Reject the null hypothesis. There is sufficient evidence to prove that the mean life is different from 7463 hours.
95% confidence interval also supports this result.
Step-by-step explanation:
Let mu be the population mean life of a large shipment of CFLs.
The hypotheses are:
: mu=7463 hours
: mu≠7463 hours
Test statistic can be calculated using the equation:
z= where
X is the sample mean life of CFLs (7163 hours) M is the mean life assumed under null hypothesis. (7463 hours) s is the population standard deviation (1080 hours)N is the sample size (81)Then z= = -2.5
p-value is 0.0124, critical values at 0.05 significance are ±1.96
At the 0.05 level of significance, the the result is significant because 0.0124<0.05. There is significant evidence that mean life of light bulbs is different than 7463 hours.
95% Confidence Interval can be calculated using M±ME where
M is the sample mean life of a large shipment of CFLs (7163 hours)ME is the margin of error from the meanmargin of error (ME) from the mean can be calculated using the formula
ME= where
z is the corresponding statistic in the 95% confidence level (1.96)s is the standard deviation of the sample (1080 hours)N is the sample size (81)Then ME= =235.2
Thus 95% confidence interval estimate of the population mean life of the light bulbs is 7163±235.2 hours. That is between 6927.8 and 7398.2 hours.
Reject the null hypothesis. There is sufficient evidence to prove that the mean life is different from 7463 hours.
95% confidence interval also supports this result.
Step-by-step explanation:
Let mu be the population mean life of a large shipment of CFLs.
The hypotheses are:
: mu=7463 hours
: mu≠7463 hours
Test statistic can be calculated using the equation:
z= where
X is the sample mean life of CFLs (7163 hours) M is the mean life assumed under null hypothesis. (7463 hours) s is the population standard deviation (1080 hours)N is the sample size (81)Then z= = -2.5
p-value is 0.0124, critical values at 0.05 significance are ±1.96
At the 0.05 level of significance, the the result is significant because 0.0124<0.05. There is significant evidence that mean life of light bulbs is different than 7463 hours.
95% Confidence Interval can be calculated using M±ME where
M is the sample mean life of a large shipment of CFLs (7163 hours)ME is the margin of error from the meanmargin of error (ME) from the mean can be calculated using the formula
ME= where
z is the corresponding statistic in the 95% confidence level (1.96)s is the standard deviation of the sample (1080 hours)N is the sample size (81)Then ME= =235.2
Thus 95% confidence interval estimate of the population mean life of the light bulbs is 7163±235.2 hours. That is between 6927.8 and 7398.2 hours.
Let the length of the side of the square being cut out equal x
The length of the box would be 4.5 - 2x
The width of the box would be 3 -2x
Volume = (4.5 -2x) * (3-2x) * x
Simplify to:
V = 4x^3 - 15x^2 + 13.5x
B See picture: The greatest volume would be the point of the highest curve.
x = 0.589 y = 3.565, Rounded to the nearest tenth x = 0.6
Process: entered the equation from A into Desmos. The Y value would be the volume, so found where the volume was the highest and then found the related x value.
C) X is the side length of the corner squares being cut out, which would also be the height of the box. The Y value is the volume of the box.
Let the length of the side of the square being cut out equal x
The length of the box would be 4.5 - 2x
The width of the box would be 3 -2x
Volume = (4.5 -2x) * (3-2x) * x
Simplify to:
V = 4x^3 - 15x^2 + 13.5x
B See picture: The greatest volume would be the point of the highest curve.
x = 0.589 y = 3.565, Rounded to the nearest tenth x = 0.6
Process: entered the equation from A into Desmos. The Y value would be the volume, so found where the volume was the highest and then found the related x value.
C) X is the side length of the corner squares being cut out, which would also be the height of the box. The Y value is the volume of the box.
Step-by-step explanation:
Give that hypothesis testing is done using independent samples.
A) The confidence interval estimate of µ 1-µ 2 is ( x 1 - x 2 ) - E < ( µ 1 - µ 2 ) < ( x 1 - x 2 ) + E.
This is true. because margin of error is subtracted and added to get lower/upper bounds.
B) When making an inference about the two means, the P-value and traditional methods of hypothesis testing result in the same conclusion as the confidence interval method. True
C) False because Var(X-Y) = Var(x)+Var(Y) when independent
D) True.
Step-by-step explanation:
Give that hypothesis testing is done using independent samples.
A) The confidence interval estimate of µ 1-µ 2 is ( x 1 - x 2 ) - E < ( µ 1 - µ 2 ) < ( x 1 - x 2 ) + E.
This is true. because margin of error is subtracted and added to get lower/upper bounds.
B) When making an inference about the two means, the P-value and traditional methods of hypothesis testing result in the same conclusion as the confidence interval method. True
C) False because Var(X-Y) = Var(x)+Var(Y) when independent
D) True.
Step-by-step explanation:
Hello!
The variable of interest is
X: Number of people that feel vulnerable to identity theft in a sample of 929.
This variable is discrete and has a binomial distribution. X~Bi(n;p)
The parameter of interest is the population proportion of people that feel vulnerable to identity theft.
To calculate the 99% CI for the population proportion you have to use the approximate distribution to normal for the sample proportion p'≈N(p; )
a. The best point estimate for p is the sample proportion p' you calculate it as:
p'= x/n= 523/929= 0.56
b. The formula for the confidence interval is
p' ±
Where is the margin of error
In this case
c. Then the interval is
0.56 ± 0.04
[0.52;0.6]
d.
With a 99% confidence level, you can expect that the interval [0.52;0.6] will include the true value of the proportion of people that feel vulnerable to identity theft.
The correct answer is
3. there is a 99% chance that the true value of the population proportion will fall between the lower bound and the upper bound.
I hope this helps!
Step-by-step explanation:
Hello!
The variable of interest is
X: Number of people that feel vulnerable to identity theft in a sample of 929.
This variable is discrete and has a binomial distribution. X~Bi(n;p)
The parameter of interest is the population proportion of people that feel vulnerable to identity theft.
To calculate the 99% CI for the population proportion you have to use the approximate distribution to normal for the sample proportion p'≈N(p; )
a. The best point estimate for p is the sample proportion p' you calculate it as:
p'= x/n= 523/929= 0.56
b. The formula for the confidence interval is
p' ±
Where is the margin of error
In this case
c. Then the interval is
0.56 ± 0.04
[0.52;0.6]
d.
With a 99% confidence level, you can expect that the interval [0.52;0.6] will include the true value of the proportion of people that feel vulnerable to identity theft.
The correct answer is
3. there is a 99% chance that the true value of the population proportion will fall between the lower bound and the upper bound.
I hope this helps!
Option 3 & 4
Explanation:
A firm's market value can be computed by multiplying it's earnings per share with P/E Ratio of a similar firm.
Earnings per share =
Price Earnings Ratio =
The product of the above two would be the market price per share of the firm.
Similarly, Market/Book ratio = Total Market Capitalization/Book Value
Also, known as price to book ratio, the product of Market/Book ratio of a similar company and Book value of a company yields Market value of the company.
I am slightly confused ,but -1/4 x^2 +3x+10 I think. Letter wise F. ?
Step-by-step explanation:
x = 0 ; y = 10 ; 10 = a(0) + b(0) + c
c = 10
x=2 ; y = 15 ; 15 = a(4) + b(2) + 10 ; 5 = 4a+2b
x=4 ; y=18 ; 18 = a(16) + b(4) + 10 ; 8 = 16a + 4b
2(5) = (4a+2b)2-
8 = 16a + 4b
2 = -8a
a = -0.25
b = 2
y = (1/4)x^2 + 2x + 10 ; 4y = x^2 + 8x + 40
Hope this helped!
It will provide an instant answer!