A sample is given. Which one of the following options is a possible bootstrap sample from this original sample?Original sample: 85 , 70 , 80 , 90 , 88 .Group of answer choices80 , 88 , 70 , 85 , 80 , 9080 , 88 , 90 , 85 , 69, 70None of the other answers are correct.80 , 88 , 70 , 85 , 9088 , 85 , 80 , 87 , 80

'Bootstrapping' is a really useful tool in Statistics that was developed relatively recently. Made possible with improved computing power, it is introduced in Section 3.3.Given the original sample data below, select which options are possible bootstrap samples that could have been selected from the original sample.There may be one or more correct answers. You must select all of the correct answers and none of the incorrect answers to gain full marks.Original Sample Data:[50,71,55,65,63] [64,66,51,72,55] [56,51,65,71,55] [72,64,51,56,66] [55,63,65,50,55] [56,51,64,71,55] [72,65,71,50,63] None above

Which of the following sampling methods does not lead to probability samples?Group of answer choicesconvenience samplingstratified samplingcluster samplingsystematic sampling

What is the main purpose of a bootstrap distribution?Group of answer choicesto estimate the variability of a sample statistic.to estimate any sample statistic.None of the other answers are correct.to estimate a sample mean.to estimate any population statistic.

Which of the following statements is TRUE. Group of answer choices Neither parametric nor non-parametric bootstrapping will give us a more reliable point estimate of the statistic of interest. The parametric bootstrap gives a more relaible sampling distribution when the model assumptions are violated. For the non-parametric bootstrap we expect approximately 50% of the observations are not included in any given bootstrap sample. The non-parametric bootstrap is useful in situations where the observations are not independent. The non-parametric bootstrap takes samples from the set of observations without replacement.

Which of the following statements about bootstrapping is true?<br /> A. 1. Bootstrapping can be applied to construct prediction intervals revealing uncertainty in predictions. <br />B. 2. Bootstrapping randomly selects samples with replacement from the original dataset. <br />C. 3. Bootstrapping involves creating multiple smaller datasets from the original dataset. <br />D. 4. Bootstrapping requires dividing the dataset into multiple folds for training and testing.