Answer:
We are 95% confident that the true proportion of U.S. adults who live with one or more chronic conditions is between 39.7% and 46.33%
Step-by-step explanation:
From the question we are told that
The sample proportion is 
The standard error is 
Given that the confidence level is 95% then the level of significance is mathematically represented as
=> 
Generally from the normal distribution table the critical value of 
is
Generally the margin of error is mathematically represented as
=> 
=> 
Generally 95% confidence interval is mathematically represented as
=> 
=> 
Converting to percentage
The statement fourth "No, the flowers were not put into groups first and then randomly assigned the two additives would be correct.
<h3>What is random experiment?</h3>
Any well-defined method that yields an observable outcome that cannot be precisely predicted in advance is referred to as a random experiment. To avoid any ambiguity or surprise, a random experiment must be properly defined.
We have:
Total number of flowers selected = 20
And puts in each vase with same amount of water.
10 flowers have been assigned to receive the new additive.
The remaining 10 flowers receive the original additive.
Based on the data, this is not a randomized block design for the experiment because the flowers were not put into groups first and then randomly assigned the two additives.
Thus, the statement fourth "No, the flowers were not put into groups first and then randomly assigned the two additives would be correct.
Learn more about the random experiment here:
brainly.com/question/14298568
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Okkk .. this is what this app is for anyways to help.
It should be a whole number.
Answer:
Explained
Step-by-step explanation:
Given that:
- A researcher is interested in determining whether a large aerospace firm is guilty of gender bias in setting wages.
According to the given info the difference in means test is too limited because it does not include the type of engineer, education level or experience. The gender with lower wages of might be reflected in the type of engineer or education level.
The research could be improved using additional data on the factors namely gender, education, education and the type of engineer.
Then, further it is recommended to construct a multiple regression where the dependent variable is a wage and the four factors are independent variables. The importance of the omited variable by the means of that the "difference in means" test in unsuitable for determining the gender bias in setting wages.
