A robot can complete 4 tasks in 5/6 hour. Each task takes the same amount of time. a. How long does it take the robot to complet
2 answers:
<h2>Part A</h2><h3>What we know</h3>
We know that a robot can complete 4 tasks in hour. Each task takes the same amount of time
We need to know how long it takes the robot to complete one task
<h3>Show your work</h3> of an hour is also equal to 50 minutes.
So now, we divide:
50 ÷ 4 = 12.5
<h3>Check your work</h3>One thing we can do to check our work is to multiply:
12.5 x 4 = 50
<h3>Answer</h3>it takes the robot 12.5 minutes to complete one task
<h2>Part B</h2><h2 /><h3>What we know (same as Part A)</h3>We know that a robot can complete 4 tasks in hour. Each task takes the same amount of time
We need to know how many tasks the robot complete in one hour.
<h3>Show your work</h3>Using the information from Part A, we know that is 50 minutes. We also know it take the robot 12.5 minutes to complete one task. 60 minutes is equal to one hour. So what we would do is divide:
60 ÷ 12.5 = 4.80
The robot cannot complete 5 tasks in one hour.
<h3>Check your work</h3>To check our work, we can multiply:
4.80 x 12.50 = 60 <em>or</em> one hour
<h3>Answer</h3>The robot can complete 4.8 tasks in one hour.
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Answer:
So the p value obtained was a very low value and using the significance level assumed for example we have
so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance the proportion of circuits that show evidence of undercutting is significantly less than 0.05.
Step-by-step explanation:
1) Data given and notation
n=1000 represent the random sample taken
X=33 represent the number of circuits that show evidence of undercutting
estimated proportion of circuits that show evidence of undercutting
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)
2) Concepts and formulas to use
We need to conduct a hypothesis in order to test the claim that they reduce the rate of undercutting to less than 5%.:
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
.
3) Calculate the statistic
Since we have all the info requires we can replace in formula (1) like this:
4) 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 next step would be calculate the p value for this test.
Since is a left tailed test the p value would be:
So the p value obtained was a very low value and using the significance level assumed for example we have
so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance the proportion of circuits that show evidence of undercutting is significantly less than 0.05.