The answer would be 543.1187 X 100= {54311.87}
Answer:
a) 61.2, b) 38.4 and c) 4.98
Step-by-step explanation:
Given:
The mean width of 12 I-Pads is 5.1 inches.
The mean width of 8 Kindles is 4.8 inches.
Question asked:
a. What is the total width of the I-Pads?
b. What is the total width of the Kindles?
c. What is the mean width of the 12 I-Pads and 8 Kindles?
Solution:
As we know:
a) Thus, the total width of the I-Pads are 61.2 inches.
b) Thus, total width of the Kindles are 38.4 inches.
Combined width of both I-pad and Kindles = 61.2 + 38.4 = 99.6 inches
Combined number of observations = 12 + 8 =20
Combined mean of width of the 12 I-Pads and 8 Kindles = Combined width of both I-pad and Kindles Combined number of observations
Combined mean of width of the 12 I-Pads and 8 Kindles =
c) Thus, the mean width of the 12 I-Pads and 8 Kindles is 4.98 inches.
The total width of the iPads is 61.2 inches and for the Kindles, 38.4 inches. When calculated together, the mean width of the iPads and Kindles is 4.98 inches per device.
This question deals with the calculation of means (averages) and total values. To find the total width of the iPads, we multiply the mean width by the number of iPads:
5.1 inches * 12 iPads = 61.2 inches.
For the Kindles, we do the same:
4.8 inches * 8 Kindles = 38.4 inches.
To find the mean width of all the devices together, we add up the total widths and divide by the total number of devices:
(61.2 inches + 38.4 inches) / (12 iPads + 8 Kindles) = 99.6 inches / 20 devices = 4.98 inches/device.
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2(x + 4) = x + 13
A. −3
B. −1
C. 5
D. 7
Heteroscedasticity does mean that the variability of y-values is larger for some x values than for others, which is a condition that can impact the efficiency of your estimator and lead to incorrect conclusions in regression analysis.
The statement 'Heteroscedasticity means that the variability of y values is larger for some x values than for others' is True. In the context of regression analysis, heteroscedasticity refers to the variability of the random disturbance (the y-values) being different across elements of an independent variable (the x-values). For instance, the variance of errors might increase or decrease with the level of the dependent variable. This violates the assumption of homoscedasticity in ordinary least squares (OLS) regression, which presumes that the variation around the regression line is the same for all values of the independent variable. The presence of heteroscedasticity could impact the efficiency of your estimator and could lead to incorrect conclusions about the relationship between the dependent and independent variables.
Learn more about Heteroscedasticity here:
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Answer:
A
Step-by-step explanation:
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