Where a = 30 and b = 55.
Probability (time = t) = 1/(b-a) = 1/ (55-30) = 1/25 = 0.04
b. P (t<45) = The area between t= 30 and t=45 minutes = (x-a) *1/(b-a) = (45-30) *0.04 = 0.6
c. P(t>48) = The area between t = 48 and t = 55 minutes = 1-((x-a) *1/(b-a)) = 1 – ((48 – 30) *0.04) = 1-(18*0.04) = 0.28
Question 2:
a. Mean = sum XX ages of all XXXX/ number of deer = 1061/XX = XX.37
XXXXXXXX deviation XX XXXXXX =
Therefore, = root of (((47-XX.XX) ^X+(XX-XX.XX) ^X+…. +(64-35.37) ^X)/ (30-1))
= XX.53
b. Using Central Limit XXXXXXX, we can XXX that XXX XXXX of the XXXXXXXXXX and XXXXXXXX deviation XXX:
XXXX = 35.37
SE = s/XXXX(n) = XX.53/ (XXXX (30)) = X.922
XXXXXXXXXXX (age &XX;=30) = 1- probability (age &XX;= XX) = 1 – X (X<= (x-mean)/SE)
= X – X (X<= (XX-35.XX)/1.XXX) = 1 – P (z&XX;=-X.79) = 1 – X.002635 = 0.XXX
XXXXXXXX X:
a. Since, XXX XXXXXX class marks XXXXX XXXXXX a XXXXXX XXXXXXXXXXXX, therefore, XXXXX any number XX samples XXXX are XXXXX XXXX XXX XXXXXXXXXX will follow a normal distribution. Therefore, the standard XXXXXXXXX and XXXX of XXX XXXXXX can be XXXX XX calculate XXX XXXX and XXXXXXXX XXXXXXXXXX XX the individual XXXXXX of XXX XXXXXXXXXX.
b. XXXX = XX.7
Standard deviation = XX.3
Variance = XX.X^2/n
XXXXXX variance = XX.255
24.3^X/n = XX.XXX or n = XX.XX or 28
So, most likely XXXXX size XXXXX XX XX
Question X:
X score = (x-mean)/XXXXXXXX XXXXXXXXX
a. X = (250-210)/ XX = 40/50 = X.8
P (XXXXX>XXX) = X- X(light<250) = X – P(X&XX;0.X) = X- X.788 = 0.212
b. P(lights&XX;XXX) = X (z < (XXX – 210)/XX)) = P (z< X.X) = 0.692
XXXXXXXXXX of bulbs XXXX needs XX be XXXXXXXX within 235 hours = 69.X%
c. X(XXX&XX;XXXXXX<400) = X (XXXXXX&XX;XXX)-P(lights<200) = X (z&XX; (400-XXX)/50) – X(X < (XXX-210)/XX)
= P(z&XX;X.X) – P (z< -X.2)
= 0.999 – 0.421 = X.XXX
Number of XXXXX XXXX lifetime XXXXXXX 200 XXX 400 h = 57.8% XX XXXX = 1156
XXXX = 5.4
Standard XXXXXXXXX = X.X
N = 64
XXXXX N>XX, XX can apply Central XXXXX theorem,
Mean XX XX XXXXXX to XXXX a job, XXXX = X.4
XX, XXXXXXXX error = XXXXXXXX XXXXXXXXX/XXXX(n) = X.X/XXXX(XX) = X.X/X = X.1
XX% XXXXXXXXXX interval:
XX has an interval of XXXX- (z*XX) XX mean + (z*SE)
XX% = X.95 XX XXX cumulative probability XXXXX XXXXXX X.XX/X on either XXXX XX the XXXXXXXX = X.025
P (b&XX; z &XX; a) = 0.95
P (z<a) – X(X&XX;b) = X.95
XX, P(X&XX; b) = 0.025
XX, b = -1.96
XXXXXXXXX, XX XXXXXXXX a = 1.96
Number XX months XXXXXX which a graduate will get a XXX XXXX XX% XXXXXXXXXX:
Lower limit = mean – (X*XX) = 5.X – (X.96*X.X) = 5.4 – X.196 = X.204
XXXXX limit = XXXX + (X*SE) = X.X + (1.XX*0.X) = 5.4 + X.196 = X.XXX
a. Margin of error = X%
b. Confidence interval:
P(b<readers&XX;a) = P(z<a)-X(X&XX;b) = X.95
P(z<b) = 0.025
b = -X.XX
By symmetry, a = X.XX
XX = 0.05 XXXX = X.63 XXXXXXXXX, XXX confidence XXXXXXXX is ( 0.XX (+/-) 1.XX*X.05)
XX, 0.XX-X.XXX to 0.XX+X.XXX
Or, X.XXX XX 0.XXX
Therefore, XX can XXX we with XX% XXXXXXXXXX that XX XXXXX XX.2% and XX XXX 72.X% XX XXX XXXXXX regularly XXXX at least a part of XXX XXXXXXXXX
c. Confidence XXXXX = 95%