Estimates and Estimation

Post about Estimates and Estimation that include point estimation, interval estimation, level of significance, Properties of estimators, and real-life examples about estimate and estimation.

Important MCQs Estimation 6

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MCQs Estimation Quiz covers the topics of Estimation for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These quizzes help get admission to different colleges and Universities. The MCQs Estimation Quiz will help the learner to understand the related concepts and enhance the knowledge too.

MCQs about statistical inference covering the topics estimation, estimator, point estimate, interval estimate, properties of a good estimator, unbiasedness, efficiency, sufficiency, Large sample, and sample estimation.

1. Roa-Blackwell Theorem enables us to obtain minimum variance unbiased estimator through:

 
 
 
 

2. The sample proportion $\hat{p}$ is ______ estimator

 
 
 
 

3. If $\alpha=0.10$ and $n=15$ then $t_{\frac{\alpha}{2}}$ will be

 
 
 
 

4. A _____ is the specific value of the statistics used to estimate the population parameter.

 
 
 
 

5. By the method of moments, one can estimate:

 
 
 
 

6. Sample median as an estimator of the population mean is always

 
 
 
 

7. An Estimator $\hat{T}$ is an unbiased estimator of the population parameter $T$ if

 
 
 
 

8. A quantity obtained by applying a certain rule or formula is known as

 
 
 
 

9. The estimation and testing of the hypothesis are the main branches of ________.

 
 
 
 

10. ________ is an estimate expressed by a single value.

 
 
 
 

11. If the sample average $\overline{x}$ is an estimate of the population mean $\mu$, then $\overline{x}$ is:

 
 
 
 

12. The consistency of an estimator can be checked by comparing

 
 
 
 

13. An estimator $T_n$ is said to be a sufficient statistic for a parameter function $\tau(\theta)$ if it contains all the information which is contained in the:

 
 
 
 

14. _______ is the value of a sample statistic.

 
 
 
 

15. Since $E(X)=$______. $X$ is said to be an unbiased estimator of the population mean.

 
 
 
 

16. If $n_1=16$, $n_2=9$ and $\alpha=0.01$, then $t_{\frac{\alpha}{2}}$ will be

 
 
 
 

17. Crammer-Rao inequality is valid in the case of:

 
 
 
 

18. For an estimator to be consistent, the unbiasedness of the estimator is

 
 
 
 

19. Sampling error decreases by ______ the sample size.

 
 
 
 

20. Criteria to check a point estimator to be good are

 
 
 
 

Statistical inference is a branch of statistics in which we conclude (make wise decisions) about the population parameter by making use of sample information. Statistical inference can be further divided into the Estimation of parameters and testing of the hypothesis.

Estimation is a way of finding the unknown value of the population parameter from the sample information by using an estimator (a statistical formula) to estimate the parameter. One can estimate the population parameter by using two approaches (I) Point Estimation and (ii) Interval Estimation.
In point Estimation, a single numerical value is computed for each parameter, while in interval estimation a set of values (interval) for the parameter is constructed. The width of the confidence interval depends on the sample size and confidence coefficient. However, it can be decreased by increasing the sample size. The estimator is a formula used to estimate the population parameter by making use of sample information.

Online MCQs Estimation with Answers

  • Criteria to check a point estimator to be good are
  • By the method of moments, one can estimate:
  • If the sample average $\overline{x}$ is an estimate of the population mean $\mu$, then $\overline{x}$ is:
  • A quantity obtained by applying a certain rule or formula is known as
  • The consistency of an estimator can be checked by comparing
  • For an estimator to be consistent, the unbiasedness of the estimator is
  • Sample median as an estimator of the population mean is always
  • Roa-Blackwell Theorem enables us to obtain minimum variance unbiased estimator through:
  • An estimator $T_n$ is said to be a sufficient statistic for a parameter function $\tau(\theta)$ if it contains all the information which is contained in the:
  • Crammer-Rao inequality is valid in the case of:
  • If $n_1=16$, $n_2=9$ and $\alpha=0.01$, then $t_{\frac{\alpha}{2}}$ will be
  • If $\alpha=0.10$ and $n=15$ then $t_{\frac{\alpha}{2}}$ will be
  • A _ is the specific value of the statistics used to estimate the population parameter.
  • An Estimator $\hat{T}$ is an unbiased estimator of the population parameter $T$ if
  • Since $E(X)=$_______. $X$ is said to be an unbiased estimator of the population mean.
  • The sample proportion $\hat{p}$ is ________ estimator Sampling error decreases by the sample size.
  • __________ is the value of a sample statistic.
  • __________ is an estimate expressed by a single value.
  • The estimation and testing of the hypothesis are the main branches of ________.
MCQs Estimation

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Important Statistical Inference Quiz 5

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MCQs from the Statistical Inference Quiz cover the topics of estimation and hypothesis testing for the preparation of exams and different statistical job tests in the government/semi-government or private organization sectors. These Quizzes are also helpful in getting admission to other colleges and Universities. The Estimation Statistical Inference Quiz will help the learner understand the related concepts and enhance their knowledge.

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Statistical inference is a branch of statistics in which we conclude (make wise decisions) about the population parameter by making use of sample information. Statistical inference can be further divided into Estimation of parameters and testing of the hypothesis.

Estimation is a way of finding the unknown value of the population parameter from the sample information by using an estimator (a statistical formula) to estimate the parameter. One can estimate the population parameter by using two approaches (I) Point Estimation and (ii) Interval Estimation.
In point Estimation, a single numerical value is computed for each parameter, while in interval estimation a set of values (interval) for the parameter is constructed. The width of the confidence interval depends on the sample size and confidence coefficient. However, it can be decreased by increasing the sample size. The estimator is a formula used to estimate the population parameter by making use of sample information.

Statistical Inference Quiz

  • The following statistics are unbiased estimators
  • A statistic is an unbiased estimator of a parameter if:
  • Which one of the following is a biased estimator?
  • For $n$ paired number of observations, the degrees of freedom for the Paired Sample t-test will be
  • If t-distribution for two independent samples $n_1=n_2=n$, then the degrees of freedom will be
  • If $1-\alpha=0.90$ then value of $Z_{\frac{\alpha}{2}}$ is
  • If the population standard deviation ($\sigma$) is known and the sample size ($n$) is less than or equal to or more than 30, the confidence interval for the population mean ($\mu$) will be
  • If the population standard deviation ($\sigma$) is unknown and the sample size ($n$) is greater than 30, the confidence interval for the population mean $\mu$ is
  • If the population standard deviation $\sigma$ is unknown and the sample size $n$ is less than or equal to 30, the confidence interval for the population mean $\mu$ is
  • Suppose the 90% confidence Interval for population mean $\mu$ is -24.3 cents to 64.3 cents, the sample mean $\overline{X}$ is
  • A 95% confidence interval for a population proportion is 32.4% to 47.6%, and the value of the sample proportion $\hat{p}$ is
  • For a normal population with a known population standard deviations $\sigma_1$ and $\sigma_2$, the confidence interval estimate for the difference between two population means $(\mu_1-\mu_2)$ is
  • If $n_1, n_2\le 30$ the confidence interval estimate for the difference of two population means ($\mu_1-\mu_2$) when population standard deviations $\sigma_1, \sigma_2$ are unknown but equal in case of pooled variates is:
  • In the case of paired observations (for a small sample $n\le 30$), the confidence interval estimate for the difference of two populations means $\mu_1-\mu_2=\mu_d$ is
  • For a large sample, the confidence interval estimate for the difference between two population proportions $p_1-p_2$ is
  • Each of the following increases the width of a confidence interval except
  • ‘Statistic’ is an estimator, and its computed value(s) is called
  • Confidence lists for mean when population SD is known
  • Mean and median are both estimators of population mean _________.
  • What does it mean when someone calculates a 95% confidence interval?
Statistical Inference Quiz

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Confidence Interval MCQs 4

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MCQs from Statistical Inference covering the topics of Estimation Confidence Interval MCQs for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These tests are also helpful in getting admission to different colleges and Universities. The Estimation and Confidence interval MCQs will help the learner to understand the related concepts and enhance the knowledge too. Let us start with Confidence Interval MCQs

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Most of the MCQs on this page are covered from Estimate and Estimation, Testing of Hypothesis, Parametric and Non-Parametric tests, etc.

Statistical inference is a branch of statistics in which we conclude (make wise decisions) about the population parameter by making use of sample information. Statistical inference can be further divided into the Estimation of parameters and testing of the hypothesis.

Estimation is a way of finding the unknown value of the population parameter from the sample information by using an estimator (a statistical formula) to estimate the parameter. One can estimate the population parameter by using two approaches (I) Point Estimation and (ii) Interval Estimation.

In point Estimation, a single numerical value is computed for each parameter, while in interval estimation a set of values (interval) for the parameter is constructed. The width of the confidence interval depends on the sample size and confidence coefficient. However, it can be decreased by increasing the sample size. The estimator is a formula used to estimate the population parameter by making use of sample information.

Confidence Interval MCQs Estimation

Online Confidence Interval MCQs

  • Estimates given in the form of confidence intervals are called
  • $(1-\alpha)$ is called
  • If $(1-\alpha)$ is increased, the width of a confidence interval is
  • By decreasing the sample size, the confidence interval becomes
  • The confidence interval becomes narrow by increasing the
  • The distance between an estimate and the estimated parameter is called
  • By increasing the sample size, the precision of the confidence interval is _______
  • The number of values that are free to vary after a certain restriction is applied to the data is called
  • A 95% confidence interval for the mean of a population is such that A confidence interval will be widened if
  • A statistician calculates a 95% confidence interval for $\mu$ and $\sigma$ is known.
  • The confidence interval is RS 18000 to RS 22000, and the amount of the sample mean $\overline{X}$ is
  • If the population standard deviation $\sigma$ is known, the confidence interval for the population mean $\mu$ is based on
  • If the population standard deviation $\sigma$ is unknown, and the sample size is small ($n\le 30$), the confidence interval for the population mean $\mu$ is based on
  • The shape of the t-distribution depends upon the
  • If the population standard deviation $\sigma$ is doubled, the width of the confidence interval for the population mean $\mu$ (the upper limit of the confidence interval — the lower limit of the confidence interval) will be
  • A range of values calculated from the sample data and it is likely to contain the true value of the parameter with some probability is called
  • The estimator is said to be ________ if the mean of the estimator is not equal to the mean of the population parameter.
  • Estimation can be classified into
  • A single value used to estimate the value of the population parameter is called
  • The probability associated with the confidence interval is called

It is important to note that the point estimates are simpler to calculate but lack information about precision. On the other hand, interval estimates provide more information but require more calculations too, and often rely on assumptions about the data. Therefore, the choice between point estimation and interval estimation depends on the specific research question and how much detail a researcher needs about the population parameter being estimated.

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Estimation Statistics MCQs 3

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Estimation Statistics MCQs Quiz covers the topics of Estimate and Estimation for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These tests are also helpful in getting admission to different colleges and Universities. The Estimation Statistics MCQs Quiz will help the learner to understand the related concepts and enhance their knowledge too.

Please go to Estimation Statistics MCQs 3 to view the test

Statistical inference is a branch of statistics in which we conclude (make wise decisions) about the population parameter by making use of sample information. Statistical inference can be further divided into the Estimation of parameters and testing of the hypothesis.

Estimation is a way of finding the unknown value of the population parameter from the sample information by using an estimator (a statistical formula) to estimate the parameter. One can estimate the population parameter by using two approaches (I) Point Estimation and (ii) Interval Estimation.

Estimation, point estimate and Interval Estimate

In point Estimation, a single numerical value is computed for each parameter, while in interval estimation a set of values (interval) for the parameter is constructed. The width of the confidence interval depends on the sample size and confidence coefficient. However, it can be decreased by increasing the sample size. The estimator is a formula used to estimate the population parameter by making use of sample information.

Estimation Statistics

Online Estimation Statistics MCQs

  • The process of making estimates about the population parameter from a sample is called
  • There are two main branches of statistical inference, namely
  • Estimation can be classified into
  • A formula or rule used for estimating the parameter of interest is called:
  • ‘Statistic’ is an estimator and its computer values are called:
  • The estimate is the observed value of an:
  • The process of using sample data to estimate the values of unknown population parameters is called
  • The numerical value which we determine from the sample for a population parameter is called
  • A single value used to estimate a population value is called:
  • A set (range) of values calculated from the sample data and is likely to contain the true value of the parameter with some probability is called:
  • A range (set) of values within which the population parameter is expected to occur is called:
  • The end points of a confidence interval are called:
  • The probability associated with confidence interval is called
  • The estimator is said to be ________ if the mean of the estimator is not equal to the mean of the population parameter.
  • If $\hat{\theta}$ is the estimator of the parameter $\theta$, then $\hat{\theta}$ is called unbiased if:
  • The value of a statistic tends towards the value of the population as the sample size increases. What is it said to be?
  • For computing the confidence interval about a single population variance, the following test will be used
  • The end points of a confidence interval are called
  • The difference between the two end points of a confidence interval is called
  • The estimate is the observed value of an
Estimation Statistics MCQs

Estimation is a fundamental part of statistics because populations can be very large or even infinite, making it impossible to measure every single member. By using estimation techniques, we can draw conclusions about the bigger picture from a manageable amount of data.

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