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garri49 [273]
3 years ago
13

For february, sales revenue is $700,000; sales commissions are 5% of sales; the sales manager's salary is $96,000; advertising e

xpenses are $90,000; shipping expenses total 2% of sales; and miscellaneous selling expenses are $2,500 plus 1/2 of 1% of sales. total selling expenses for the month of february are select one:
a. $161,000

b. $237,500

c. $235,000

d. $241,000
Business
1 answer:
lianna [129]3 years ago
5 0

Answer:

The correct option is <u>"d. $241,000".</u>

Explanation:

let's sum each selling expense:

Sales Commissions ($700,000 sales x 5%)  = $35,000

Sales Manager Salary = $96,000

Advertising expenses = $90,000

Shipping expenses = ($700,000 sales x 2%) = 14,000

Miscellaneous selling expenses = ($2,500 + $700,000 x 1% x 0.5) = $6,000

Total Selling Expenses = $241,000

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3 years ago
Let x be per capita income in thousands of dollars. Let y be the number of medical doctors per 10,000 residents. Six small citie
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Answer:

The percentage of variation esplained by the model is given by the determination coefficient, on this case:

R^2 = 0.934^2 =0.872

And we have 87.2% of the variation explained by the linear model given.

\hat y = 5.756(8.5) -36.895=12.031

And we have 12.031 doctors per 10000 residents.

Explanation:

Assuming the following dataset:

x                 y

8.6           9.6

9.3           18.5

10.1          20.9

8.0           10.2

8.3           11.4

8.7            13.1

Assuming this question: "The data has a correlation coefficient of r = 0.934. Calculate the regression line for this  data. What percentage ofvariation is explained by the regression line? Predict the number of doctors per 10,000 residents in a town with a per capita income of $8500."

We want a linear model like this:

y = mx +b

Where m represent the slope and b the intercept for the linear model. And we cna find the slope and b with the following formulas:

m = \frac{n \sum xy - \sum x \sum y}{n \sum x^2 -(\sum x)^2}

b = \frac{\sum y}{n} -m \frac{\sum x}{n}

And from the dataset we have the following values:

n= 6, \sum x =53, \sum y = 83.7 , \sum xy = 755.89, \sum x^2 = 471.04

And replacing into the equation for m we got:

m =\frac{6(755.89) - (53)(83.7)}{6(471.04) -(53)^2}=5.756

And the intercept:

b = \frac{83.7}{6}-36.895 5.756 \frac{53}{6}=-36.895

And then the linear model is given by:

\hat y = 5.756 x -36.895

We can find the estimation replacing x = 8.5 into the linear model and we got:

\hat y = 5.756(8.5) -36.895=12.031

And we have 12.031 doctors per 10000 residents.

The percentage of variation esplained by the model is given by the determination coefficient, on this case:

R^2 = 0.934^2 =0.872

And we have 87.2% of the variation explained by the linear model given.

4 0
3 years ago
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