The distribution function of the univariate random variable x is continuous at x if and only if , F (x) = P (X ≤ x)
Continuous univariate statistical distributions are functions that describe the likelihood that a random variable, say, X, falls within a given range. Let P (a Xb) represent the probability that X falls within the range [a, b].
A numerically valued variable is said to be continuous if, in any unit of measurement, whenever it can take on the values a and b. If the random variable X can assume an infinite and uncountable set of values, it is said to be a continuous random variable.
If X can take any specific value on the real line, the probability of any specific value is effectively zero (because we'd have a=b, which means no range). As a result, continuous probability distributions are frequently described in terms of their cumulative distribution function, F(x).
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Answer:
Step-by-step explanation:
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It shows the Ora scale for area multiply width and length
The 90% confidence interval for the population mean is 78.1905<x<81.8095
<h3>Confidence interval</h3>
The formula for calculating the confidence interval is expressed as:
CI = x ± z*s/√n
Given the following parameters
mean "x" = 80
z = 1.645
s = 5.5
n = 25
Substitute
CI = 80 ± 1.645*5.5/√25
CI = 80 ± 1.8095
CI = {78.1905, 81.8095}
Hence the 90% confidence interval for the population mean is 78.1905<x<81.8095
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9 Is in the hundred million place
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