Explanation:
The sample mean is not always equal to the population mean but if we take more and more number of samples from the population then the mean of the sample would become equal to the population mean.
The Central Limit Theorem states that we can have a normal distribution of sample means even if the original population doesn't follow normal distribution, But we have to take a lot of samples.
Suppose a population doesn't follow normal distribution and is very skewed then we can still have sampling distribution that is completely normal if we take a lot of samples.
Answer:
Juan wins the race
Step-by-step explanation:
<u>The graph is shown in attached image.</u>
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The black line is Juan's graph.
The green line is Antonio's graph.
The graph shows the distance (y-axis) with time (x-axis).
The end of the curve(s) means the end of the race. Both curve's ending point in y-axis is 4 miles so the end of the race is 4 miles.
But in x-axis, we see the time:
Juan finishes at 13 minutes
Antonio finishes at 15 minutes
<u>Definitely Juan wins the race</u>
She walks 6/5 of a mile in 1 hour
So, she walks 1.2 miles per hour
569/x=100/18
multiply both sides of the equation
569=5.55556
now we divide
569/5.55556
102.42=x
18% of 569=102.42