DATA 604 HW 8
Ahsanul Choudhury
October 22, 2017
Kelton Chapter 6 Problems
For Problem 1,2 and 3 I ahve used the Stst::Fit software.
6.5.1
Problem 1 in Stat::Fit
For this problem Expotential distribution is recommended.
P-P Plot
Correct Simio expression:
Exponential(2.06, 9.86)
6.5.2
Problem 2 in Stat::Fit
For this problem Lognormal distribution is recommended.
P-P Plot
Correct Simio expression:
Lognormal(2.76, 1.84, 0.717)
6.5.3
Problem 3 in Stat::Fit
For this problem Lognormal distribution is recommended.
P-P Plot
Correct Simio expression:
Lognormal(-0.497, 1.14, 0.439)
6.5.4
CDF for Continuous Uniform Distribution (DES Banks, Chapter 8):
\[ F(x) = \begin{cases} 0, & x < a\\ \ \frac{x - a}{b - a}, & a \leq x \leq b \\ 1, & x > b \end{cases} \]
Inverse Transform, Soving for X in terms of R :
\[X= a + (b-a)R\]
6.5.5
CDF for Continuous Weibull Distribution (DES Banks, Chapter 8):
\[F(x)=1 - e^{-(x/\alpha)^\beta}, x \geq0\]
Inverse Transform, Soving for X in terms of R :
\[X= \alpha[-ln(1-R)]^{1/\beta}\]
6.5.8
# Purchase price
oats.p <- 1.05
peas.p <- 3.17
beans.p <- 1.99
barley.p <- 0.95
# Sale price
oats.s <- 1.29
peas.s <- 3.76
beans.s <- 2.23
barley.s <- 1.65
# Pre-packaged size and their demand probabilities.
lb.demand.oats = data.frame(lb = c(0, 0.5, 1, 1.5, 2.0, 3.0, 4.0, 5.0, 7.5, 10.0),
prob = c(0.05, 0.07, 0.09, 0.11, 0.15, 0.25, 0.10, 0.09, 0.06, 0.03))
lb.demand.peas = data.frame(lb = c(0, 0.5, 1.0, 1.5, 2.0, 3.0),
prob = c(0.1, 0.2, 0.2, 0.3, 0.1, 0.1))
lb.demand.beans = data.frame(lb = c(0, 1.0, 3.0, 4.5),
prob = c(0.2, 0.4, 0.3, 0.1))
lb.demand.barley = data.frame(lb = c(0, 0.5, 1.0, 3.5),
prob = c(0.2, 0.4, 0.3, 0.1))
# Genetate demand for the season(90 days)
oats.sale <- c(sample(lb.demand.oats$lb, prob=lb.demand.oats$prob, 90,replace=TRUE))
peas.sale <- c(sample(lb.demand.peas$lb, prob=lb.demand.peas$prob, 90, replace = TRUE))
beans.sale <- c(sample(lb.demand.beans$lb, prob=lb.demand.beans$prob, 90, replace=TRUE))
barley.sale <- c(sample(lb.demand.barley$lb, prob=lb.demand.barley$prob,90,replace = TRUE))
par(mfrow=c(2,2))
hist(oats.sale, xlab = "pounds", main = "Histogram of Oats Demand(lbs)")
hist(peas.sale, xlab = "pounds", main = "Histogram of peas Demand(lbs)")
hist(beans.sale, xlab = "pounds", main = "Histogram of Beans Demand(lbs)")
hist(barley.sale, xlab = "pounds", main = "Histogram of Barley Demand(lbs)")
oats.cost <- oats.p * oats.sale
peas.cost <- peas.p * peas.sale
beans.cost <-beans.p * beans.sale
barley.cost <-barley.p * barley.sale
oats.revenue <- oats.s * oats.sale
peas.revenue <- peas.s * peas.sale
beans.revenue <- beans.s * beans.sale
barley.revenue <-barley.s * barley.sale
oats.profit <- oats.revenue - (oats.p * oats.sale)
peas.profit <- peas.revenue - (peas.p * peas.sale)
beans.profit <- beans.revenue -(beans.p * beans.sale)
barley.profit <- barley.revenue - (barley.p * barley.sale)
day <- c(1:90)
df <- data.frame(day, oats.sale, peas.sale, beans.sale, barley.sale, oats.cost, peas.cost, beans.cost, barley.cost, oats.revenue, peas.revenue, beans.revenue, barley.revenue, oats.profit, peas.profit, beans.profit, barley.profit )
colnames(df) <- c("Day", "Oats Demand(lbs)", "Peas Demand(lbs)", "Beans Demand(lbs)", "Barley Demand(lbs)", "Oats Cost($)", "Peas Cost($)", "Beans Cost($)", "Barley Cost($)","Oats Revenue($)", "Peas Revenue($)", "Beans Revenue($)","Barley Revenue($)","Oats Profit($)", "Peas Profit($)", "Beans Profit($)", "Barley Profit($)")
knitr::kable(df)| Day | Oats Demand(lbs) | Peas Demand(lbs) | Beans Demand(lbs) | Barley Demand(lbs) | Oats Cost($) | Peas Cost($) | Beans Cost($) | Barley Cost($) | Oats Revenue($) | Peas Revenue($) | Beans Revenue($) | Barley Revenue($) | Oats Profit($) | Peas Profit($) | Beans Profit($) | Barley Profit($) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2.0 | 1.5 | 3.0 | 1.0 | 2.100 | 4.755 | 5.970 | 0.950 | 2.580 | 5.64 | 6.690 | 1.650 | 0.48 | 0.885 | 0.72 | 0.70 |
| 2 | 2.0 | 0.5 | 3.0 | 3.5 | 2.100 | 1.585 | 5.970 | 3.325 | 2.580 | 1.88 | 6.690 | 5.775 | 0.48 | 0.295 | 0.72 | 2.45 |
| 3 | 1.5 | 0.5 | 1.0 | 0.0 | 1.575 | 1.585 | 1.990 | 0.000 | 1.935 | 1.88 | 2.230 | 0.000 | 0.36 | 0.295 | 0.24 | 0.00 |
| 4 | 7.5 | 0.5 | 1.0 | 0.5 | 7.875 | 1.585 | 1.990 | 0.475 | 9.675 | 1.88 | 2.230 | 0.825 | 1.80 | 0.295 | 0.24 | 0.35 |
| 5 | 0.5 | 0.5 | 4.5 | 1.0 | 0.525 | 1.585 | 8.955 | 0.950 | 0.645 | 1.88 | 10.035 | 1.650 | 0.12 | 0.295 | 1.08 | 0.70 |
| 6 | 1.0 | 2.0 | 3.0 | 3.5 | 1.050 | 6.340 | 5.970 | 3.325 | 1.290 | 7.52 | 6.690 | 5.775 | 0.24 | 1.180 | 0.72 | 2.45 |
| 7 | 0.0 | 1.5 | 1.0 | 0.5 | 0.000 | 4.755 | 1.990 | 0.475 | 0.000 | 5.64 | 2.230 | 0.825 | 0.00 | 0.885 | 0.24 | 0.35 |
| 8 | 5.0 | 0.5 | 3.0 | 1.0 | 5.250 | 1.585 | 5.970 | 0.950 | 6.450 | 1.88 | 6.690 | 1.650 | 1.20 | 0.295 | 0.72 | 0.70 |
| 9 | 5.0 | 1.0 | 0.0 | 1.0 | 5.250 | 3.170 | 0.000 | 0.950 | 6.450 | 3.76 | 0.000 | 1.650 | 1.20 | 0.590 | 0.00 | 0.70 |
| 10 | 1.0 | 2.0 | 1.0 | 0.5 | 1.050 | 6.340 | 1.990 | 0.475 | 1.290 | 7.52 | 2.230 | 0.825 | 0.24 | 1.180 | 0.24 | 0.35 |
| 11 | 3.0 | 1.0 | 4.5 | 1.0 | 3.150 | 3.170 | 8.955 | 0.950 | 3.870 | 3.76 | 10.035 | 1.650 | 0.72 | 0.590 | 1.08 | 0.70 |
| 12 | 1.5 | 2.0 | 3.0 | 0.5 | 1.575 | 6.340 | 5.970 | 0.475 | 1.935 | 7.52 | 6.690 | 0.825 | 0.36 | 1.180 | 0.72 | 0.35 |
| 13 | 1.5 | 2.0 | 0.0 | 0.5 | 1.575 | 6.340 | 0.000 | 0.475 | 1.935 | 7.52 | 0.000 | 0.825 | 0.36 | 1.180 | 0.00 | 0.35 |
| 14 | 4.0 | 1.5 | 1.0 | 1.0 | 4.200 | 4.755 | 1.990 | 0.950 | 5.160 | 5.64 | 2.230 | 1.650 | 0.96 | 0.885 | 0.24 | 0.70 |
| 15 | 3.0 | 1.0 | 4.5 | 1.0 | 3.150 | 3.170 | 8.955 | 0.950 | 3.870 | 3.76 | 10.035 | 1.650 | 0.72 | 0.590 | 1.08 | 0.70 |
| 16 | 1.0 | 1.5 | 1.0 | 0.0 | 1.050 | 4.755 | 1.990 | 0.000 | 1.290 | 5.64 | 2.230 | 0.000 | 0.24 | 0.885 | 0.24 | 0.00 |
| 17 | 2.0 | 1.0 | 4.5 | 0.5 | 2.100 | 3.170 | 8.955 | 0.475 | 2.580 | 3.76 | 10.035 | 0.825 | 0.48 | 0.590 | 1.08 | 0.35 |
| 18 | 3.0 | 1.5 | 3.0 | 3.5 | 3.150 | 4.755 | 5.970 | 3.325 | 3.870 | 5.64 | 6.690 | 5.775 | 0.72 | 0.885 | 0.72 | 2.45 |
| 19 | 3.0 | 0.5 | 1.0 | 1.0 | 3.150 | 1.585 | 1.990 | 0.950 | 3.870 | 1.88 | 2.230 | 1.650 | 0.72 | 0.295 | 0.24 | 0.70 |
| 20 | 4.0 | 1.5 | 3.0 | 0.0 | 4.200 | 4.755 | 5.970 | 0.000 | 5.160 | 5.64 | 6.690 | 0.000 | 0.96 | 0.885 | 0.72 | 0.00 |
| 21 | 4.0 | 1.5 | 1.0 | 1.0 | 4.200 | 4.755 | 1.990 | 0.950 | 5.160 | 5.64 | 2.230 | 1.650 | 0.96 | 0.885 | 0.24 | 0.70 |
| 22 | 10.0 | 0.0 | 0.0 | 0.5 | 10.500 | 0.000 | 0.000 | 0.475 | 12.900 | 0.00 | 0.000 | 0.825 | 2.40 | 0.000 | 0.00 | 0.35 |
| 23 | 0.0 | 1.5 | 3.0 | 1.0 | 0.000 | 4.755 | 5.970 | 0.950 | 0.000 | 5.64 | 6.690 | 1.650 | 0.00 | 0.885 | 0.72 | 0.70 |
| 24 | 5.0 | 2.0 | 3.0 | 0.5 | 5.250 | 6.340 | 5.970 | 0.475 | 6.450 | 7.52 | 6.690 | 0.825 | 1.20 | 1.180 | 0.72 | 0.35 |
| 25 | 3.0 | 0.5 | 3.0 | 0.5 | 3.150 | 1.585 | 5.970 | 0.475 | 3.870 | 1.88 | 6.690 | 0.825 | 0.72 | 0.295 | 0.72 | 0.35 |
| 26 | 5.0 | 1.5 | 1.0 | 0.0 | 5.250 | 4.755 | 1.990 | 0.000 | 6.450 | 5.64 | 2.230 | 0.000 | 1.20 | 0.885 | 0.24 | 0.00 |
| 27 | 2.0 | 1.5 | 1.0 | 0.5 | 2.100 | 4.755 | 1.990 | 0.475 | 2.580 | 5.64 | 2.230 | 0.825 | 0.48 | 0.885 | 0.24 | 0.35 |
| 28 | 0.0 | 1.0 | 1.0 | 3.5 | 0.000 | 3.170 | 1.990 | 3.325 | 0.000 | 3.76 | 2.230 | 5.775 | 0.00 | 0.590 | 0.24 | 2.45 |
| 29 | 5.0 | 1.0 | 1.0 | 0.5 | 5.250 | 3.170 | 1.990 | 0.475 | 6.450 | 3.76 | 2.230 | 0.825 | 1.20 | 0.590 | 0.24 | 0.35 |
| 30 | 3.0 | 0.5 | 3.0 | 3.5 | 3.150 | 1.585 | 5.970 | 3.325 | 3.870 | 1.88 | 6.690 | 5.775 | 0.72 | 0.295 | 0.72 | 2.45 |
| 31 | 2.0 | 1.5 | 3.0 | 0.5 | 2.100 | 4.755 | 5.970 | 0.475 | 2.580 | 5.64 | 6.690 | 0.825 | 0.48 | 0.885 | 0.72 | 0.35 |
| 32 | 4.0 | 1.0 | 4.5 | 0.5 | 4.200 | 3.170 | 8.955 | 0.475 | 5.160 | 3.76 | 10.035 | 0.825 | 0.96 | 0.590 | 1.08 | 0.35 |
| 33 | 3.0 | 3.0 | 4.5 | 0.0 | 3.150 | 9.510 | 8.955 | 0.000 | 3.870 | 11.28 | 10.035 | 0.000 | 0.72 | 1.770 | 1.08 | 0.00 |
| 34 | 3.0 | 1.5 | 3.0 | 1.0 | 3.150 | 4.755 | 5.970 | 0.950 | 3.870 | 5.64 | 6.690 | 1.650 | 0.72 | 0.885 | 0.72 | 0.70 |
| 35 | 1.5 | 0.5 | 1.0 | 1.0 | 1.575 | 1.585 | 1.990 | 0.950 | 1.935 | 1.88 | 2.230 | 1.650 | 0.36 | 0.295 | 0.24 | 0.70 |
| 36 | 3.0 | 0.5 | 0.0 | 0.0 | 3.150 | 1.585 | 0.000 | 0.000 | 3.870 | 1.88 | 0.000 | 0.000 | 0.72 | 0.295 | 0.00 | 0.00 |
| 37 | 1.5 | 1.5 | 4.5 | 0.5 | 1.575 | 4.755 | 8.955 | 0.475 | 1.935 | 5.64 | 10.035 | 0.825 | 0.36 | 0.885 | 1.08 | 0.35 |
| 38 | 7.5 | 1.0 | 0.0 | 3.5 | 7.875 | 3.170 | 0.000 | 3.325 | 9.675 | 3.76 | 0.000 | 5.775 | 1.80 | 0.590 | 0.00 | 2.45 |
| 39 | 5.0 | 1.5 | 0.0 | 0.5 | 5.250 | 4.755 | 0.000 | 0.475 | 6.450 | 5.64 | 0.000 | 0.825 | 1.20 | 0.885 | 0.00 | 0.35 |
| 40 | 2.0 | 1.5 | 3.0 | 1.0 | 2.100 | 4.755 | 5.970 | 0.950 | 2.580 | 5.64 | 6.690 | 1.650 | 0.48 | 0.885 | 0.72 | 0.70 |
| 41 | 2.0 | 1.5 | 3.0 | 0.5 | 2.100 | 4.755 | 5.970 | 0.475 | 2.580 | 5.64 | 6.690 | 0.825 | 0.48 | 0.885 | 0.72 | 0.35 |
| 42 | 5.0 | 0.5 | 1.0 | 1.0 | 5.250 | 1.585 | 1.990 | 0.950 | 6.450 | 1.88 | 2.230 | 1.650 | 1.20 | 0.295 | 0.24 | 0.70 |
| 43 | 1.5 | 2.0 | 1.0 | 0.0 | 1.575 | 6.340 | 1.990 | 0.000 | 1.935 | 7.52 | 2.230 | 0.000 | 0.36 | 1.180 | 0.24 | 0.00 |
| 44 | 3.0 | 1.5 | 1.0 | 1.0 | 3.150 | 4.755 | 1.990 | 0.950 | 3.870 | 5.64 | 2.230 | 1.650 | 0.72 | 0.885 | 0.24 | 0.70 |
| 45 | 3.0 | 1.5 | 4.5 | 1.0 | 3.150 | 4.755 | 8.955 | 0.950 | 3.870 | 5.64 | 10.035 | 1.650 | 0.72 | 0.885 | 1.08 | 0.70 |
| 46 | 3.0 | 0.0 | 0.0 | 1.0 | 3.150 | 0.000 | 0.000 | 0.950 | 3.870 | 0.00 | 0.000 | 1.650 | 0.72 | 0.000 | 0.00 | 0.70 |
| 47 | 3.0 | 1.5 | 0.0 | 0.5 | 3.150 | 4.755 | 0.000 | 0.475 | 3.870 | 5.64 | 0.000 | 0.825 | 0.72 | 0.885 | 0.00 | 0.35 |
| 48 | 4.0 | 3.0 | 1.0 | 1.0 | 4.200 | 9.510 | 1.990 | 0.950 | 5.160 | 11.28 | 2.230 | 1.650 | 0.96 | 1.770 | 0.24 | 0.70 |
| 49 | 1.0 | 1.0 | 0.0 | 1.0 | 1.050 | 3.170 | 0.000 | 0.950 | 1.290 | 3.76 | 0.000 | 1.650 | 0.24 | 0.590 | 0.00 | 0.70 |
| 50 | 1.0 | 1.5 | 3.0 | 0.5 | 1.050 | 4.755 | 5.970 | 0.475 | 1.290 | 5.64 | 6.690 | 0.825 | 0.24 | 0.885 | 0.72 | 0.35 |
| 51 | 0.5 | 0.0 | 4.5 | 0.0 | 0.525 | 0.000 | 8.955 | 0.000 | 0.645 | 0.00 | 10.035 | 0.000 | 0.12 | 0.000 | 1.08 | 0.00 |
| 52 | 5.0 | 1.5 | 3.0 | 1.0 | 5.250 | 4.755 | 5.970 | 0.950 | 6.450 | 5.64 | 6.690 | 1.650 | 1.20 | 0.885 | 0.72 | 0.70 |
| 53 | 1.5 | 1.5 | 3.0 | 0.5 | 1.575 | 4.755 | 5.970 | 0.475 | 1.935 | 5.64 | 6.690 | 0.825 | 0.36 | 0.885 | 0.72 | 0.35 |
| 54 | 3.0 | 0.5 | 3.0 | 1.0 | 3.150 | 1.585 | 5.970 | 0.950 | 3.870 | 1.88 | 6.690 | 1.650 | 0.72 | 0.295 | 0.72 | 0.70 |
| 55 | 3.0 | 0.5 | 1.0 | 0.5 | 3.150 | 1.585 | 1.990 | 0.475 | 3.870 | 1.88 | 2.230 | 0.825 | 0.72 | 0.295 | 0.24 | 0.35 |
| 56 | 1.5 | 1.0 | 3.0 | 0.5 | 1.575 | 3.170 | 5.970 | 0.475 | 1.935 | 3.76 | 6.690 | 0.825 | 0.36 | 0.590 | 0.72 | 0.35 |
| 57 | 2.0 | 0.5 | 4.5 | 0.0 | 2.100 | 1.585 | 8.955 | 0.000 | 2.580 | 1.88 | 10.035 | 0.000 | 0.48 | 0.295 | 1.08 | 0.00 |
| 58 | 0.0 | 3.0 | 1.0 | 0.5 | 0.000 | 9.510 | 1.990 | 0.475 | 0.000 | 11.28 | 2.230 | 0.825 | 0.00 | 1.770 | 0.24 | 0.35 |
| 59 | 2.0 | 0.0 | 3.0 | 0.0 | 2.100 | 0.000 | 5.970 | 0.000 | 2.580 | 0.00 | 6.690 | 0.000 | 0.48 | 0.000 | 0.72 | 0.00 |
| 60 | 1.5 | 0.5 | 1.0 | 1.0 | 1.575 | 1.585 | 1.990 | 0.950 | 1.935 | 1.88 | 2.230 | 1.650 | 0.36 | 0.295 | 0.24 | 0.70 |
| 61 | 7.5 | 1.0 | 0.0 | 1.0 | 7.875 | 3.170 | 0.000 | 0.950 | 9.675 | 3.76 | 0.000 | 1.650 | 1.80 | 0.590 | 0.00 | 0.70 |
| 62 | 1.0 | 2.0 | 4.5 | 1.0 | 1.050 | 6.340 | 8.955 | 0.950 | 1.290 | 7.52 | 10.035 | 1.650 | 0.24 | 1.180 | 1.08 | 0.70 |
| 63 | 1.0 | 1.5 | 0.0 | 1.0 | 1.050 | 4.755 | 0.000 | 0.950 | 1.290 | 5.64 | 0.000 | 1.650 | 0.24 | 0.885 | 0.00 | 0.70 |
| 64 | 7.5 | 1.5 | 1.0 | 1.0 | 7.875 | 4.755 | 1.990 | 0.950 | 9.675 | 5.64 | 2.230 | 1.650 | 1.80 | 0.885 | 0.24 | 0.70 |
| 65 | 2.0 | 0.5 | 3.0 | 1.0 | 2.100 | 1.585 | 5.970 | 0.950 | 2.580 | 1.88 | 6.690 | 1.650 | 0.48 | 0.295 | 0.72 | 0.70 |
| 66 | 4.0 | 1.5 | 0.0 | 3.5 | 4.200 | 4.755 | 0.000 | 3.325 | 5.160 | 5.64 | 0.000 | 5.775 | 0.96 | 0.885 | 0.00 | 2.45 |
| 67 | 7.5 | 3.0 | 4.5 | 1.0 | 7.875 | 9.510 | 8.955 | 0.950 | 9.675 | 11.28 | 10.035 | 1.650 | 1.80 | 1.770 | 1.08 | 0.70 |
| 68 | 3.0 | 0.5 | 0.0 | 0.0 | 3.150 | 1.585 | 0.000 | 0.000 | 3.870 | 1.88 | 0.000 | 0.000 | 0.72 | 0.295 | 0.00 | 0.00 |
| 69 | 1.5 | 0.5 | 3.0 | 1.0 | 1.575 | 1.585 | 5.970 | 0.950 | 1.935 | 1.88 | 6.690 | 1.650 | 0.36 | 0.295 | 0.72 | 0.70 |
| 70 | 1.0 | 1.5 | 0.0 | 0.5 | 1.050 | 4.755 | 0.000 | 0.475 | 1.290 | 5.64 | 0.000 | 0.825 | 0.24 | 0.885 | 0.00 | 0.35 |
| 71 | 1.0 | 1.5 | 1.0 | 1.0 | 1.050 | 4.755 | 1.990 | 0.950 | 1.290 | 5.64 | 2.230 | 1.650 | 0.24 | 0.885 | 0.24 | 0.70 |
| 72 | 1.0 | 1.5 | 3.0 | 1.0 | 1.050 | 4.755 | 5.970 | 0.950 | 1.290 | 5.64 | 6.690 | 1.650 | 0.24 | 0.885 | 0.72 | 0.70 |
| 73 | 5.0 | 0.5 | 1.0 | 1.0 | 5.250 | 1.585 | 1.990 | 0.950 | 6.450 | 1.88 | 2.230 | 1.650 | 1.20 | 0.295 | 0.24 | 0.70 |
| 74 | 1.5 | 1.0 | 1.0 | 1.0 | 1.575 | 3.170 | 1.990 | 0.950 | 1.935 | 3.76 | 2.230 | 1.650 | 0.36 | 0.590 | 0.24 | 0.70 |
| 75 | 0.5 | 3.0 | 1.0 | 3.5 | 0.525 | 9.510 | 1.990 | 3.325 | 0.645 | 11.28 | 2.230 | 5.775 | 0.12 | 1.770 | 0.24 | 2.45 |
| 76 | 4.0 | 0.5 | 3.0 | 1.0 | 4.200 | 1.585 | 5.970 | 0.950 | 5.160 | 1.88 | 6.690 | 1.650 | 0.96 | 0.295 | 0.72 | 0.70 |
| 77 | 3.0 | 1.5 | 4.5 | 0.5 | 3.150 | 4.755 | 8.955 | 0.475 | 3.870 | 5.64 | 10.035 | 0.825 | 0.72 | 0.885 | 1.08 | 0.35 |
| 78 | 2.0 | 1.0 | 1.0 | 0.5 | 2.100 | 3.170 | 1.990 | 0.475 | 2.580 | 3.76 | 2.230 | 0.825 | 0.48 | 0.590 | 0.24 | 0.35 |
| 79 | 3.0 | 0.5 | 0.0 | 0.0 | 3.150 | 1.585 | 0.000 | 0.000 | 3.870 | 1.88 | 0.000 | 0.000 | 0.72 | 0.295 | 0.00 | 0.00 |
| 80 | 3.0 | 0.5 | 1.0 | 0.0 | 3.150 | 1.585 | 1.990 | 0.000 | 3.870 | 1.88 | 2.230 | 0.000 | 0.72 | 0.295 | 0.24 | 0.00 |
| 81 | 10.0 | 0.0 | 1.0 | 0.0 | 10.500 | 0.000 | 1.990 | 0.000 | 12.900 | 0.00 | 2.230 | 0.000 | 2.40 | 0.000 | 0.24 | 0.00 |
| 82 | 4.0 | 0.0 | 0.0 | 1.0 | 4.200 | 0.000 | 0.000 | 0.950 | 5.160 | 0.00 | 0.000 | 1.650 | 0.96 | 0.000 | 0.00 | 0.70 |
| 83 | 2.0 | 1.0 | 1.0 | 0.5 | 2.100 | 3.170 | 1.990 | 0.475 | 2.580 | 3.76 | 2.230 | 0.825 | 0.48 | 0.590 | 0.24 | 0.35 |
| 84 | 2.0 | 1.5 | 3.0 | 0.0 | 2.100 | 4.755 | 5.970 | 0.000 | 2.580 | 5.64 | 6.690 | 0.000 | 0.48 | 0.885 | 0.72 | 0.00 |
| 85 | 2.0 | 1.5 | 3.0 | 1.0 | 2.100 | 4.755 | 5.970 | 0.950 | 2.580 | 5.64 | 6.690 | 1.650 | 0.48 | 0.885 | 0.72 | 0.70 |
| 86 | 1.5 | 1.5 | 1.0 | 0.0 | 1.575 | 4.755 | 1.990 | 0.000 | 1.935 | 5.64 | 2.230 | 0.000 | 0.36 | 0.885 | 0.24 | 0.00 |
| 87 | 5.0 | 2.0 | 1.0 | 0.5 | 5.250 | 6.340 | 1.990 | 0.475 | 6.450 | 7.52 | 2.230 | 0.825 | 1.20 | 1.180 | 0.24 | 0.35 |
| 88 | 7.5 | 0.0 | 0.0 | 3.5 | 7.875 | 0.000 | 0.000 | 3.325 | 9.675 | 0.00 | 0.000 | 5.775 | 1.80 | 0.000 | 0.00 | 2.45 |
| 89 | 7.5 | 0.0 | 3.0 | 1.0 | 7.875 | 0.000 | 5.970 | 0.950 | 9.675 | 0.00 | 6.690 | 1.650 | 1.80 | 0.000 | 0.72 | 0.70 |
| 90 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.00 | 0.000 | 0.000 | 0.00 | 0.000 | 0.00 | 0.00 |
par(mfrow=c(1,1))
plot(oats.cost, type = "o", col = "red", xlab = "Day", ylab = "Dollars", main = "Oats Cost, Renenue and Profit by Day")
lines(oats.revenue, type = "o", col = "blue")
lines(oats.profit, type = "o", col = "green")
plot(peas.cost, type = "o", col = "red", xlab = "Day", ylab = "Dollars", main = "Peas Cost, Renenue and Profit by Day")
lines(peas.revenue, type = "o", col = "blue")
lines(peas.profit, type = "o", col = "green")
plot(beans.cost, type = "o", col = "red", xlab = "Day", ylab = "Dollars", main = "Beans Cost, Renenue and Profit by Day")
lines(beans.revenue, type = "o", col = "blue")
lines(beans.profit, type = "o", col = "green")
plot(barley.cost, type = "o", col = "red", xlab = "Day", ylab = "Dollars", main = "Barley Cost, Renenue and Profit by Day")
lines(barley.revenue, type = "o", col = "blue")
lines(barley.profit, type = "o", col = "green")
# Total cost, revenue and profit by products in 90 days
# Oats
total.cost.oats <- sum(oats.cost)
total.revenue.oats <- sum(oats.revenue)
total.profit.oats <- sum(oats.profit)
df1 <- data.frame(total.cost.oats, total.revenue.oats, total.profit.oats)
colnames(df1) <- c("Total Oats Cost($)", "Total OatsRevenue($)", "Total Oats Profit($)")
knitr::kable(df1)| Total Oats Cost($) | Total OatsRevenue($) | Total Oats Profit($) |
|---|---|---|
| 282.45 | 347.01 | 64.56 |
# Peas
total.cost.peas <- sum(peas.cost)
total.revenue.peas <- sum(peas.revenue)
total.profit.peas <- sum(peas.profit)
df2 <- data.frame(total.cost.peas, total.revenue.peas, total.profit.peas)
colnames(df2) <- c("Total Peas Cost($)", "Total Peas Revenue($)", "Total Peas Profit($)")
knitr::kable(df2)| Total Peas Cost($) | Total Peas Revenue($) | Total Peas Profit($) |
|---|---|---|
| 329.68 | 391.04 | 61.36 |
total.cost.beans <- sum(beans.cost)
total.revenue.beans <- sum(beans.revenue)
total.profit.beans <- sum(beans.profit)
df3 <- data.frame(total.cost.beans, total.revenue.beans, total.profit.beans)
colnames(df3) <- c("Total Beans Cost($)", "Total Beans Revenue($)", "Total Beans Profit($)")
knitr::kable(df3)| Total Beans Cost($) | Total Beans Revenue($) | Total Beans Profit($) |
|---|---|---|
| 345.265 | 386.905 | 41.64 |
total.cost.barley <- sum(barley.cost)
total.revenue.barley <- sum(barley.revenue)
total.profit.barley <- sum(barley.profit)
df4 <- data.frame(total.cost.barley, total.revenue.barley, total.profit.barley)
colnames(df4) <- c("Total Barley Cost($)", "Total Barley Revenue($)", "Total Barley Profit($)")
knitr::kable(df4)| Total Barley Cost($) | Total Barley Revenue($) | Total Barley Profit($) |
|---|---|---|
| 77.9 | 135.3 | 57.4 |