Jerrilyn wrote the following argument to prove that
1 answer:
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is the linear equation to find the temperature T at an elevation x on the mountain, where x is in thousands of feet.
<em><u>Solution:</u></em>
The linear equation in slope intercept form is given as:
T = cx + k ------ (i)
Where "t" is the temperature at an elevation x
And x is in thousands of feet
<em><u>Given that its 76 degrees fahrenheit at the 6000-foot level of a mountain</u></em>
Given, when c = 6 thousand ft and fahrenheit
This implies,
From (i)
76 = c(6) + k
76 = 6c + k
⇒ k = 76 - 6c ----- (ii)
<em><u>Given that 49 degrees Fahrenheit at the 12000-foot level of the mountain</u></em>
Given, when c = 12 thousand ft and fahrenheit
This implies,
From (i)
49 = c(12) + k
49 = 12c + k
Substitute (ii) in above equation
49 = 12c + (76 - 6c)
49 = 12c + 76 - 6c
49 - 76 = 6c
6c = -27
Substituting the value of c in (ii) we get
Substituting the value of c and k in (i)
Where "x" is in thousands of feet
Thus the required linear equation is found
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%