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By identifying the radius and the center, we concluded that the equation is something like:
For a circle centered on the point (a, b) with a radius R, the equation is:
So first we need to identify the center, we can see that it is: (2, 1.5)
And the radius is the distance between the center and the edge.
R = 1.8 (measured with a ruler, respecting the notation of the graph).
Then the equation of the circle is something like:
If you want to learn more about circles:
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Answer:
a)
b) The accountant should not question the voucher as the request is much higher than expected.
c) We can add the following variables
d) The percentage of variation in the trip cost that can be explained by the model = (0.64)^2 =0.4096 or 40.96%
A. Type of travel (air/car)
B. Gender of the executive (male/female)
The last option is not too important since we are analyzing the traveling executives not the type of business
Step-by-step explanation:
Part a
For this case we have the multiple regression model given by:
Where represent the number of days on the road and
the distance traveled in miles.
And we want to find the expected amount from a 340-mile trip that took her out of town for 5 days, so we just need to replace x1= 5 and x2=340 and we got:
Part b
The expected value taking in count that x1 = 5 and y = 340 is:
And the difference from the obtained value is : 685-441.50=243.5
So then the correct answer would be:
The accountant should not question the voucher as the request is much higher than expected.
Part c
We can add the following variables
A. Type of travel (air/car)
B. Gender of the executive (male/female)
The last option is not too important since we are analyzing the traveling executives not the type of business
Part d
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.
The coefficient of determination "is a measure used in statistical analysis that assesses how well a model explains and predicts future outcomes. It is indicative of the level of explained variability in the data set".
The percentage of variation in the trip cost that can be explained by the model = (0.64)^2 =0.4096 or 40.96%