pt1 <- c(5.6, 6.3, 7, 7.7, 8.4)
pt2 <- c(8.8, 12.4, 14.8, 18.2, 20.8)
reg <- lm(pt2 ~ pt1)
summary(reg)
##
## Call:
## lm(formula = pt2 ~ pt1)
##
## Residuals:
## 1 2 3 4 5
## -0.24 0.38 -0.20 0.22 -0.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -14.8000 1.0365 -14.28 0.000744 ***
## pt1 4.2571 0.1466 29.04 8.97e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3246 on 3 degrees of freedom
## Multiple R-squared: 0.9965, Adjusted R-squared: 0.9953
## F-statistic: 843.1 on 1 and 3 DF, p-value: 8.971e-05
y = -14.8 + 4.2571x
knitr::include_graphics('gif.gif')
Step 1. Find the revenue function R ( x, y ).
revenue_func <- function(x, y){
x*(81 - (21*x) + (17*y)) + y * (40 + (11 * x) - 23*y)
}
Step 2. What is the revenue if she sells the “house” brand for $2.30 and the “name” brand for $4.10?
revenue_func(2.3, 4.1)
## [1] 116.62
knitr::include_graphics('Q5.gif')
knitr::include_graphics('P5.gif')
double_integral <- function(x,y) exp(8*x + 3 * y)
format(round(quad2d(double_integral, 2, 4, 2, 4), 17), scientific = FALSE)
## [1] "534155947497083904"