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Using the Central Limit Theorem, it is found that a larger sample size reduces the variability, that is, the experimental probabilities will be closer to the theoretical probabilities, which explains why the actual results are closer to the expected probability of 1/2 when rolling the cube 100 times.

<h3>What does the Central Limit Theorem states?</h3>

It states that for a proportion p in a sample of size n, the sampling distribution of sample proportion is approximately normal with mean $\mu = p$ and standard error $s = \sqrt{\frac{p(1 - p)}{n}}$ , as long as $np \geq 10$ and $n(1 - p) \geq 10$ .

That is, the standard error is inverse proportional to the square root of the sample size, hence a larger sample size reduces the variability, that is, the experimental probabilities will be closer to the theoretical probabilities, which explains why the actual results are closer to the expected probability of 1/2 when rolling the cube 100 times.

More can be learned about the Central Limit Theorem at brainly.com/question/16695444

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A bacteria population experiences continuous growth. If there were 2,000 bacteria present at the start of an experiment, how lon

allochka39001 [22]

Answer:

D. About 15.69 hours.

Step-by-step explanation:

Let x be the number of hours.

We have been given that there were 2,000 bacteria present at the start of an experiment and the growth rate was 7% per hour.

Since number of bacteria is growing exponentially, so we will use exponential growth function to solve our given problem.

The continuous exponential growth formula is in form: $y=e^{kx}$ , where,

e= mathematical constant,

k = Growth rate in decimal form.

Let us convert our given rate in decimal form.

$7\%=\frac{7}{100}=0.07$

Upon substituting our given values we will get exponential function for bacteria growth as: $y=2,000*e^{0.07x}$ , where, y represents number of bacteria after x hours.