Question

Digit wants to perform some trials with a spinner and calculate the probabilities of each food option being spun based on the results. He spins the spinner 60 times and records the results in the following table.

Options:

P({x: x is cucumber & lime mutton})
P({x: x is gooseberry & passion fruit cheesecake})
P({x: x is oven-baked apple & lavender calzone})
P({x: x is cured pasta & bear})
P({x: x is poached fennel & lemon alligator})
P({x: x is pressure-cooked mushroom & garlic chicken})
P({x: x is praline wafer})
If Digit's results show that he spun each of these food options a certain number of times in his 60 spins, and you'd like to find the probabilities of each food option being spun, please provide the specific number of times each option was spun during the trials."




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Answer 1

Digit needs to provide the number of times each food option was spun to calculate their respective probabilities. The probability of each outcome is the ratio of the number of times the event occurs to the total number of trials.

Step-by-step explanation:

To assess the probability of each option on a spinner being selected, Digit must divide the number of times each item was spun by the total number of spins (60 in this case). To compute these probabilities, specific results for each category need to be provided, which are missing in the statement. Usually, the probability of each outcome (P) is calculated using the formula P(event) = number of times the event occurs/total number of trials. For example, if cucumber & lime mutton was spun 10 times, its probability would be 10/60 = 1/6.

Probabilities in real-life situations aren't always equal due to potential biases like with coins or dice. Over time, the more trials conducted, the closer the experimental probability will come to the theoretical probability, assuming fair conditions. When dealing with events not equally likely, different statistical techniques are used. Real-life probabilities may vary due to biases, so the more trials, the more accurate the probability estimation.

In an example with jelly beans, if you wanted to find the probability of selecting a blue jelly bean (P(B)) from a jar of 150 where there are 26 blue jelly beans, you would calculate P(B) = 26/150. Similar calculations can be made for each color jelly bean to find their respective probabilities. These outcomes should be rounded to four decimal places to maintain accuracy in reporting results.