Test: Theoretical Distributions- 1

does not exist.

exists only in theory.

exists in real life.

both (b) and (c).

discrete.

continuous.

infinite.

both (a) and (b).

Poisson distribution.

Normal distribution.

Cauchy distribution.

Log normal distribution.

Binomial distribution.

Poisson distribution.

Geometric distribution.

Chi-square distribution.

population.  

sample.    

probability distribution.  

both (a) and (b).

sample mean.

population mean.

binomial distribution.

sample size.

make something possible.

make something impossible.

prosecute an offender in a court of law.

produce an outcome which is neither certain nor impossible.

each trial is associated with just two possible outcomes.

trials are independent.

trials are infinite.

both (a) and (b).

f(x) = p^x q ^n–x.

f(x) = ⁿc_x p^x q ^n–x.

f(x) = ⁿc_x q^x p ^n–x.

f(x) = ⁿc_x p^n–x q ^x.

any value between 0 and n.

any value between 0 and n, both inclusive.

any whole number between 0 and n, both inclusive.

any number between 0 and infinity.

never symmetrical.

never positively skewed.

never negatively skewed.

symmetrical when p = 0.5.

n (1– p).

np (1 – p).

np.

np² (1 – p).

nq (1 – q).

n²p² (1– p)².

binomial distribution.

poisson distribution.

normal distribution.

both (a) and (b).

are never equal.

are always equal.

are equal when q = 0.50.

do not always exist.

always more than its variance.

always equal to its variance.

always less than its variance.

always equal to its standard deviation.

one mode.

two mode.

(a).

(a) or (b).

n/2.

n/4.

np (1 – p).

2n.

method of least square.

method of moments.

method of probability distribution.

method of deviations.

the probability of having success in a small time interval is constant.

the probability of having success more than one in a small time interval is very small.

the probability of having success in a small interval is independent of time and also of earlier success.

the probability of having success in a small time interval (t, t + dt) is kt for a positive constant k.

Binomial.

Poisson.

Normal.

Hyper geometric.

mean and standard deviation are equal.

mean and variance are equal.

standard deviation and variance are equal.

both (a) and (b).

unimodal.

bimodal.

Multi-modal.

(a) or (b).

always symmetric.

always positively skewed.

always negatively skewed.

symmetric only when m = 2.

m → ∝.

p → 0.

m → ∝ and p → 0.

m → ∝ and p → 0 so that mp remainsfinite..

we equate the Poisson parameter to the mean of the frequency distribution.

we equate the Poisson parameter to the median of the distribution.

we equate the Poisson parameter to the mode of the distribution.

none of these.

Binomial distribution.

Normal distribution.

Chi-square distribution.

sampling distribution.

none of these.

one.

50 per cent.

0.50.

any value between 0 and 1.

Bell-shaped.

U- shaped.

J- shaped.

Inverted J – shaped.

positively skewed.

negatively skewed.

Symmetrical.

all these.

between – ∝ to µ is 0.50.

between µ to ∝ is 0.50.

between – ∝ to ∝ is 0.50.

both (a) and (b).

may be equal.

may be different.

are always equal.

(a) or (b).

0.675 σ.

0.675.

0.80 σ.

0.80.

0.675.

67.50.

2.70.

3.20.

µ – σ and µ + σ.

– σ and σ.

–1 and 1.

0 and 1.

0 to a.

a to ∞.

– ∝ to a.

– ∝ to ∝.

95% area of a normal distribution.

96% area of a normal distribution.

99% area of a normal distribution.

all but 0.27% area of a normal distribution.

Normal distribution .

Poisson distribution.

Binomial distribution.

Standard normal distribution.