1.Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. (5.6,8.8), (6.3,12.4), (7,14.8), (7.7,18.2), (8.4,20.8)
x <- data.frame(x= c(5.6, 6.3, 7, 7.7, 8.4), y= c(8.8, 12.4, 14.8, 18.2, 20.8))
lm(x$y~x$x)
##
## Call:
## lm(formula = x$y ~ x$x)
##
## Coefficients:
## (Intercept) x$x
## -14.800 4.257
plot(x)
abline(lm(x$y~x$x))
The linear regression line is y=4.26x-14.8
\(\frac{df}{dx}=24-6y^2\) => \(0=24-6y^2\) => y=-2, 2
\(\frac{df}{dy}=12xy-24y^2\) => \(0=12xy-24y^2=y(12x-24y)\) => \(12x=24y\) => \(x=2y\), so x=4, -4
The local maxima and minima are: (4, 2), (-4, -2)
\(R(x,y)=x(81-21x + 17y)+y(40 + 11x-23y)\)
\(R(2.30,4.10)=2.30*(81-21*2.30 + 17*(4.1))+4.1*(40 + 11*2.3-23*4.1)=117\)
4.A company has a plant in Los Angeles and a plant in Denver. The firm is committed to produce a total of 96 units of a product each week. The total weekly cost is given by \(C(x,y)= \frac{x^2}{6} + \frac{y^2}{6} +7x+25y+700\), where x is the number of units produced in Los Angeles and y is the number of units produced in Denver. How many units should be produced in each plant to minimize the total weekly cost? x+y=96, so x=96-y \(C(96-y, y)=\frac{(96-y)^2}{6} + \frac{y^2}{6} +7(96-y)+25y+700\)
\(\frac{df}{dy}=\frac{-96+y}{3}+\frac{y}{3}-7+25=-32+\frac{2y}{3}+18=\frac{2y}{3}-14\) => \(\frac{2}{3}y=14\) => y=21, so x=75.
21 units need to be prodiced in Denver adn 75 need to be produced in LA.
\(\int\int_Re^{8x+3y}dA; R:2\leq x \leq 4\) and \(2\leq y\leq4\)
Start with \(\int_2^4e^{8x+3y}dy\) and let \(u=8x+3y\) and \(\frac{1}{3}du=dy\), so we have \(\frac{1}{3}\int_2^4e^{u}du=\frac{1}{3}(e^{u})]_2^4=\frac{1}{3}(e^{8x+3y})]_2^4=\frac{1}{3}e^{8x+12}-\frac{1}{3}e^{8x+6}\) then we get \(\int_2^4\frac{1}{3}e^{8x+12}-\frac{1}{3}e^{8x+6}dx\)= \(\int_2^4\frac{1}{3}e^{8x+12}dx-\int_2^4\frac{1}{3}e^{8x+6}dx\)=\(\frac{1}{24}(e^{8x+12}-e^{8x+6})]_2^4=\frac{1}{24}(e^{44}-e^{38}-e^{28}+e^{22})\)