Answer:
The researcher will conclude that there is a correlation between the number of hours worked out and the amount of pounds lost by people on the exercise program
Step-by-step explanation:
Here, we want to state what the conclusion of the researcher should be:
From the last part of the question, we can see that the test statistic is greater than the critical value
So what do we do in a case like this?
We can see that the researcher is trying to see if there is a correlation between hours worked out and the number of pounds lost over a specific period of time.
Now, let us form the null hypothesis;
The null hypothesis here H0 is that we do not have a correlation between number of hours spent working out and the amount of pounds lost
The alternative hypothesis here H1 is that there is a correlation between the number of hours spent working out and the amount of pounds lost
Since we have the value of the test statistic greater than the critical value, we reject the null hypothesis and accept the alternative hypothesis
So therefore, the researcher will conclude that there is a correlation between the number of hours worked out and the amount of pounds lost by people on the exercise program
You have to use the equation (72+y)=5(4+y) which Y =+13
Answer:
The probability that 2 or more of the original 5,100 components may fail during the useful life of the product is:
= 0.001
Step-by-step explanation:
Probability of operating without failure = 0.999
The probability of failed component = 0.001 (1 - 0.999)
The number of components of the electronic office product = 5,100
The number of components that may fail, given the above successful operation = 5.1 (0.001 * 5,100).
Therefore, the probability that 2 or more of the original 5,100 components may fail during the useful life of the product = 0.001
Probability of component failure is the likelihood that a component fails during the useful life of the product. It is expressed as the number of likely failed components divided by the total number of components. This result can be left in decimal form or expressed as a percentage.
In anova, by dividing the mean square between groups by the mean square within groups, a(n) Analysis of variance statistic is computed.
What is Analysis of variance ?
- With the help of the statistical analysis approach known as ANOVA, apparent aggregate variability within a data set is explained by separating systematic components from random factors.
- Systematic influences, but not random ones, statistically affect the data set that is being presented.
What are some instances where ANOVA has been applied?
- An ANOVA demonstrates the link between the dependent variable and the level of the independent variable.
- For illustration: In order to determine whether there is a difference in the number of hours of sleep each night as your independent variable, you divide the groups into low, medium, and high social media use categories.
Learn more about Analysis of variance
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Answer:
No
Step-by-step explanation:
See attached
