Pls help 10 points!!!!!
1 answer:
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Answer:
<em>11 years</em>
Step-by-step explanation:
This is a case of exponential decay.
Let n = number of years; p = population after n years; a = initial population; r = rate of decay
p = a(1 - r)^n
Now we substitute the numbers we know leaving n as the only unknown.
100 = 250(1 - 0.08)^n
100 = 250(0.92)^n
Divide both sides by 250.
0.4 = 0.92^n
Take the log base ten of each side.
log 0.4 = log 0.92^n
Use log rule: log a^n = n * log a
log 0.4 = n * log 0.92
Divide both sides by log 0.92
n = 10.989
Answer: 11 years
Answer:
Step-by-step explanation:
Data given and notation
n=1500 represent the random sample taken
estimated proportion of chips that do not fail in the first 1000 hours of use
is the value that we want to test
represent the significance level
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 the true proportion is 0.51 or 51%:
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 required we can replace in formula (1) like this:
Statistical decision
It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.
The significance level assumed on this case is . The next step would be calculate the p value for this test.
Since is a bilateral test the p value would be:
So the p value obtained was a very high value and using the significance level assumed we see that
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 5% of significance the proportion of chips that do not fail in the first 1000 hours of use is not significatnly different from 0.51 .