Question 19 Not yet an swered The objective or relative frequency interpretation of probability is justified by the Law of large numbers' According to this interpretation, if B is the event that a certain brand of dishwasher will need service while under warranty, and P(B)=0

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Question 19 Not yet an swered The objective or relative frequency interpretation of probability is justified by the Law of large numbers' According to this interpretation, if B is the event that a certain brand of dishwasher will need service while under warranty, and P(B)=0.1, this HE) is interpreted to mean Marked °"' "f Select one or more: 1.00 '7 Fl a. That if we observe a very large number of washers, 10% of them will need service while under warranty. as ""95"" b. In the long run, 10% of all such dishwashers will need service while under warranty cl one out of every 10 washers will need service while under warranty do twenty out at 200 will need service while under warranty. -

Question 20 Not yet an swered Marked cut of 1.00 \7 Flag question The law of large numbers (LLN) added only what to the belief that more observations obviously give more accurate estimates of the chances? Select one or more: - a. The LLN (Law of Large Numbers) showed thatthe probability that the estimate is close to the truth increases with the number of trials. b' The LLN tells us that we can be more certain that long observations give us accurate estimates the more the observations made, c. The LLN Iegitimizes the frequentist definition of probability. d. The LLN is saying the same thing as the central limit theorem.

Question 21 Not yet a nswered Marked out of 1.00 F Flag question Study Side Box 13 to see the difference between simulation and random experiment' An experiment consists of , whereas a simulation consists of , For example, if I go to the app at https://www.randomservicescrg/random/a pps/cheExperIment.ntml I can do simulations by assuming a particular model (making assumptions) about the roll of a six-sided dicet If I choose n=1and Y=sum, the sum is just the number of the roll, and the distribution of the data generated by 1000 rolls of the Fair six-sided die is close to , However, if for n=1, Y=sum, I choose the option "skewed left", and roll 1000 times, the data generated will be such that the number 6 is the , and the number'l the , The model assumed (Dist in the app) for the skewed left dice in this app gives a probability of to the number 6, but the data gives an estimated probability of , That makes sense because the Dist column is the model and the Data contains the empirical proportions observed in a finite number of rolls in the simulation, If I gave you a six sided dice, you would not know that it is fair until you roll it many times and observe a proportion for all the numbers 1 to 6' You would not discover the model until you have done the simulation repeat an activity under the same conditions many times and observe how often an event of interest happens to learn the proportion of times that event happens and from there estimate a possible model for the variable of int assuming a model is true and generating data from that model to see the proportion of time an event happens under that modelt uniform most likely to happen Least likely to happen 0286 approximately 0286 equally likely to happen 75 uniform