The area to the left of (μ+σ) for a normal distribution is approximately equal to:
0.16
0.34
0.5
0.84
42 practice sets · Page 2 of 3
The area to the left of (μ+σ) for a normal distribution is approximately equal to:
0.16
0.34
0.5
0.84
Pearson's constants for a normal distribution with mean μ and variance σ2 are:
β1=0, β2=0, γ1=0, γ2=0
β1=0, β2=1, γ1=1, γ2=3
β1=0, β2=3, γ1=0, γ2=0
β1=3, β2=0, γ1=0, γ2=0
In normal probability distribution for a continuous random variable, the value of a mean deviation is approximately equal to:
(2/3)
2/3 σ
(4/5)
4/5 σ
In normal distribution:
Mean = Median = Mode
Mean < Median < Mode
Mean > Median > Mode
Mean ≠ Median ≠ Mode
In a standard normal distribution, the value of mode is:
Equal to zero
Less than zero
Greater than zero
Exactly one
In a standard normal distribution, the area to the left of Z = 1 is :
0.6413
0.7413
0.8413
0.3413
In a normal probability distribution of a continuous random variable, the value of standard deviation is:
Zero
Less than zero
Greater than zero
None of the above
In a normal distribution, the lower and upper quartiles are equidistant from the mean and are at a distance of:
0.7979
0.7979 σ
0.6745
0.6745 σ
In a normal distribution whose mean is land standard deviation 0, the value 4 quartile deviation is approximately:
(4/5)
4/5 σ
2/3 σ
(2/3)
In a normal curve, the ordinate is highest at:
Mean
Variance
Standard deviation
Q1
In a normal curve, the highest point on the curve occurs at the mean, μ, which is also the:
Median and mode
Geometric mean and harmonic mean
Lower and upper quartiles
Variance and standard deviation
In a normal curve μ ± 0.6745σ covers:
50% area
68.27% area
95.45% area
99.73% area
If Z~N, then μ4 is equal to:
1
3
σ4
If Z~N, the coefficient of variation is equal to:
Zero
One
Infinity
Hundred percent
If X~N(100, 64), then standard deviation σ is:
100
64
8
100 - 64 = 36
If X is a normal random variable having mean μ, then E|X - μ| is equal to:
Variance
Standard deviation
Quartile deviation
Mean deviation
If X is a normal random variable having mean μ, then E(X - μ)2 is equal to:
σ2
σ
3σ4
β1
If X ~ N(μ,σ2), the standard normal variate is distributed as:
N(1,0)
N(0,1)
N(μ,0)
N(0,σ2)
If X ~ N(μ,σ2), the points of inflection of normal distribution are:
±σ
±μ
σ ± μ
μ ± σ
Given a standardized normal distribution (with a mean of zero and a standard deviation of one), P(Z < variance) is equal to:
0.8413
0.3413
0.1587
0.5