Sampling Distribution MCQs 14

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Free quiz on sampling distribution MCQs with answers. Covers standard deviation of sampling distribution, types of bias, cluster vs stratified sampling, and formulas. Essential prep for data analytics and statistics exams. Let us start with the online sampling distribution MCQs now.

Online Sampling Distribution MCQs with Answers

Online Sampling and Sampling Distribution MCQs with Answers

1. In systematic sampling, the population is 200, and the selected sample size is 50; then the sampling interval is

 
 
 
 

2. A type of stratified proportion sampling in which information is gathered on a convenience basis from different groups of the population is classified as

 
 
 
 

3. If the standard deviation of the population is 35 and the sample size is 9, then the standard deviation of the sampling distribution is

 
 
 
 

4. Regardless of the difference in the distribution of the sample and population, the mean of the sampling distribution must be equal to

 
 
 
 

5. In cluster sampling, elements of selected clusters are classified as

 
 
 
 

6. Methods in statistics that use sample statistics to estimate the parameters of a population are considered as

 
 
 
 

7. Method of sampling in which the population is divided into mutually exclusive groups that have useful context in statistical research is classified as

 
 
 
 

8. Bias in which a few respondents respond to the offered questionnaire is classified as

 
 
 
 

9. In statistical analysis, a sample size is considered large if

 
 
 
 

10. A principle that states that a larger sample size larger accuracy and stability is part of

 
 
 
 

11. Listing of elements in the population with an identifiable number is classified as

 
 
 
 

12. Bias occurred in the collection of the sample because of confusing questions in the questionnaire is classified as

 
 
 
 

13. If the standard deviation of the population is known, then $\mu$ must be equal to

 
 
 
 

14. Mistakes or biases that are considered causes of non-sampling errors must include

 
 
 
 

15. Cluster sampling, stratified sampling, and systematic sampling are types of

 
 
 
 

16. If the population parameter $\mu$ and an unbiased estimate of the population is $\overline{x}$, then the sampling error is as

 
 
 
 

17. If the mean of the population is 25, then the mean of the sampling distribution is

 
 
 
 

18. Parameters of the population are denoted by the

 
 
 
 

19. In systematic sampling, the value of $k$ is classified as

 
 
 
 

20. An unknown or exact value that represents the whole population is classified as

 
 
 
 


Online Sampling Distribution MCQs with Answers

  • If the standard deviation of the population is 35 and the sample size is 9, then the standard deviation of the sampling distribution is
  • In systematic sampling, the value of $k$ is classified as
  • A type of stratified proportion sampling in which information is gathered on a convenience basis from different groups of the population is classified as
  • Parameters of the population are denoted by the
  • Mistakes or biases that are considered causes of non-sampling errors must include
  • Regardless of the difference in the distribution of the sample and population, the mean of the sampling distribution must be equal to
  • Cluster sampling, stratified sampling, and systematic sampling are types of
  • Bias occurred in the collection of the sample because of confusing questions in the questionnaire is classified as
  • Bias in which a few respondents respond to the offered questionnaire is classified as
  • A principle that states that a larger sample size larger accuracy and stability is part of
  • An unknown or exact value that represents the whole population is classified as
  • Listing of elements in the population with an identifiable number is classified as
  • In statistical analysis, a sample size is considered large if
  • If the standard deviation of the population is known, then $\mu$ must be equal to
  • Methods in statistics that use sample statistics to estimate the parameters of a population are considered as
  • In systematic sampling, the population is 200, and the selected sample size is 50; then the sampling interval is
  • In cluster sampling, elements of selected clusters are classified as
  • Method of sampling in which the population is divided into mutually exclusive groups that have useful context in statistical research is classified as
  • If the mean of the population is 25, then the mean of the sampling distribution is
  • If the population parameter $\mu$ and an unbiased estimate of the population is $\overline{x}$, then the sampling error is as

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Linear Regression Quiz 15

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Test your understanding of fundamental linear regression concepts with this Linear Regression Quiz. The Linear Regression Quiz covers key properties of regression and correlation coefficients, their invariance under data transformations, and includes practical problems on calculating correlation, regression lines, and interpreting results. Perfect for statistics students and data analysts. Let us start with the Linear Regression Quiz now.

Online Linear Regression Quiz with Answers
Please go to Linear Regression Quiz 15 to view the test

Online Linear Regression Quiz with Answers

  • Regression coefficients are independent of the change of
  • If $K$ is the arithmetic mean between the two regression coefficients and $r$ is the correlation, then which of the following must be true?
  • If we add or subtract any constant number from each observation of data, then the regression coefficients:
  • If we multiply or divide any constant number by each value of the variable, then the regression coefficients
  • If we add or subtract any constant number in each of the variables involved in the data, then the value of $r$ is
  • If we multiply or divide any constant number by each of the variables involved in the data, then the value of $r$ is
  • If all the observation points in the bi-variate data are defined in KMS, then the value of the correlation coefficient is in
  • If $b_{yx} = -\frac{3}{2}$ and $b_{xy} = -\frac{1}{6}$ then the value of $r$ is
  • Two judges $A$ and $B$, have given marks to seven students as follows”: The regression coefficient of $Y$ on $X$ and $X$ on $Y$ are
  • The average marks given by Judge-A and Judge-B are  
  • The correlation coefficient between the marks given by two judges in
  • The regression line of marks given by Judge-B than the marks given by Judge-A in the following data is
  • When Judge-B has given 50 marks, then the best estimated marks given by Judge-A are in the data below.
  • When Judge-A has given 45 marks, then the error in estimation is
  • The two regression lines are $X+2Y-5=0$ and $2X+3Y-8=0$. If the variance of $Y$ is 4 then the variance of $X$ is
  • The regression equation $Y$ on $X$ and $X$ on $Y$ are $9X+nY+8=0$ and $2X+Y-m=0$ and also the mean of $X$ and $Y$ are $-1$ and 4, respectively, then the values of $m$ and $n$ are
  • The error in the case of regression analysis may be
  • In the regression line $Y$ on $X$, the variable $X$ is so called
  • In the regression line $Y$ on $X$; $Y=a+bX$, $a$ is known as
  • A Q-Q plot (Quantile-Quantile plot) of your regression residuals is used primarily to check which assumption?

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Understanding Advanced SAS Procedures

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Master advanced statistical modeling in SAS with our detailed question-and-answer guide. This Understanding Advanced SAS Procedures post explains the core statements and functionality of essential SAS procedures like PROC NLIN for nonlinear regression, PROC NLMIXED for nonlinear mixed models, PROC GLIMMIX for linear and generalized linear mixed models, and PROC PROBIT for dose-response analysis. Learn how to use PARMS, MODEL, RANDOM, and CLASS statements correctly, avoid common syntax errors, and interpret your results with practical examples from the sashelp.cars and sashelp.iris datasets. Perfect for data analysts and statisticians looking to deepen their SAS programming skills.

Understanding Advanced SAS Procedures

Understanding Advanced SAS Procedures

Explain the following SAS Statements used in the Example below (Non-Linear Mixed Model)

proc nlmixed data = CARS;
parms b1 = 220 b2 = 500 b3 = 310 s2u = 100 s2e = 60;
model X ~ normal(num/den, s2e);
random u1 ~ normal(0, s2u) subject = NUMBER;
run;

This is an excellent example of a nonlinear mixed model in SAS. The MIXED MODEL statement defines the dependent variable and its conditional distribution given the random effects. In the above statement, a normal (Gaussian) conditional distribution is specified.

This code is fitting a nonlinear mixed-effects model to data about cars (from the CARS dataset). It is trying to estimate parameters ($b_1, b_2$, and $b_3$) for a specific nonlinear relationship between a predictor and the outcome $X$, while also accounting for random variations between different groups of cars (grouped by NUMBER).

The RANDOM statement defines the single random effect to be $u1$, and specifies that it follows a normal distribution with mean $0$ and variance $s2u$. The SUBJECT= statement in the RANDOM statement is used to define a variable that will indicate when the random effect obtains new realizations.

Explain the following SAS statements (Linear Mixed Model) in the example below

proc glimmix data = sashelp.iris;
class species;
model age = weight;
random age = weight;
run;

The CLASS statement instructs the technique to treat the variable species as type variables. The version announcement in the example shown above specifies the reaction variable as a pattern proportion by means of the use of the occasions/trials approach.

This PROC GLIMMIX code contains a critical error in its RANDOM statement, which makes the model, as written, invalid and nonsensical.

In code, it is trying to fit a linear mixed model to the sashelp.iris dataset (famous Fisher’s Iris data). The intent might have been to see how age (which does not exist in the standard iris dataset) is related to weight (which also does not exist), while accounting for the grouping structure of species. The syntax of the RANDOM statement is completely incorrect.

Explain the use of each SAS statement (PROC PROBIT) given below

PROC PROBIT dataset;
CLASS <dependent variables>;
Model < dependent variables > = <independent VARIABLES>;

This statement outlines the basic structure for using PROC PROBIT in SAS, but it contains a few common misunderstandings and a critical error in the CLASS statement. However, the line-by-line explanation of the code is:

The DATA= option specifies the dataset that will be studied.

The PLOTS= choice within the PROC PROBIT statement, collectively with the ODS graphics announcement, requests all plots (as all have been specified in brackets, we will pick out a selected plot also) for the anticipated opportunity values and peak ranges.

The model statement prepares a response between a structured variable and independent variables. The variables top and weight are the stimuli or explanatory variables.

Explain the following SAS example (PROC NLIN)

proc nlin data = sashelp.cars method = gauss;
parms hosepower = 135
cylinders = 6;
model mpg_highway = (horsepower/cylinders);
run;

This code is used to fit a nonlinear regression model (PROC NLIN) to car data. The METHOD = option directs PROC NLIN to use the GAUSS iterative method. The PARMS statement declares the parameters and specifies their initial values.

The code is trying to model a car’s highway fuel efficiency (mpg_highway) as a simple nonlinear function of its power (horsepower) and engine size (cylinders). Specifically, it is testing the hypothesis that highway MPG is directly proportional to the power-per-cylinder (horsepower / cylinders). The code contains a critical error in its model specification, which will cause it to fail.

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