Let, \(u = -7x\)
By taking derivative of u with respect to x we get,
\(du = -7dx\)
or, \(dx = -du/7\)
Now, we have, \(\int\) \(4e^{-7x}\) \(dx\)
Putting \(-7x = u\) and \(dx = -du/7\) we get,
\(-4/7\) \(\int\) \(e^{u}\) \(du\)
\(=\) \(-4/7\) \(e^{u}\) + \(C\)
\(=\) \(-4/7\) \(e^{-7x}\) + \(C\)
Given rate, dN/dt = -3150/ \(t^{4}\) - 220, integrating the rate equation with respect to time we get,
\(\int\) \(dN\) = -3150 \(\int\) \(t^{-4}\) dt - 220 \(\int\) dt
\(N(t)\) = \(3150/3\) \(t^{-3}\) - \(220t + C\)
\(N(1)\)=\(6530\)= \(3150/3\) \(1^{-3}\) - \(220 + C\) [for, t=1, N(1)=6530]
\(C= 6530-1050+220\)
\(C = 5700\)
Therefore, the function will be: \(N(t)\) = \(1050\) \(t^{-3}\) - \(220t + 5700\)
From the given plot we get the lower limit= 4.5 and the upper limit =8.5. Now, by integrating the given line function within this limits we get the area. Below is the R code for finding the area by doing integration.
# define the area function
area_func<-function(x){
return(2*x-9)
}
# Integrate the function over the limits
result<-integrate(area_func, lower = 4.5, upper = 8.5)
result$value
## [1] 16
Solve the given two equations for x values:
\(x+2 = x^{2}−2x−2\)
or, \(x^{2}-3x-4 = 0\)
or, \(x^{2}-4x+x-4 = 0\)
or, \(x(x-4)+1(x-4)=0\)
or, \((x+1) (x-4)=o\)
Therefore, \(x= -1\) and \(x=4\)
func_1 <- function(x){
return(x^2 - 2*x -2)
}
func_2 <- function(x){
return(x +2)
}
plot(func_1, -10, 10,ylab = "y")
plot(func_2, -10, 10, add = TRUE)
From the plot above, it is seen that the x values at the two curves intersection points are -1 and 4 respectively. We are going to use this values as the lower and upper limits of the integration to find the desired area.
# define function for intersection
inter_func <- function(x){
return((x+2) - (x^2 - 2*x -2))
}
# find area for bounded by found x limits
area <- integrate(inter_func, lower = -1, upper = 4)
area$value
## [1] 20.83333
Let’s consider, order quantity, q annual demand, d = 110 (targeted sale number) holding cost per unit per year, h= 3.75 and fixed cost per order, k =8.25
# Define the function for calculating total annual inventory cost
total_inventory_cost <- function(q, d= 110, h = 3.75, k= 8.25) {
# Calculate annual ordering cost
annual_order_cost<- k* d / q
# Calculate annual holding cost
annual_holding_cost<-h * q / 2 # assuming that the average inventory level is half of the order quantity.
# Calculate total annual inventory cost
total_annual_inventory_cost= annual_order_cost + annual_holding_cost
# Return total cost
return(total_annual_inventory_cost)
}
# Optimization techniques to find the optimal order quantity
optimized_result <- optimize(total_inventory_cost, interval = c(1, 110), d = 110, h = 3.75, k = 8.25)
# Minimum lot size
optimized_result$minimum
## [1] 22.00002
# Minimum total inventory cost
optimized_result$objective
## [1] 82.5
Therefore, the lot size that will minimize inventory costs is 22 flat irons, and the number of orders per year will be 110 /22 = 5 orders per year.
Let, \(u = ln(9x)\) and, \(v = x^{6}\)
So, \(du = 1/x dx\)
and \(dv = 1/7x^{7}dx\)
Now, \(\int\) \((uv)dx\) = \(ln(9x)∗1/7x^{7}\)− \(\int\)\(1/x* 1/7* x^{7}dx\)
\(\int\) \((uv)dx\) = \(ln(9x)∗1/7x^{7}\)−1/7\(\int\)\(x^{6}dx\)
\(\int\) \((uv)dx\) = \(ln(9x)∗1/7x^{7}\)−\(1/49x^{7}+C\)
\(f(x)= 1/6x\)
\(\int_{1}^{e^{6}}\) \(1/6xdx\)
\(=\) \(1/6\)∗\(lnx\)\(|_{1}^{e^{6}}\)
\(=1/6ln(e^{6})−1/6ln(1)\)
\(=1/6[6−0]=1\)
The function, \(f(x) = 1/(6x)\) is non-negative for all \(x\) in \([1, e^{6}]\) and the integral of the function over the given limit is equal to 1. Therefore, the function, f(x) is a probability density function on the given interval.