SlideShare a Scribd company logo
1 of 1
Download to read offline
Let X and Y be independent and normally distributed, X with mean 0 and variance 1, Y with
mean 1. Suppose P(X>Y) = 1/3. Find the standard deviation of Y.
Solution
for the joint distribution x-y the new mean would be E(x) - E(y) = -1 and new
variance would be Var(x)+ Var(y) = 1+sigma(y)^2 ......so final standard deviation would be
sqrt(1+ sigma^2) = k ......now P(X-Y > 0) = 1/3 ......P([X-Y - (-1)]/[k] > 1/k ) = 1/3 ......1 -
F(1/k) = 1/3 // F is the CDF .......F(1/k) = 2/3 ........1/k = 0.435 (from table of cdf of normal
functions) .........k = 2.298 .........var(Y) = k^2 -1 = 4.28 ...........SD(y) = sqrt(var(Y)) = 2.06

More Related Content

More from info665359

More from info665359 (12)

Let X be an r.v. defined on a sample space S into the real line R . .pdf
Let X be an r.v. defined on a sample space S into the real line R . .pdfLet X be an r.v. defined on a sample space S into the real line R . .pdf
Let X be an r.v. defined on a sample space S into the real line R . .pdf
 
let X be a raondom variable that represents the number of infants in.pdf
let X be a raondom variable that represents the number of infants in.pdflet X be a raondom variable that represents the number of infants in.pdf
let X be a raondom variable that represents the number of infants in.pdf
 
Let X be a random variable with cumulative distribution funtion0.pdf
Let X be a random variable with cumulative distribution funtion0.pdfLet X be a random variable with cumulative distribution funtion0.pdf
Let X be a random variable with cumulative distribution funtion0.pdf
 
let X be a random variable having Poisson density parameter Calcula.pdf
let X be a random variable having Poisson density parameter  Calcula.pdflet X be a random variable having Poisson density parameter  Calcula.pdf
let X be a random variable having Poisson density parameter Calcula.pdf
 
Let X be a random number between 0 and 1 produced by the idealized r.pdf
Let X be a random number between 0 and 1 produced by the idealized r.pdfLet X be a random number between 0 and 1 produced by the idealized r.pdf
Let X be a random number between 0 and 1 produced by the idealized r.pdf
 
Let X be a binomial random variable with parameters m and 13. Find .pdf
Let X be a binomial random variable with parameters m and 13. Find .pdfLet X be a binomial random variable with parameters m and 13. Find .pdf
Let X be a binomial random variable with parameters m and 13. Find .pdf
 
Let X be a discrete random variable that takes on two values -1 and.pdf
Let X be a discrete random variable that takes on two values -1 and.pdfLet X be a discrete random variable that takes on two values -1 and.pdf
Let X be a discrete random variable that takes on two values -1 and.pdf
 
Let X and Y have the joint normal distribution described in equation.pdf
Let X and Y have the joint normal distribution described in equation.pdfLet X and Y have the joint normal distribution described in equation.pdf
Let X and Y have the joint normal distribution described in equation.pdf
 
Let X and Y be uniformly distributed in the interval [0,1]. Compute .pdf
Let X and Y be uniformly distributed in the interval [0,1]. Compute .pdfLet X and Y be uniformly distributed in the interval [0,1]. Compute .pdf
Let X and Y be uniformly distributed in the interval [0,1]. Compute .pdf
 
Let V be a vector space containing polynomials in the form f(x)=ao+a.pdf
Let V be a vector space containing polynomials in the form f(x)=ao+a.pdfLet V be a vector space containing polynomials in the form f(x)=ao+a.pdf
Let V be a vector space containing polynomials in the form f(x)=ao+a.pdf
 
Let V be the vector space of ordered pairs of complex numbers over t.pdf
Let V be the vector space of ordered pairs of complex numbers over t.pdfLet V be the vector space of ordered pairs of complex numbers over t.pdf
Let V be the vector space of ordered pairs of complex numbers over t.pdf
 
Let X (t) be a birth and death process with possible states 0, 1, ...pdf
Let X (t) be a birth and death process with possible states 0, 1, ...pdfLet X (t) be a birth and death process with possible states 0, 1, ...pdf
Let X (t) be a birth and death process with possible states 0, 1, ...pdf
 

Recently uploaded

Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
ciinovamais
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
Chris Hunter
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
heathfieldcps1
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
kauryashika82
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
PECB
 

Recently uploaded (20)

Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
Z Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphZ Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot Graph
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
 
PROCESS RECORDING FORMAT.docx
PROCESS      RECORDING        FORMAT.docxPROCESS      RECORDING        FORMAT.docx
PROCESS RECORDING FORMAT.docx
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
 
Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
 
Key note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfKey note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdf
 

Let X and Y be independent and normally distributed, X with mean 0 a.pdf

  • 1. Let X and Y be independent and normally distributed, X with mean 0 and variance 1, Y with mean 1. Suppose P(X>Y) = 1/3. Find the standard deviation of Y. Solution for the joint distribution x-y the new mean would be E(x) - E(y) = -1 and new variance would be Var(x)+ Var(y) = 1+sigma(y)^2 ......so final standard deviation would be sqrt(1+ sigma^2) = k ......now P(X-Y > 0) = 1/3 ......P([X-Y - (-1)]/[k] > 1/k ) = 1/3 ......1 - F(1/k) = 1/3 // F is the CDF .......F(1/k) = 2/3 ........1/k = 0.435 (from table of cdf of normal functions) .........k = 2.298 .........var(Y) = k^2 -1 = 4.28 ...........SD(y) = sqrt(var(Y)) = 2.06