Question # 1

Types of statistical inferences are

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one

two

three

four

A specific value of an estimator computed from the sample data is called

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estimation

estimate

interval estimate

point estimate

Confidence intervals which are often used in practice are

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90%

95%

98%

all of these

If the observations are paired and the number of pairs is n, then the number of degree of freedom is equal to

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n

n - 1

2n

2n - 1

If population proportion (P) is unknown, the standard error of the sample proportion (p) can be estimated by the formula

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The following statistic are unbiased estimators:

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The Sample mean

S² = Σ(X - X)²/n-1

The sample proportion

All the above

Large sample contains more than

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5 values

10 values

20 values

30 values

A range of values within which the population parameter is expected:

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Confidence interval

Confidence coefficient

Confidence limits

Level of significance

Level of significance is denoted by

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2 - α

3 - α

α

1 - α

Statistical inference has two branches namely:

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Level of confidence and degrees of freedom

Biased estimator and unbiased estimator

Point estimate and interval estimate

Estimation of parameter and testing of hypothesis

Statistic is an estimator and its calculated value is called:

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Biased estimate

Estimation

Interval estimate

Estimate

The endpoints of a confidence interval are called:

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confidence coefficient

Confidence limits

Error of estimation

Parameters

(1-α) is called:

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Critical value

Level of significance

Level of confidence

Interval estimate

100(1-α)% confidence interval for population proportion of success,&=nbsp;π is

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P (L <μ < U) = 1 - α

P (L <σ < U) = 1 - α

P (L <π< U) = 1 - α

P (L <P&=nbsp;< U) = 1 - α

By increasing the sample size, the precision of confidence interval is:

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Decreased

Increased

Constant

Unchanged

A single value used to estimate a population value is called:

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Interval estimate

Point estimate

Confidence interval

Level of confidence

The distance between an estimate and the estimated parameter is called:

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Sampling error

Standard error

Bias

Error of estimation

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