Statistics Methods I - Homework 6 Solution | ST 301, Assignments of Data Analysis & Statistical Methods

Material Type: Assignment; Professor: Muse; Class: STATIS METHODS I; Subject: Statistics; University: North Carolina State University; Term: Unknown 1989;

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ST301
Homework6Solutions
Ch4.4
59.)µX=0(0.01)+1(0.05)+2(0.30)+3(0.43)+4(0.21)=2.78
60.)LetX=#ofnonworderrorsina250wordessay,Y=#ofworderrorsina250wordessay.
Meannumberofnonworderrors:µX=0(0.1)+1(0.2)+2(0.3)+3(0.3)+4(0.1)=2.1
Meannumberofworderrors:µY=0(0.4)+1(0.3)+2(0.2)+3(0.1)=1
64.)(a)XandYIndependent:µX+Y=µX+µY=2.1+1=3.1
 σX+Y=
=

=(02.1)2(0.1)+(12.1)2(0.2)+(22.1)2(0.3)+(32.1)2(0.3)+(42.1)2(0.1)=1.29
1.291
=(0–1)2(0.4)+(1–1)2(0.3)+(21)2(0.2)+(3–1)2(0.1)=1
SoσX+Y= . 

(b)XandYDependentwithρ=0.5:µX+YisstillµX+µY=2.1+1=3.1
σX+Y=

2
1.2912󰇛0.5󰇜󰇛1.1358󰇜󰇛1󰇜 .
65.)(a)
StockValueafterTwoDays(rise/rise,rise/fall,fall/rise,fall/fall)
Value1.32*2*1000=$562.501000=$16901.3*.75*1000=$975 .75*1.3*1000=$975 .75
Probability0.250.250.250.25
P(stockworthmorethan$1000aftertwodays)=0.25
(b)MeanValue=1690(0.25)+975(0.5)+562.5(0.25)=$1050.625
 
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ST 301

Homework 6 Solutions

Ch 4.

59.) μ (^) X = 0(0.01) + 1(0.05) + 2(0.30) + 3(0.43) + 4(0.21) = 2.

60.) Let X = # of nonword errors in a 250 ‐word essay, Y = # of word errors in a 250 ‐word essay.

Mean number of nonword errors: μX = 0(0.1) + 1(0.2) + 2(0.3) + 3(0.3) + 4(0.1) = 2.

Mean number of word errors: μ (^) Y = 0(0.4) + 1(0.3) + 2(0.2) + 3(0.1) = 1

64.) (a) X and Y Independent: μ (^) X+Y = μ (^) X + μ (^) Y = 2.1 + 1 = 3.

σX+Y = ඥߪ௑ା௒ଶ^ = ඥߪ௑ଶ^ ߪ ൅௒ଶ

ߪ௑ ଶ= (0 – 2.1)^2 (0.1) + (1 – 2.1)^2 (0.2) + (2 – 2.1)^2 (0.3) + (3 – 2.1)^2 (0.3) + (4 – 2.1)^2 (0.1) = 1.

௒ଶ

1.29 ൅ 1

ߪ = (0 –1)^2 (0.4) + (1 –1)^2 (0.3) + (2 – 1)^2 (0.2) + (3 –1)^2 (0.1) = 1

So σX+Y = √ ൌ ૚. ૞૚૜

(b) X and Y Dependent with ρ=0.5: μ (^) X+Y is still μ (^) X + μ (^) Y = 2.1 + 1 = 3.

σX+Y = ඥ ߪඥ ൌ௑ଶ^ ߪ ൅௒ଶ^ ߪߩ2 ൅௑ ߪ௒ ൌ ඥ1.29 ൅ 1 ൅ 2ሺ0.5ሻሺ1.1358ሻሺ1ሻ ൌ ૚. ૡ૞૚

65.) (a)

Stock Value after Two Days (rise/rise, rise/fall, fall/rise, fall/fall) Value 1.3 2 * 1000 = $1690 1.3.751000= $975 .751.31000= $975 .75^2 *1000= $562. Probability 0.25 0.25 0.25 0.

P(stock worth more than $1000 after two days) = 0.

(b) Mean Value = 1690(0.25) + 975(0.5) + 562.5(0.25) = $1050.

74.) Let X = Height in centimeters, Y = Height in inches. Y = (^) ଶ.ହସଵ*X.

Given: μ (^) X = 176.8, σX = 7.2 (so σX^2 = 51.84)

By the rules of linear transformation with a = 0 and b = (^) ଶ.ହସଵ,

μ (^) Y = (^) ଶ.ହସ^ ଵ^ כ

ଵ ଶ.ହସ

μ (^) X = 69.606 inches

σY^2 = ቀ ቁ

σX^2 = (.155)*(51.84) = 8.

So σY = ൌ √8.035 ൌ ૛. ૡ૜૞

Ch 5

inches

1.) (a) Yes, it’s reasonable to use a binomial distribution with n=500, p=1/12, because there are a fixed

(b) This is not binomial because the number of trials is not fixed. In this case X actually has a Geometric

(c) Not binomial; this can’t be expressed as a series of trials with success/failure outcomes.

2.) (a) Not binomial because the variable X is not simply a success/failure, and the number of trials is not

(b) Yes, binomial works here because the pool is a fixed 100 people, and each one either does or doesn’t

(c) Yes, binomial works. Looking at one year means the number of trials is fixed at 52, and we can

3.) (a) Let X = # of errors caught out of 20. Then X is distributed Binomial with n=20, p=0..

Let Y = # of errors missed out of 20. Then Y is distributed Binomial with n=20, p=0..

(b) P(Y ≥ 9) = 0.0654 + 0.0308 + 0.0120 + 0.0039 + 0.0010 + 0.0002 = 0.1133 (using Table C)

5.) (a) μX = n*p = 20(0.7) = 14 errors caught. μ (^) Y = 20(0.3) = 6 errors missed.

(b) σX = (^) ሻ݌െ ሺ1 כ ݌ כ ݊ඥ

number of trials and they all have equal probability of success.

distribution.

fixed.

oppose the death penalty. Since we have no knowledge of their opinions, we treat them as all having the same probability of opposing it or not.

assume the probability of winning is the same every week.