monammed walks 3 km from point A on aboaring OF 0230 . He then wars 4km on a bearing on 11.30 to Point Point B. What is the is t
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Answer:
The probability is
Step-by-step explanation:
We can divide the amount of favourable cases by the total amount of cases.
The total amount of cases is the total amount of ways to put 8 rooks on a chessboard. Since a chessboard has 64 squares, this number is the combinatorial number of 64 with 8,
For a favourable case, you need one rook on each column, and for each column the correspondent rook should be in a diferent row than the rest of the rooks. A favourable case can be represented by a bijective function with A = {1,2,3,4,5,6,7,8}. f(i) = j represents that the rook located in the column i is located in the row j.
Thus, the total of favourable cases is equal to the total amount of bijective functions between a set of 8 elements. This amount is 8!, because we have 8 possibilities for the first column, 7 for the second one, 6 on the third one, and so on.
We can conclude that the probability for 8 rooks not being able to capture themselves is
Answer:
a) By the Central Limit Theorem, the distribution would be approximately normal, with mean and standard deviation
b) 4.02 standard deviations below the mean.
c) Making 25 or less shots has a pvalue of -4.02. Z-scores lower than -2 are considered surprising, so yes, it would be surprising for him to make only 25 of these shots.
Step-by-step explanation:
To solve this question, we are going to need to understand the normal probability distribution and the central limit theorem.
Normal probability distribution
Problems of normally distributed samples are solved using the z-score formula.
In a set with mean and standard deviation
, the zscore of a measure X is given by:
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For proportions, we have that the mean is and the standard deviation is
In this problem, we have that:
a)By the Central Limit Theorem, the distribution would be approximately normal, with mean and standard deviation
b)This is Z when X = 0.25.
By the Central Limit Theorem
4.02 standard deviations below the mean.
c) Making 25 or less shots has a pvalue of -4.02. Z-scores lower than -2 are considered
surprising, so yes, it would be surprising for him to make only 25 of these shots.