Assignment 15

1

x <- c(5.6, 6.3, 7, 7.7, 8.4)
y <- c(8.8, 12.4, 14.8, 18.2, 20.8)

# regression
ans1 <- lm(y ~ x)
summary(ans1)
## 
## Call:
## lm(formula = y ~ x)
## 
## 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 ***
## x             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

Model is:

y = -14.8 + 4.2571(x)

3

x <- 2.3
y <- 4.1
z <- -21*(x^2) - 23*(y^2) + 28*x*y + 81*x +40*y
print(paste0("The revenue is ", z))
## [1] "The revenue is 116.62"

4

knitr::include_graphics('15.4.png')

5

e <- exp(1)

# Let's calculate the answer
answer4 <- ((e^44 - e^38)/24) - ((e^28 - e^22)/24)
print(answer4)
## [1] 5.341559e+17