribution is Poisson, the standard for full credibility for IÒ] Ó aggregate claims per period based on number of claims is 8! Ò ÐIÒ] ÓÑ Ó 8! Ò ÐIÒ] ÓÑ Ó, here ] is the claim amount random variable For the lognormal, IÒ] Ó /B:Ð 5 Ñ and IÒ] Ó /B:Ð 5 Ñ /B:Ð 5 Ñ 5 Therefore, ''!! 8 8 / %!! 8!/ p 8!!Þ* Ò/B:Ð 5 ÑÓ C T Since 8! Ð Þ Ñ, e have C Þ! But C is the percentile of the standard normal distribution From the normal table, e have T Ò[ Þ!Ó Þ)'%$ T, here [ µ RÐ!ß Ñ Therefore, T Þ($ 9Þ!)Þ% ÒÓ!)Þ% p ÒÓ Þ ß!)Þ%!ß )% p! Þ Then, Ò Ó ÒÓ!ÎÞ!!, and then Z +<ÐWÑ!!!!!)Þ% ÒÓ IÐRÑ &%Þ p IÐRÑ & Þ Since IÐRÑ IÐ] Ñ, e have!! &IÐ] Ñ p IÐ] Ñ! If R is Poisson, then Z+<ÐRÑ IÐRÑ & Then Z +<ÐWÑ IÐRÑ IÐ] Ñ p!!! & IÐ] Ñ p IÐ] Ñ!! p Z +<Ð] Ñ IÐ] Ñ ÒIÐ] ÑÓ!!!!!, hich is not possible If ] has an exponential distribution, then Z +<Ð] Ñ ÒIÐ] ÑÓ %!! Þ Then Z +<ÐWÑ IÐRÑ Z +<Ð] Ñ Z +<ÐRÑ ÒIÐ] ÑÓ p!!! & %!! Z +<ÐRÑ %!! p Z +<ÐRÑ Þ&, hich is not possible 10 Since R is Poisson, the full credibility standard for estimating the mean of R is either Z+<ÐRÑ!)Þ% (i)!)þ% ÒIÐR ÑÓ!)Þ% as the expected number of exposures of R (eeks) needed, or Z+<ÐRÑ (ii)!)þ% IÐRÑ!)Þ%!)Þ% as the total expected number of claims needed Since e do not kno the value of, the only standard e can apply is (ii) %!! With 400 claims in 20 eeks, the average number of claims per eek (sample mean) is R!! Using credibility standard (ii) above, the partial credibility factor is ^ É %!!!)Þ% Þ'!(*, and the partial credibility premium is ^ R Ð ^Ñ Q Þ' Þ$*Q T, here Q is the manual premium With 500 claims in 30 eeks, the average number of claims per eek (sample mean) is &!! R $! 'Þ'''( Using credibility standard (ii) above, the partial credibility factor is ^ É &!!!)Þ% Þ'(*(, and the partial credibility premium is ^ R Ð ^Ñ Q Þ$$ Þ$!$Q T Þ*, here Q is the manual premium

9 10 continued From the to equations, Þ' Þ$*Q T and Þ$$ Þ$!$Q T Þ*, e get Q &Þ!% and T )Þ!% Then, ith 550 claims in 35 eeks, e have &&! R $& &Þ(%$ Using credibility standard (ii) above, the partial credibility factor is ^ É &&!!)Þ% Þ(), and the partial credibility premium is ^ R Ð ^Ñ Q Þ! ÐÞ)(ÑÐ&Þ!%Ñ &Þ& 11 (E) is correct This is the formula at the bottom of page 514 in the MahlerDean Credibility study note Anser: E 12 When considering the compound Poisson aggregate claims distribution W ith claim size distribution ], e have three ays of setting the standard for full credibility: (i) the number of exposures (periods of W ) is 8! ÐIÒ] ÓÑ (ii) the number of claims is 8! ÐIÒ] ÓÑ, or (iii) the aggregate amount of claims is 8! IÒ]Ó IÒ] Ó We do not kno or IÒ] Ó in this case, and therefore (i) and (iii) cannot be used We are given È that ] has coefficient of variation $, so that IÒ] Ó $, and ÐIÒ] ÓÑ *, and e can use C: Þ*' standard (ii) The credibility criterion has 8! Ð < Ñ Ð Ñ &$'Þ'%, since : Þ*& and < The standard for full credibility is &$'Þ'%Ð *Ñ &ß $''Þ% as the number of claims needed Since only 1000 claims occurred in the first year, e have not met the standard for full credibility, and e apply the method of partial credibility The credibility factor ^ is ^ 738ÖÉ!!! &ß$''Þ% ß Þ&& We are trying to determine the credibility premium for aggregate claims for the second year We have only one exposure for aggregate claims, that being the first year, so W 'Þ(& million The manual premium is given to be Q& million The credibility premium for the second year is ^ W Ð ^Ñ Q ÐÞ&&ÑÐ'ß (&!ß!!!Ñ ÐÞ(%&ÑÐ&ß!!!ß!!!Ñ &ß %%'ß &! Anser: A Þ'%& 5 M 13 Class I:!)Þ% Ð Ñ Ð!!Ñ p 5 M!ß!!! IÒ\ M Ó Ð!!Ñ p IÒ\ M Ó!ß!!! Þ'%& 5 MM Class II:!)Þ% Ð Ñ Ð!!Ñ p 5 MM %!ß!!! IÒ\ MM Ó Ð!!Ñ p IÒ\ MMÓ )!ß!!! For the combined portfolio of policies, the loss per insured is \, is a mixture of \ M and \ MM, ith and eighting applied, respectively Then %!! $ $ IÒ\Ó Ð$ ÑÐ!!Ñ Ð$ ÑÐ!!Ñ $ and IÒ\ Ó Ð$ ÑÐ!ß!!!Ñ Ð$ ÑÐ)!ß!!!Ñ %!ß!!!, so that %!!!!ß!!! Z +<Ò\Ó %!ß!!! Ð $ Ñ * Þ'%& Z +<Ò\Ó Þ'%&!!ß!!!Î* The full credibility standard for \ is Ð Ñ ÐIÒ\ÓÑ Ð Ñ Ð%!!Î$Ñ $&$ Anser: E

10 14 The partial credibility approach sets the credibility factor to be Z+<Ò\Ó Þ'%& ^ 738š Ê 8 Š 8! ÐIÒ\ÓÑ ß In this example 8! Ð Ñ!)Þ% Based on the original assumption of \ being uniform on Ò!ß) Ó, and 8 &!, ) Î e get ^ Ê &! Š!)Þ% Ð) ÎÑ Þ$($ Based on the revised assumption of \ being uniform on Ò!ß Þ) Ó, and 8 (&, e get Þ%%) Î ^ Ê (& Š!)Þ% ÐÞ ) ÎÑ Þ%&'! ^ Î^ È(&Î&! È Þ& Anser: D 15 The full credibility standard ithin 5% of expected number claims per year 90% of the time has < (5%) and C: Þ'%& (95th percentile of the standard normal distribution) For the annual claim number distribution being Poisson, the number of claims (not exposures) needed for C: Þ'%& full credibility is 8! Ð < Ñ Ð Ñ!)Þ% actual number of observations The partial credibility factor (hen less than 1) is ^É number needed for full credibility We are told that ^Þ'), from hich e get actual number of observations number needed for full credibility ÐÞ')Ñ Þ%', so that actual number of observations ÐÞ%'ÑÐ!)Þ%Ñ &!!Þ& 5005 is the full credibility standard C: C: ith < Þ and C :, here &!!Þ& Ð Ñ Ð Ñ!!C :, so that C: È < Þ &Þ!!& Þ% From reference to the standard normal table, 2 $ is the 9874th percentile Therefore, the probability that the 500 observed claims ill satisfy the full credibility standard ithin 10% of expected number of claims per year is ÐÞ*)(%Ñ Þ*(%) Anser: D 16!)Þ% ÒÓ!)Þ% p ÒÓ Þ ß!)Þ%!ß )% p! Þ Then, Ò Ó ÒÓ!ÎÞ!!, and then Z +<ÐWÑ!!!!!)Þ% ÒÓ IÐRÑ &%Þ p IÐRÑ & Þ Since IÐRÑ IÐ] Ñ, e have!! &IÐ] Ñ p IÐ] Ñ!

11 17 We ill denote the frequency by R and the severity ill be ] The full credibility standard for the expected number of claims needed is Z +<ÒRÓ ÐIÒ] ÓÑ IÒRÓ Z +<Ò] Ó!)Þ% IÒRÓ ÐIÒ] ÓÑ &% Since the severity is exponential, e have IÒ] Ó ) and Z +<Ò] Ó ) ÐIÒ] ÓÑ The full credibility standard for expected number of claims needed becomes Z +<ÒRÓIÒRÓ!)Þ% IÒRÓ Since both the mean and variance of R are increasing by 20%, this full credibility standard is unchanged at 5412 Z +<Ð] Ñ Î 18 Þ (a)(i)!)þ% ÒIÐ] ÑÓ!)Þ% Ò) ÎÓ $'!Þ) Þ Z +<Ð] Ñ ) Î (ii)!)þ% IÐ] Ñ!)Þ% ) Î )!Þ%) Þ IÒ] Ó ) Î$ (b)(i)!)þ% ÒÓ!)Þ% ÒIÐ] ÑÓ!)Þ% Ò) ÎÓ %%$Þ IÒ] Ó ) Î$ (ii)!)þ%!)þ% IÐ] Ñ!)Þ% ) Î (Þ') (iii)!)þ% ÒÓ IÒRÓ %%$Þ %%$Þ ) 19 The full credibility standard based on the number of observations needed is!)þ% Z+<Ò[Ó ÐIÒ[ ÓÑ and based on the sum of the observed values it is!)þ% Z+<Ò[Ó IÒ[Ó From the given information, the sample mean of the observed values is [ The credibility premium based on partial credibility using is ^[ Ð ^ÑQ, here [ )&(Þ% ß Q!!! and ^ is the partial credibility factor $!!ß!!! $&! )&(Þ% Þ Using the credibility standard based on the expected number of observations needed, ^ Ê $&!, so that )&(Þ%^!!!Ð ^Ñ ))%Þ%!, from hich e get ÐIÒ[ ÓÑ!)Þ% Z+<Ò[Ó $&! Z+<Ò[Ó ^ Þ)!*, and therefore Z+<Ò[Ó Þ'&%, so that Þ%*%!)Þ% ÐIÒ[ ÓÑ ÐIÒ[ ÓÑ Using the credibility standard based on the expected sum of the observed values needed, ^ Ê $!!ß!!!!)Þ% Z+<Ò[Ó IÒ[Ó, so that )&(Þ%^!!!Ð ^Ñ ))(Þ*, from hich e get $!!ß!!! Z+<Ò[Ó ^ Þ(*!, and therefore Z+<Ò[Ó Þ'$, so that %%&!)Þ% IÒ[Ó IÒ[Ó Z +<Ò[ ÓÎIÒ[ Ó %%& Then, IÒ[ Ó Z+<Ò[ÓÎÐIÒ[ÓÑ Þ%*% *!, and Z +<Ò[ Ó %!ß!!!

TABLE OF CONTENTS - VOLUME 2

TABLE OF CONTENTS - VOLUME 2 TABLE OF CONTENTS - VOLUME 2 CREDIBILITY SECTION 1 - LIMITED FLUCTUATION CREDIBILITY PROBLEM SET 1 SECTION 2 - BAYESIAN ESTIMATION, DISCRETE PRIOR PROBLEM SET 2 SECTION 3 - BAYESIAN CREDIBILITY, DISCRETE

More information

MAY 2007 SOA EXAM MLC SOLUTIONS

MAY 2007 SOA EXAM MLC SOLUTIONS 1 : œ : : p : œ Þ*& ( ( ( ( Þ*' (& ' B Þ( %:( œ / ( B œ / Þ*& Þ( & ( ( % ( MAY 2007 SOA EXAM MLC SOLUTIONS : œ : : œ Þ*' / œ Þ))* Answer: E 2 Z+

More information

NOVEMBER 2003 SOA COURSE 3 EXAM SOLUTIONS

NOVEMBER 2003 SOA COURSE 3 EXAM SOLUTIONS NOVEMER 2003 SOA COURSE 3 EXAM SOLUTIONS Prepared by Sam roverman http://membersrogerscom/2brove 2brove@rogerscom sam@utstattorontoedu 1 l; $!À$ œ $ ; $!À$ ; $!À$ œ $ ; $! $ ; $ ; $! ; $ (the second equality

More information

TABLE OF CONTENTS - VOLUME 1

TABLE OF CONTENTS - VOLUME 1 TABLE OF CONTENTS - VOLUME 1 INTRODUCTORY COMMENTS MODELING SECTION 1 - PROBABILITY REVIE PROBLEM SET 1 LM-1 LM-9 SECTION 2 - REVIE OF RANDOM VARIABLES - PART I PROBLEM SET 2 LM-19 LM-29 SECTION 3 - REVIE

More information

ACT455H1S - TEST 1 - FEBRUARY 6, 2007

ACT455H1S - TEST 1 - FEBRUARY 6, 2007 ACT455H1S - TEST 1 - FEBRUARY 6, 2007 Write name and student number on each page. Write your solution for each question in the space provided. For the multiple decrement questions, it is always assumed

More information

SIMULATION - PROBLEM SET 2

SIMULATION - PROBLEM SET 2 SIMULATION - PROBLEM SET Problems 1 to refer the following random sample of 15 data points: 8.0, 5.1,., 8.6, 4.5, 5.6, 8.1, 6.4,., 7., 8.0, 4.0, 6.5, 6., 9.1 The following three bootstrap samples of the

More information

ACT370H1S - TEST 2 - MARCH 25, 2009

ACT370H1S - TEST 2 - MARCH 25, 2009 ACT370H1S - TEST 2 - MARCH 25, 2009 Write name and student number on each page. Write your solution for each question in the space provided. Do all calculations to at least 6 significant figures. The only

More information

S. BROVERMAN STUDY GUIDE FOR THE SOCIETY OF ACTUARIES EXAM MLC 2012 EDITION EXCERPTS. Samuel Broverman, ASA, PHD

S. BROVERMAN STUDY GUIDE FOR THE SOCIETY OF ACTUARIES EXAM MLC 2012 EDITION EXCERPTS. Samuel Broverman, ASA, PHD S. ROVERMAN STUDY GUIDE FOR THE SOCIETY OF ACTUARIES EXAM MLC 2012 EDITION EXCERPTS Samuel roverman, ASA, PHD 2brove@rogers.com www.sambroverman.com copyright 2012, S. roverman www.sambroverman.com SOA

More information

SAMPLE PROBLEM PROBLEM SET - EXAM P/CAS 1

SAMPLE PROBLEM PROBLEM SET - EXAM P/CAS 1 SAMPLE PROBLEM SET - EXAM P/CAS 1 1 SAMPLE PROBLEM PROBLEM SET - EXAM P/CAS 1 1. A life insurer classifies insurance applicants according to the folloing attributes: Q - the applicant is male L - the applicant

More information

4-2 Probability Distributions and Probability Density Functions. Figure 4-2 Probability determined from the area under f(x).

4-2 Probability Distributions and Probability Density Functions. Figure 4-2 Probability determined from the area under f(x). 4-2 Probability Distributions and Probability Density Functions Figure 4-2 Probability determined from the area under f(x). 4-2 Probability Distributions and Probability Density Functions Definition 4-2

More information

Review for Exam 2. item to the quantity sold BÞ For which value of B will the corresponding revenue be a maximum?

Review for Exam 2. item to the quantity sold BÞ For which value of B will the corresponding revenue be a maximum? Review for Exam 2.) Suppose we are given the demand function :œ& % ß where : is the unit price and B is the number of units sold. Recall that the revenue function VÐBÑ œ B:Þ (a) Find the revenue function

More information

S. BROVERMAN STUDY GUIDE FOR THE SOCIETY OF ACTUARIES EXAM MLC 2010 EDITION EXCERPTS. Samuel Broverman, ASA, PHD

S. BROVERMAN STUDY GUIDE FOR THE SOCIETY OF ACTUARIES EXAM MLC 2010 EDITION EXCERPTS. Samuel Broverman, ASA, PHD S BROVERMAN STUDY GUIDE FOR THE SOCIETY OF ACTUARIES EXAM MLC 2010 EDITION EXCERPTS Samuel Broverman, ASA, PHD 2brove@rogerscom wwwsambrovermancom copyright 2010, S Broverman Excerpts: Table of Contents

More information

LOAN DEBT ANALYSIS (Developed, Composed & Typeset by: J B Barksdale Jr / )

LOAN DEBT ANALYSIS (Developed, Composed & Typeset by: J B Barksdale Jr / ) LOAN DEBT ANALYSIS (Developed, Composed & Typeset by: J B Barksdale Jr / 06 04 16) Article 01.: Loan Debt Modelling. Instances of a Loan Debt arise whenever a Borrower arranges to receive a Loan from a

More information

MAY 2005 EXAM FM SOA/CAS 2 SOLUTIONS

MAY 2005 EXAM FM SOA/CAS 2 SOLUTIONS MAY 2005 EXAM FM SOA/CAS 2 SOLUTIONS Prepared by Sam Broverman http://wwwsambrovermancom 2brove@rogerscom sam@utstattorontoedu = 8l 8 " " 1 E 8 " œ@ = œ@ + œð" 3Ñ + œ + Á + (if 3Á! ) Ð" 3Ñ Answer: E 8l

More information

CREDIBILITY - PROBLEM SET 2 Bayesian Analysis - Discrete Prior

CREDIBILITY - PROBLEM SET 2 Bayesian Analysis - Discrete Prior CREDIBILITY - PROBLEM SET 2 Bayesian Analysis - Discrete Prior Questions 1 and 2 relate to the following situation Two bowls each contain 10 similarly shaped balls Bowl 1 contains 5 red and 5 white balls

More information

MyMathLab Homework: 11. Section 12.1 & 12.2 Derivatives and the Graph of a Function

MyMathLab Homework: 11. Section 12.1 & 12.2 Derivatives and the Graph of a Function DERIVATIVES AND FUNCTION GRAPHS Text References: Section " 2.1 & 12.2 MyMathLab Homeork: 11. Section 12.1 & 12.2 Derivatives and the Graph of a Function By inspection of the graph of the function, e have

More information

Anomalies and monotonicity in net present value calculations

Anomalies and monotonicity in net present value calculations Anomalies and monotonicity in net present value calculations Marco Lonzi and Samuele Riccarelli * Dipartimento di Metodi Quantitativi Università degli Studi di Siena P.zza San Francesco 14 53100 Siena

More information

A probability distribution shows the possible outcomes of an experiment and the probability of each of these outcomes.

A probability distribution shows the possible outcomes of an experiment and the probability of each of these outcomes. Introduction In the previous chapter we discussed the basic concepts of probability and described how the rules of addition and multiplication were used to compute probabilities. In this chapter we expand

More information

Precise Frequency and Amplitude Tracking of Waveforms CFS-175 Web Version August 30 through October 24, 1999

Precise Frequency and Amplitude Tracking of Waveforms CFS-175 Web Version August 30 through October 24, 1999 Precise Frequency and Amplitude Tracking of Waveforms CFS-175 We Version August 30 through Octoer 2, 1999 David Dunthorn.c-f-systems.com Note This document has een assemled from the major parts of three

More information

Robust portfolio optimization using second-order cone programming

Robust portfolio optimization using second-order cone programming 1 Robust portfolio optimization using second-order cone programming Fiona Kolbert and Laurence Wormald Executive Summary Optimization maintains its importance ithin portfolio management, despite many criticisms

More information

SOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS

SOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS SOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS Questions 1-307 have been taken from the previous set of Exam C sample questions. Questions no longer relevant

More information

Math 1AA3/1ZB3 Sample Test 1, Version #1

Math 1AA3/1ZB3 Sample Test 1, Version #1 Math 1AA3/1ZB3 Sample Test 1, Version 1 Name: (Last Name) (First Name) Student Number: Tutorial Number: This test consists of 20 multiple choice questions worth 1 mark each (no part marks), and 1 question

More information

c. What is the probability that the next car tuned has at least six cylinders? More than six cylinders?

c. What is the probability that the next car tuned has at least six cylinders? More than six cylinders? Exercises Section 3.2 [page 98] 11. An automobile service facility specializing in engine tune-ups knows that %&% of all tune-ups are done on four-cylinder automobiles, %!% on six-cylinder automobiles,

More information

INTRODUCTION TO MATHEMATICAL MODELLING LECTURES 3-4: BASIC PROBABILITY THEORY

INTRODUCTION TO MATHEMATICAL MODELLING LECTURES 3-4: BASIC PROBABILITY THEORY 9 January 2004 revised 18 January 2004 INTRODUCTION TO MATHEMATICAL MODELLING LECTURES 3-4: BASIC PROBABILITY THEORY Project in Geometry and Physics, Department of Mathematics University of California/San

More information

EXCERPTS FROM S. BROVERMAN STUDY GUIDE FOR SOA EXAM FM/CAS EXAM 2 SPRING 2007

EXCERPTS FROM S. BROVERMAN STUDY GUIDE FOR SOA EXAM FM/CAS EXAM 2 SPRING 2007 EXCERPTS FROM S. BROVERMAN STUDY GUIDE FOR SOA EXAM FM/CAS EXAM 2 SPRING 2007 Table of Contents Introductory Comments Section 12 - Bond Amortization, Callable Bonds Section 18 - Option Strategies (1) Problem

More information

Adv. Micro Theory, ECON

Adv. Micro Theory, ECON Av. Micro Theory, ECON 60-090 Assignment 4 Ansers, Fall 00 Due: Wenesay October 3 th by 5pm Directions: Anser each question as completely as possible. You may ork in a group consisting of up to 3 members

More information

SYLLABUS OF BASIC EDUCATION SPRING 2018 Construction and Evaluation of Actuarial Models Exam 4

SYLLABUS OF BASIC EDUCATION SPRING 2018 Construction and Evaluation of Actuarial Models Exam 4 The syllabus for this exam is defined in the form of learning objectives that set forth, usually in broad terms, what the candidate should be able to do in actual practice. Please check the Syllabus Updates

More information

Institute of Actuaries of India Subject CT6 Statistical Methods

Institute of Actuaries of India Subject CT6 Statistical Methods Institute of Actuaries of India Subject CT6 Statistical Methods For 2014 Examinations Aim The aim of the Statistical Methods subject is to provide a further grounding in mathematical and statistical techniques

More information

Introduction Models for claim numbers and claim sizes

Introduction Models for claim numbers and claim sizes Table of Preface page xiii 1 Introduction 1 1.1 The aim of this book 1 1.2 Notation and prerequisites 2 1.2.1 Probability 2 1.2.2 Statistics 9 1.2.3 Simulation 9 1.2.4 The statistical software package

More information

EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES SHORT-TERM ACTUARIAL MATHEMATICS STUDY NOTE CHAPTER 8 FROM

EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES SHORT-TERM ACTUARIAL MATHEMATICS STUDY NOTE CHAPTER 8 FROM EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES SHORT-TERM ACTUARIAL MATHEMATICS STUDY NOTE CHAPTER 8 FROM FOUNDATIONS OF CASUALTY ACTUARIAL SCIENCE, FOURTH EDITION Copyright 2001, Casualty Actuarial Society.

More information

Cambridge University Press Risk Modelling in General Insurance: From Principles to Practice Roger J. Gray and Susan M.

Cambridge University Press Risk Modelling in General Insurance: From Principles to Practice Roger J. Gray and Susan M. adjustment coefficient, 272 and Cramér Lundberg approximation, 302 existence, 279 and Lundberg s inequality, 272 numerical methods for, 303 properties, 272 and reinsurance (case study), 348 statistical

More information

Chapter Learning Objectives. Discrete Random Variables. Chapter 3: Discrete Random Variables and Probability Distributions.

Chapter Learning Objectives. Discrete Random Variables. Chapter 3: Discrete Random Variables and Probability Distributions. Chapter 3: Discrete Random Variables and Probability Distributions 3-1Discrete Random Variables ibl 3-2 Probability Distributions and Probability Mass Functions 3-33 Cumulative Distribution ib ti Functions

More information

Chapter 17: Vertical and Conglomerate Mergers

Chapter 17: Vertical and Conglomerate Mergers Chapter 17: Vertical and Conglomerate Mergers Learning Objectives: Students should learn to: 1. Apply the complementary goods model to the analysis of vertical mergers.. Demonstrate the idea of double

More information

Information Acquisition in Financial Markets: a Correction

Information Acquisition in Financial Markets: a Correction Information Acquisition in Financial Markets: a Correction Gadi Barlevy Federal Reserve Bank of Chicago 30 South LaSalle Chicago, IL 60604 Pietro Veronesi Graduate School of Business University of Chicago

More information

**BEGINNING OF EXAMINATION** A random sample of five observations from a population is:

**BEGINNING OF EXAMINATION** A random sample of five observations from a population is: **BEGINNING OF EXAMINATION** 1. You are given: (i) A random sample of five observations from a population is: 0.2 0.7 0.9 1.1 1.3 (ii) You use the Kolmogorov-Smirnov test for testing the null hypothesis,

More information

3. The Dynamic Programming Algorithm (cont d)

3. The Dynamic Programming Algorithm (cont d) 3. The Dynamic Programming Algorithm (cont d) Last lecture e introduced the DPA. In this lecture, e first apply the DPA to the chess match example, and then sho ho to deal ith problems that do not match

More information

M.Sc. ACTUARIAL SCIENCE. Term-End Examination

M.Sc. ACTUARIAL SCIENCE. Term-End Examination No. of Printed Pages : 15 LMJA-010 (F2F) M.Sc. ACTUARIAL SCIENCE Term-End Examination O CD December, 2011 MIA-010 (F2F) : STATISTICAL METHOD Time : 3 hours Maximum Marks : 100 SECTION - A Attempt any five

More information

Credibility. Chapters Stat Loss Models. Chapters (Stat 477) Credibility Brian Hartman - BYU 1 / 31

Credibility. Chapters Stat Loss Models. Chapters (Stat 477) Credibility Brian Hartman - BYU 1 / 31 Credibility Chapters 17-19 Stat 477 - Loss Models Chapters 17-19 (Stat 477) Credibility Brian Hartman - BYU 1 / 31 Why Credibility? You purchase an auto insurance policy and it costs $150. That price is

More information

1. You are given the following information about a stationary AR(2) model:

1. You are given the following information about a stationary AR(2) model: Fall 2003 Society of Actuaries **BEGINNING OF EXAMINATION** 1. You are given the following information about a stationary AR(2) model: (i) ρ 1 = 05. (ii) ρ 2 = 01. Determine φ 2. (A) 0.2 (B) 0.1 (C) 0.4

More information

FV N = PV (1+ r) N. FV N = PVe rs * N 2011 ELAN GUIDES 3. The Future Value of a Single Cash Flow. The Present Value of a Single Cash Flow

FV N = PV (1+ r) N. FV N = PVe rs * N 2011 ELAN GUIDES 3. The Future Value of a Single Cash Flow. The Present Value of a Single Cash Flow QUANTITATIVE METHODS The Future Value of a Single Cash Flow FV N = PV (1+ r) N The Present Value of a Single Cash Flow PV = FV (1+ r) N PV Annuity Due = PVOrdinary Annuity (1 + r) FV Annuity Due = FVOrdinary

More information

Describing Uncertain Variables

Describing Uncertain Variables Describing Uncertain Variables L7 Uncertainty in Variables Uncertainty in concepts and models Uncertainty in variables Lack of precision Lack of knowledge Variability in space/time Describing Uncertainty

More information

Problem Set #3 (15 points possible accounting for 3% of course grade) Due in hard copy at beginning of lecture on Wednesday, March

Problem Set #3 (15 points possible accounting for 3% of course grade) Due in hard copy at beginning of lecture on Wednesday, March Department of Economics M. Doell California State University, Sacramento Spring 2011 Intermediate Macroeconomics Economics 100A Problem Set #3 (15 points possible accounting for 3% of course grade) Due

More information

Continuous random variables

Continuous random variables Continuous random variables probability density function (f(x)) the probability distribution function of a continuous random variable (analogous to the probability mass function for a discrete random variable),

More information

MAS187/AEF258. University of Newcastle upon Tyne

MAS187/AEF258. University of Newcastle upon Tyne MAS187/AEF258 University of Newcastle upon Tyne 2005-6 Contents 1 Collecting and Presenting Data 5 1.1 Introduction...................................... 5 1.1.1 Examples...................................

More information

CHAPTER 8: INDEX MODELS

CHAPTER 8: INDEX MODELS CHTER 8: INDEX ODELS CHTER 8: INDEX ODELS ROBLE SETS 1. The advantage of the index model, compared to the arkoitz procedure, is the vastly reduced number of estimates required. In addition, the large number

More information

CS 237: Probability in Computing

CS 237: Probability in Computing CS 237: Probability in Computing Wayne Snyder Computer Science Department Boston University Lecture 12: Continuous Distributions Uniform Distribution Normal Distribution (motivation) Discrete vs Continuous

More information

EconS 301 Review Session #6 Chapter 8: Cost Curves

EconS 301 Review Session #6 Chapter 8: Cost Curves EconS 01 Revie Session #6 Chapter 8: Cost Curves 8.1. Consider a production function ith to inputs, labor and capital, given by (. The marginal products associated ith this production function are as follos:

More information

METHODS AND ASSISTANCE PROGRAM 2014 REPORT Navarro Central Appraisal District. Glenn Hegar

METHODS AND ASSISTANCE PROGRAM 2014 REPORT Navarro Central Appraisal District. Glenn Hegar METHODS AND ASSISTANCE PROGRAM 2014 REPORT Navarro Central Appraisal District Glenn Hegar Navarro Central Appraisal District Mandatory Requirements PASS/FAIL 1. Does the appraisal district have up-to-date

More information

Morningstar Rating Analysis

Morningstar Rating Analysis Morningstar Research January 2017 Morningstar Rating Analysis of European Investment Funds Authors: Nikolaj Holdt Mikkelsen, CFA, CIPM Ali Masarwah Content Morningstar European Rating Analysis of Investment

More information

Statistics 6 th Edition

Statistics 6 th Edition Statistics 6 th Edition Chapter 5 Discrete Probability Distributions Chap 5-1 Definitions Random Variables Random Variables Discrete Random Variable Continuous Random Variable Ch. 5 Ch. 6 Chap 5-2 Discrete

More information

ACTEX. SOA Exam STAM Study Manual. With StudyPlus + Spring 2018 Edition Volume I Samuel A. Broverman, Ph.D., ASA

ACTEX. SOA Exam STAM Study Manual. With StudyPlus + Spring 2018 Edition Volume I Samuel A. Broverman, Ph.D., ASA ACTEX SOA Exam STAM Study Manual With StudyPlus + StudyPlus + gives you digital access* to: Actuarial Exam & Career Strategy Guides Technical Skill elearning Tools Samples of Supplemental Textbooks And

More information

Schedule of Fees Deposit Account Agreement Funds Availability Policy Overdraft Protection for Consumers Privacy Policy Electronic Fund Transfer

Schedule of Fees Deposit Account Agreement Funds Availability Policy Overdraft Protection for Consumers Privacy Policy Electronic Fund Transfer Schedule of Fees Deposit Account Agreement Funds Availability Policy Overdraft Protection for Consumers Privacy Policy Electronic Fund Transfer Agreement First Option Checking Free Style Checking Maximizer

More information

Calibration approach estimators in stratified sampling

Calibration approach estimators in stratified sampling Statistics & Probability Letters 77 (2007) 99 103 www.elsevier.com/locate/stapro Calibration approach estimators in stratified sampling Jong-Min Kim a,, Engin A. Sungur a, Tae-Young Heo b a Division of

More information

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions April 9th, 2018 Lecture 20: Special distributions Week 1 Chapter 1: Axioms of probability Week 2 Chapter 3: Conditional probability and independence Week 4 Chapters 4, 6: Random variables Week 9 Chapter

More information

The relationship between reciprocal currency futures prices

The relationship between reciprocal currency futures prices The relationship between reciprocal currency futures prices Avi Bick Beedie School of Business Simon Fraser University Burnaby, B. C. V5A 1S6, Canada Tel. 778-782-3748 Fax:778-782-4920 E-mail: bick@sfu.ca

More information

Contents. An Overview of Statistical Applications CHAPTER 1. Contents (ix) Preface... (vii)

Contents. An Overview of Statistical Applications CHAPTER 1. Contents (ix) Preface... (vii) Contents (ix) Contents Preface... (vii) CHAPTER 1 An Overview of Statistical Applications 1.1 Introduction... 1 1. Probability Functions and Statistics... 1..1 Discrete versus Continuous Functions... 1..

More information

Model Paper Statistics Objective. Paper Code Time Allowed: 20 minutes

Model Paper Statistics Objective. Paper Code Time Allowed: 20 minutes Model Paper Statistics Objective Intermediate Part I (11 th Class) Examination Session 2012-2013 and onward Total marks: 17 Paper Code Time Allowed: 20 minutes Note:- You have four choices for each objective

More information

Exam P Flashcards exams. Key concepts. Important formulas. Efficient methods. Advice on exam technique

Exam P Flashcards exams. Key concepts. Important formulas. Efficient methods. Advice on exam technique Exam P Flashcards 01 exams Key concepts Important formulas Efficient methods Advice on exam technique All study material produced by BPP Professional Education is copyright and is sold for the exclusive

More information

CLASSIC PRICE WITH NON-GUARANTEED COE STANDARD CHARGES # 6 MTHS ROAD TAX **

CLASSIC PRICE WITH NON-GUARANTEED COE STANDARD CHARGES # 6 MTHS ROAD TAX ** k ] z { z w { w œ Ž > R PRIE IT OE 6 MTS ROD TX RGES ES INSURNE FINNE ES INSURNE FINNE ES TEGOR OE $ $ $ $ IOS E NE E UTO NPRBEPRT E $76 $ $70 $ $ $000 $ $ E UTO ELEGNE NPRBEPRT E $76 $ $70 $ $7 $000 $6

More information

M249 Diagnostic Quiz

M249 Diagnostic Quiz THE OPEN UNIVERSITY Faculty of Mathematics and Computing M249 Diagnostic Quiz Prepared by the Course Team [Press to begin] c 2005, 2006 The Open University Last Revision Date: May 19, 2006 Version 4.2

More information

Chapter 3 Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2013 John Wiley & Sons, Inc.

Chapter 3 Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2013 John Wiley & Sons, Inc. 1 3.1 Describing Variation Stem-and-Leaf Display Easy to find percentiles of the data; see page 69 2 Plot of Data in Time Order Marginal plot produced by MINITAB Also called a run chart 3 Histograms Useful

More information

PCI VISA JCB 1.0 ! " #$&%'( I #J! KL M )+*, -F;< P 9 QR I STU. VXW JKX YZ\[X ^]_ - ` a 0. /\b 0 c d 1 * / `fe d g * /X 1 2 c 0 g d 1 0,

PCI VISA JCB 1.0 !  #$&%'( I #J! KL M )+*, -F;< P 9 QR I STU. VXW JKX YZ\[X ^]_ - ` a 0. /\b 0 c d 1 * / `fe d g * /X 1 2 c 0 g d 1 0, B! + BFḦ! + F s! ẗ ẅ ẍ!! þ! Š F!ñF+ + ±ŠFŌF ¹ F! FÀF +!±Š!+ÌÌ!!±! ±Š F!ñ±Š í îï!ñö øf!ù ûü ñń ¹F!!À!!! + B s s s s s B s s F ¹ ¹ ¹ ¹ Ì Ì ± ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ø ¹ ¹ ¹ ¹ ÀÀÀÀ ¹ ¹ ¹ ¹ ¹ ¹ ¹ ¹

More information

Correlation Structures Corresponding to Forward Rates

Correlation Structures Corresponding to Forward Rates Chapter 6 Correlation Structures Corresponding to Forward Rates Ilona Kletskin 1, Seung Youn Lee 2, Hua Li 3, Mingfei Li 4, Rongsong Liu 5, Carlos Tolmasky 6, Yujun Wu 7 Report prepared by Seung Youn Lee

More information

FOLIOfn Investments, Inc. McLean, Virginia

FOLIOfn Investments, Inc. McLean, Virginia FOLIOfn Investments, Inc. McLean, Virginia Statements of Financial Condition Contents: December 31, 2014 June 30, 2014 (unaudited) December 31, 2013 June 30, 2013 (unaudited) FOLIOfn INVESTMENTS, INC.

More information

ECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 10: Continuous RV Families. Prof. Vince Calhoun

ECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 10: Continuous RV Families. Prof. Vince Calhoun ECE 340 Probabilistic Methods in Engineering M/W 3-4:15 Lecture 10: Continuous RV Families Prof. Vince Calhoun 1 Reading This class: Section 4.4-4.5 Next class: Section 4.6-4.7 2 Homework 3.9, 3.49, 4.5,

More information

Intermediate Micro HW 2

Intermediate Micro HW 2 Intermediate Micro HW June 3, 06 Leontief & Substitution An individual has Leontief preferences over goods x and x He starts ith income y and the to goods have respective prices p and p The price of good

More information

Chapter 4 Random Variables & Probability. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables

Chapter 4 Random Variables & Probability. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Chapter 4.5, 6, 8 Probability for Continuous Random Variables Discrete vs. continuous random variables Examples of continuous distributions o Uniform o Exponential o Normal Recall: A random variable =

More information

PRIMETIME HEALTH PLAN

PRIMETIME HEALTH PLAN AULTCARE'S PRIMETIME HEALTH PLAN INSTRUCTIONS ON HOW TO ENROLL IN PRIMETIME HEALTH PLAN _... YOU MUST LIVE IN THE PRIMETIME HEALTH PLAN SERVICE AREA, WHICH IS:..._ CARROLL, COLUMBIANA, HARRISON, HOLMES,

More information

Midterm Exam 2. Tuesday, November 1. 1 hour and 15 minutes

Midterm Exam 2. Tuesday, November 1. 1 hour and 15 minutes San Francisco State University Michael Bar ECON 302 Fall 206 Midterm Exam 2 Tuesday, November hour and 5 minutes Name: Instructions. This is closed book, closed notes exam. 2. No calculators of any kind

More information

Simple Random Sample

Simple Random Sample Simple Random Sample A simple random sample (SRS) of size n consists of n elements from the population chosen in such a way that every set of n elements has an equal chance to be the sample actually selected.

More information

ECON 214 Elements of Statistics for Economists 2016/2017

ECON 214 Elements of Statistics for Economists 2016/2017 ECON 214 Elements of Statistics for Economists 2016/2017 Topic The Normal Distribution Lecturer: Dr. Bernardin Senadza, Dept. of Economics bsenadza@ug.edu.gh College of Education School of Continuing and

More information

Discrete-time Asset Pricing Models in Applied Stochastic Finance

Discrete-time Asset Pricing Models in Applied Stochastic Finance Discrete-time Asset Pricing Models in Applied Stochastic Finance P.C.G. Vassiliou ) WILEY Table of Contents Preface xi Chapter ^Probability and Random Variables 1 1.1. Introductory notes 1 1.2. Probability

More information

AP Statistics Ch 8 The Binomial and Geometric Distributions

AP Statistics Ch 8 The Binomial and Geometric Distributions Ch 8.1 The Binomial Distributions The Binomial Setting A situation where these four conditions are satisfied is called a binomial setting. 1. Each observation falls into one of just two categories, which

More information

2009 Plan Information Worksheet

2009 Plan Information Worksheet Plan Sponsor Information 2009 Plan Information Worksheet Status: Plan Sponsor's Name Plan Sponsor's Mailling Address Foreign American University of Beirut 3 DAG Hammarskjold Plaza, 8th Floor Abbreviated

More information

Chapter 4 and 5 Note Guide: Probability Distributions

Chapter 4 and 5 Note Guide: Probability Distributions Chapter 4 and 5 Note Guide: Probability Distributions Probability Distributions for a Discrete Random Variable A discrete probability distribution function has two characteristics: Each probability is

More information

Practice Exam 1. Loss Amount Number of Losses

Practice Exam 1. Loss Amount Number of Losses Practice Exam 1 1. You are given the following data on loss sizes: An ogive is used as a model for loss sizes. Determine the fitted median. Loss Amount Number of Losses 0 1000 5 1000 5000 4 5000 10000

More information

IAEA SAFEGUARDS TECHNICAL MANUAL

IAEA SAFEGUARDS TECHNICAL MANUAL IAEA-TECDOC-227 IAEA SAFEGUARDS TECHNICAL MANUAL PART F STATISTICAL CONCEPTS AND TECHNIQUES VOLUME 1 SECOND REVISED EDITION A TECHNICAL DOCUMENT ISSUED BY THE INTERNATIONAL ATOMIC ENERGY AGENCY, VIENNA,

More information

Homework Problems Stat 479

Homework Problems Stat 479 Chapter 10 91. * A random sample, X1, X2,, Xn, is drawn from a distribution with a mean of 2/3 and a variance of 1/18. ˆ = (X1 + X2 + + Xn)/(n-1) is the estimator of the distribution mean θ. Find MSE(

More information

Australian Journal of Basic and Applied Sciences. Conditional Maximum Likelihood Estimation For Survival Function Using Cox Model

Australian Journal of Basic and Applied Sciences. Conditional Maximum Likelihood Estimation For Survival Function Using Cox Model AENSI Journals Australian Journal of Basic and Applied Sciences Journal home page: wwwajbaswebcom Conditional Maximum Likelihood Estimation For Survival Function Using Cox Model Khawla Mustafa Sadiq University

More information

Study Guide and Review - Chapter 2

Study Guide and Review - Chapter 2 Divide using long division. 31. (x 3 + 8x 2 5) (x 2) So, (x 3 + 8x 2 5) (x 2) = x 2 + 10x + 20 +. 33. (2x 5 + 5x 4 5x 3 + x 2 18x + 10) (2x 1) So, (2x 5 + 5x 4 5x 3 + x 2 18x + 10) (2x 1) = x 4 + 3x 3

More information

KARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI

KARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI 88 P a g e B S ( B B A ) S y l l a b u s KARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI Course Title : STATISTICS Course Number : BA(BS) 532 Credit Hours : 03 Course 1. Statistical

More information

Chapter 2 Forecast Error

Chapter 2 Forecast Error Chapter 2 Forecast Error 2.1 Introduction The standard deviation of the 1-month-ahead forecast error is used to determine how much safety stock is needed to satisfy the level of service to customers. An

More information

1/2 2. Mean & variance. Mean & standard deviation

1/2 2. Mean & variance. Mean & standard deviation Question # 1 of 10 ( Start time: 09:46:03 PM ) Total Marks: 1 The probability distribution of X is given below. x: 0 1 2 3 4 p(x): 0.73? 0.06 0.04 0.01 What is the value of missing probability? 0.54 0.16

More information

No, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation.

No, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation. 1) If n 100 and p 0.02 in a binomial experiment, does this satisfy the rule for a normal approximation? Why or why not? No, because np 100(0.02) 2. The value of np must be greater than or equal to 5 to

More information

A First Course in Probability

A First Course in Probability A First Course in Probability Seventh Edition Sheldon Ross University of Southern California PEARSON Prentice Hall Upper Saddle River, New Jersey 07458 Preface 1 Combinatorial Analysis 1 1.1 Introduction

More information

Introduction to Stochastic Calculus With Applications

Introduction to Stochastic Calculus With Applications Introduction to Stochastic Calculus With Applications Fima C Klebaner University of Melbourne \ Imperial College Press Contents Preliminaries From Calculus 1 1.1 Continuous and Differentiable Functions.

More information

Information, efficiency and the core of an economy: Comments on Wilson s paper

Information, efficiency and the core of an economy: Comments on Wilson s paper Information, efficiency and the core of an economy: Comments on Wilson s paper Dionysius Glycopantis 1 and Nicholas C. Yannelis 2 1 Department of Economics, City University, Northampton Square, London

More information

Using Monte Carlo Analysis in Ecological Risk Assessments

Using Monte Carlo Analysis in Ecological Risk Assessments 10/27/00 Page 1 of 15 Using Monte Carlo Analysis in Ecological Risk Assessments Argonne National Laboratory Abstract Monte Carlo analysis is a statistical technique for risk assessors to evaluate the uncertainty

More information

2.1 Random variable, density function, enumerative density function and distribution function

2.1 Random variable, density function, enumerative density function and distribution function Risk Theory I Prof. Dr. Christian Hipp Chair for Science of Insurance, University of Karlsruhe (TH Karlsruhe) Contents 1 Introduction 1.1 Overview on the insurance industry 1.1.1 Insurance in Benin 1.1.2

More information

3a Plan administrator s name and address (if same as plan sponsor, enter Same ) 3b Administrator s EIN. 3c Administrator s telephone number.

3a Plan administrator s name and address (if same as plan sponsor, enter Same ) 3b Administrator s EIN. 3c Administrator s telephone number. Form 5500 (2009) Page 2 3a Plan administrator s name and address (if same as plan sponsor, enter Same ) SAME ½ñ± ïîíìëêéèç ßÞÝÜÛ ïîíìëêéèç ßÞÝÜÛ Ý ÌÇÛÚÙØ ßÞô ÍÌ ðïîíìëêéèçðï ËÕ 4 If the name and/or EIN

More information

This homework assignment uses the material on pages ( A moving average ).

This homework assignment uses the material on pages ( A moving average ). Module 2: Time series concepts HW Homework assignment: equally weighted moving average This homework assignment uses the material on pages 14-15 ( A moving average ). 2 Let Y t = 1/5 ( t + t-1 + t-2 +

More information

Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the

Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the mean, use the CLT for the mean. If you are being asked to

More information

Index Numbers and Moving Averages

Index Numbers and Moving Averages 5 Index Numbers and Moving Averages 5.1 INDEX NUMBERS The value of money is going don, e hear everyday. This means that since prices of things are going up, e get lesser and lesser quantities of the same

More information

2. The sum of all the probabilities in the sample space must add up to 1

2. The sum of all the probabilities in the sample space must add up to 1 Continuous Random Variables and Continuous Probability Distributions Continuous Random Variable: A variable X that can take values on an interval; key feature remember is that the values of the variable

More information

Chapter 4 Probability Distributions

Chapter 4 Probability Distributions Slide 1 Chapter 4 Probability Distributions Slide 2 4-1 Overview 4-2 Random Variables 4-3 Binomial Probability Distributions 4-4 Mean, Variance, and Standard Deviation for the Binomial Distribution 4-5

More information

Chapter 7 presents the beginning of inferential statistics. The two major activities of inferential statistics are

Chapter 7 presents the beginning of inferential statistics. The two major activities of inferential statistics are Chapter 7 presents the beginning of inferential statistics. Concept: Inferential Statistics The two major activities of inferential statistics are 1 to use sample data to estimate values of population

More information

Some Discrete Distribution Families

Some Discrete Distribution Families Some Discrete Distribution Families ST 370 Many families of discrete distributions have been studied; we shall discuss the ones that are most commonly found in applications. In each family, we need a formula

More information

Statistics & Flood Frequency Chapter 3. Dr. Philip B. Bedient

Statistics & Flood Frequency Chapter 3. Dr. Philip B. Bedient Statistics & Flood Frequency Chapter 3 Dr. Philip B. Bedient Predicting FLOODS Flood Frequency Analysis n Statistical Methods to evaluate probability exceeding a particular outcome - P (X >20,000 cfs)

More information

MAFS Computational Methods for Pricing Structured Products

MAFS Computational Methods for Pricing Structured Products MAFS550 - Computational Methods for Pricing Structured Products Solution to Homework Two Course instructor: Prof YK Kwok 1 Expand f(x 0 ) and f(x 0 x) at x 0 into Taylor series, where f(x 0 ) = f(x 0 )

More information

Lecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial

Lecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial Lecture 23 STAT 225 Introduction to Probability Models April 4, 2014 approximation Whitney Huang Purdue University 23.1 Agenda 1 approximation 2 approximation 23.2 Characteristics of the random variable:

More information