Answer:
The margin of error for constructing a 95% confidence interval on the population mean income before taxes of all consumer units in the U.S is 1406.32.
Step-by-step explanation:
We are given that according to a survey of 500, the mean income before taxes of consumer units (i.e., households) in the U.S. was $60,533 with a standard error of 717.51.
Margin of error tells us that how much our sample mean value deviates from the true population value.
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<u>Margin of error is calculated using the following formula;</u>
Margin of error = 
where, 
= level of significance = 1 - confidence level
= 1 - 0.95 = 0.05 or 5%
Standard of Error = 
= 717.51
Now, the value of z at 2.5% level of significance (
) is given in the z table as 1.96, that means;
Margin of error =
= 
= 1406.32
Hence, the margin of error for constructing a 95% confidence interval on the population mean income before taxes of all consumer units in the U.S is 1406.32.
Answer:
2 2/3 Square Units
Step-by-step explanation:
To Find The Area Of Shaded Rectangle, We Must Multiply The Rectangle's Length And Width. The Length Of The Shaded Rectangle is 4 Units The Width Of The Rectangle Is 2/3 Of Unit. Area Of Rectangle = Length x Width So, 4/1 x 2/3 = 2 2/3 That's Your FINAL Answer!
Mark As Brainliest Pls! =)
Answer:
lim [(x(2) + 3) × 1/ x(4) ] = 3 × ( + ○○) = + ○○
50.00 - 13.95 = 36.05 (subtract the registration fee)
36.05/0.49 = 73.57 so she can buy 73 songs
Answer:
The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:
Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:
The two parameters are:
who represent the mean and is on the center of the distribution
who represent the standard deviation
One particular case is the normal standard distribution denoted by:
Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated
Step-by-step explanation:
The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:
Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:
The two parameters are:
who represent the mean and is on the center of the distribution
who represent the standard deviation
One particular case is the normal standard distribution denoted by:
Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated
