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).
To learn more about univariated data
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Answer:
There is no sufficient evidence to reject the company's claim at the significance level of 0.05
Step-by-step explanation:
Let 
be the true mean weight per apple the company ship. We want to test the next hypothesis
vs 
(two-tailed test).
Because we have a large sample of size n = 49 apples randomly selected from a shipment, the test statistic is given by
which is normally distributed. The observed value is
. The rejection region for 
is given by RR = {z| z < -1.96 or z > 1.96} where the area below -1.96 and under the standard normal density is 0.025; and the area above 1.96 and under the standard normal density is 0.025 as well. Because the observed value 1.4583 does not fall inside the rejection region RR, we fail to reject the null hypothesis.
Step-by-step explanation:
When the denominator is zero, the expression is undefined
2x-4=0
2x=4
x=2
