Answer:
Given that the events A and B are mutually exclusive.
P(A) = 0.1
P(B) = 0.4
Mutually Exclusive Events: When two events are Mutually Exclusive it is impossible for them to happen together i.e
If A and B are two events then; P(A and B) = 0
then;
P(A or B) = P(A) +P(B)
By the definition of mutually exclusive events;
P(A or B) = P(A) +P(B) ......[1]
Substitute the values of P(A) = 0.1 and P(B) = 0.4 in [1] we have;
P(A or B) = P(A) +P(B) = 0.1+0.4 = 0.5
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Answer:
= bruv
Step-by-step explanation:
you spelt power wrong too
Answer:
Step-by-step explanation:
Data given and notation
n=250 represent the random sample taken
estimated proportion of readers owned a particular make of car
is the value that we want to test
z would represent the statistic (variable of interest)
represent the p value (variable of interest)
Concepts and formulas to use
We need to conduct a hypothesis in order to test the claim that that the percentage is actually different from the reported percentage.:
Null hypothesis:
Alternative hypothesis:
When we conduct a proportion test we need to use the z statistic, and the is given by:
(1)
The One-Sample Proportion Test is used to assess whether a population proportion is significantly different from a hypothesized value .
Calculate the statistic
Since we have all the info requires we can replace in formula (1) like this:
Answer:
The calculated value of test statistic is z=2.48.
This has a P-value of P=0.00657.
If we state the null hypothesis at a significance level of , we would reject this null hypothesis as .
Step-by-step explanation:
We have in this problem, a hypothesis test of proportions.
The test statistic for this is the z-value, and is calculated like that:
Where the term 0.5/N is the correction for continuity and is negative in the cases that p>π.
p: proportion of the sample; π: proportion of the population; σ: standard deviation of the population.
The standard deviation of the population has to be calculated as:
The proportion of the sample (p) is .
Then, the test statistic z is
The P-value of this statistic is P(z>2.48)=0.00657
If we state the null hypothesis at a significance level of , we would reject this null hypothesis as .
When a dividend is divided by a divisor that is less than 1, the resulting quotient is greater than the original dividend. This is equivalent to multiplying the dividend by the reciprocal of the divisor.
In mathematics, when we divide any number (the dividend) by a number that is less than 1 (the divisor), the quotient will be greater than the dividend. This is because dividing by a number less than 1 is equivalent to multiplying by its reciprocate which is more than 1. For example, let's consider 10 divided by 0.5 (which is less than 1); the quotient is 20, which is greater than the dividend (10). Therefore, in relation to your question, the quotient is larger than the dividend when the divisor is less than 1.
#SPJ2
Answer:
Greater provided the divisor is positive.
It would be more accurate and clearer to say that when dividing a number which is greater than zero by a number between 0 and 1, then the quotient is greater than the dividend.
If the divisor is negative and the dividend is positive, then the quotient is negative (and so less than the dividend).
Answer:
The probabilities of Type I is 0.10.
The probability of type II error is 0.3
Step-by-step explanation:
Consider the provided information.
Type I error: If we reject the null hypothesis when null hypothesis is true then it is called type I error.
The type I error is denoted by α.
Type II error: If we fail to reject the null hypothesis when null hypothesis is false then it is called type II error.
The type II error is denoted by β.
It is given that significance level α = 0.10.
Thus, the probabilities of Type I is 0.10.
The power of the test is:
It is given that power is 0.7.
Therefore,
Hence, the probability of type II error is 0.3