Answer: a) $2155, b) $1763.03.
Step-by-step explanation:
Since we have given that
Number of cable knit sweaters = 15
Cost per sweaters = $65.00
Number of pairs of khaki twill pants = 22
Cost of each pair = $50.00
Number of casual jackets = 18
Cost of each jackets = $62.00
total sale volume would be

Employee discount = 35%
Additional discount = 15%
So, Total markdown dollars taken is given by

Hence, a) $2155, b) $1763.03.
Answer: Yer dad a lesbian
Step-by-step explanation:
Gay mom ^2 *granny tranny= lesbian dad
Rational,whole,integer,real
Step-by-step explanation:
Regression analysis is used to infer about the relationship between two or more variables.
The line of best fit is a straight line representing the regression equation on a scatter plot. The may pass through either some point or all points or none of the points.
<u>Method 1:</u>
Using regression analysis the line of best fit is: 
Here <em>α </em>= intercept, <em>β</em> = slope and <em>e</em> = error.
The formula to compute the intercept is:

Here<em> </em>
and
are mean of the <em>y</em> and <em>x</em> values respectively.

The formula to compute the slope is:

And the formula to compute the error is:

<u>Method 2:</u>
The regression line can be determined using the descriptive statistics mean, standard deviation and correlation.
The equation of the line of best fit is:

Here <em>r</em> = correlation coefficient = 
and
are standard deviation of <em>x</em> and <em>y</em> respectively.
