Latihan 1: Pemasangan Polinomial

Menyesuaikan polinomial dengan data adalah masalah aljabar linier: menyusun vektor yang sesuai untuk mewakili berbagai kekuatan. Misalnya, inilah cara menyesuaikan model kuadrat dengan variabel ccfversus dalam file data:temp"utilities.csv”

library(mosaicCalc)
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Utils <- read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
gf_point(ccf ~ temp, data = Utils) %>%
  gf_labs(y = "Natural gas usage (ccf/month)", 
          x = "Average outdoor temperature (F)")

Utilities = read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
project(ccf ~ 1 + temp + I(temp^2), data = Utilities)
##  (Intercept)         temp    I(temp^2) 
## 317.58743630  -6.85301947   0.03609138

Koefisien memberi tahu kita bahwa model kuadrat yang paling pas dari ccfversus tempadalah:

ccfQuad <- makeFun(317.587 - 6.853*T + 0.0361*T^2 ~ T)
gf_point(ccf ~ temp, data = Utilities) %>%
  slice_plot(ccfQuad(temp) ~ temp)

Untuk mencari nilai model ini pada temperatur tertentu, evaluasi saja fungsinya. (Dan perhatikan bahwa ccfQuad( )didefinisikan dengan variabel input T.)

ccfQuad(T=72)
## [1] 11.3134
  1. Sesuaikan polinomial orde-3 versus dengan data utilitas. Berapa nilai model ini untuk suhu 32 derajat? {87.103.128, 142 .143.168.184} MENJAWAB:
project(ccf ~ 1 + temp + I(temp^2) + I(temp^3), data = Utils)
##   (Intercept)          temp     I(temp^2)     I(temp^3) 
##  2.550709e+02 -1.427408e+00 -9.643482e-02  9.609511e-04
ccfCubic <- 
  makeFun(2.551e2 - 1.427*T -
          9.643e-2*T^2 + 9.6095e-4*T^3 ~ T)
gf_point(ccf ~ temp, data = Utils) %>%
  slice_plot(ccfCubic(temp) ~ temp) 

ccfCubic(32)
## [1] 142.1801

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