Alternative Assessment 2
Irfan Abidin As Salik Bin Noor Riza Al Jeffery (17204593)
5/24/2021
a) Interprating the confusion matrix
In this example, the model correctly predicted for Tested Positive is 120 times and incorrectly predicted it 15 times. The model correctly predicted for Tested Negative is 50 times and incorrectly predicted it 10 times.
The following can be computed from this confusion matrix:
- The model made 170 correct predictions (120 + 50).
- The model made 25 incorrect predictions (15 + 10).
- There are 195 total scored cases (120 + 15 + 10 + 50).
- The error rate is 25/195 = 0.1282.
- The overall accuracy rate is 170/195 = 0.8718.
b) EDA and Codebook
EDA
Description
- An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.
Format
A data frame with 71 observations on the following 2 variables.
weight = a numeric variable giving the chick weight.
feed = a factor giving the feed type.
Details - Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.
Dataset of Chicken Weight by Feed Type(first 6 data)
## weight feed
## 1 179 horsebean
## 2 160 horsebean
## 3 136 horsebean
## 4 227 horsebean
## 5 217 horsebean
## 6 168 horsebean
Codebook
-Categorical Variables
##
## casein horsebean linseed meatmeal soybean sunflower
## 12 10 12 11 14 12- The summary for each feed type.
## chickwts$feed: casein
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 216.0 277.2 342.0 323.6 370.8 404.0
## ------------------------------------------------------------
## chickwts$feed: horsebean
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 108.0 137.0 151.5 160.2 176.2 227.0
## ------------------------------------------------------------
## chickwts$feed: linseed
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 141.0 178.0 221.0 218.8 257.8 309.0
## ------------------------------------------------------------
## chickwts$feed: meatmeal
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 153.0 249.5 263.0 276.9 320.0 380.0
## ------------------------------------------------------------
## chickwts$feed: soybean
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 158.0 206.8 248.0 246.4 270.0 329.0
## ------------------------------------------------------------
## chickwts$feed: sunflower
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 226.0 312.8 328.0 328.9 340.2 423.0- The standard deviation of the weights grouped by feed type.
## chickwts$feed: casein
## [1] 323.5833
## ------------------------------------------------------------
## chickwts$feed: horsebean
## [1] 160.2
## ------------------------------------------------------------
## chickwts$feed: linseed
## [1] 218.75
## ------------------------------------------------------------
## chickwts$feed: meatmeal
## [1] 276.9091
## ------------------------------------------------------------
## chickwts$feed: soybean
## [1] 246.4286
## ------------------------------------------------------------
## chickwts$feed: sunflower
## [1] 328.9167c) Demonstration of dplyr for data manipulation
## Year Month Palestinians.Killed Israelis.Killed
## 1 2000 DECEMBER 51 8
## 2 2000 NOVEMBER 112 22
## 3 2000 OCTOBER 104 10
## 4 2000 SEPTEMBER 16 1
## 5 2001 DECEMBER 67 36
## 6 2001 NOVEMBER 39 14
## 7 2001 OCTOBER 89 14
## 8 2001 SEPTEMBER 59 13
## 9 2001 AUGUST 37 26
## 10 2001 JULY 32 10i) Changing the existing column name.
Palestine1 <- dplyr :: rename(Palestine, "Palestinians_Killed" = Palestinians.Killed, "Israelis_Killed" = Israelis.Killed)- Changed the column name from Palestinians.Killed to Palestinians_Killed
- Changed the column name from Israelis.Killed to Israelis_Killed
- This changes used (dplyr::rename(dataframe,existing.name = new.name))
ii) Pick rows based on their values.
dplyr::filter(Palestine1, as.numeric(Palestinians_Killed) > 100)## Year Month Palestinians_Killed Israelis_Killed
## 1 2000 NOVEMBER 112 22
## 2 2000 OCTOBER 104 10- Picked the row with Palestinians_Killed which are more than 100 by using (dplyr::filter(dataframe, condition))
iii) Add new columns to a data frame.
Palestine2 <- dplyr::mutate(Palestine1, Palestinian_Injured = as.numeric(Palestinians_Killed)*2)- Added a new columns named “Palestinian_Injured” and the data are (Palestinians_Killed*2)
- by using _(dplyr::mutate(dataframe, new.variable = choosed.variable*2))_
iv) Combine data across two or more data frames
dplyr::bind_cols(Palestine2, PalestineCombine)## Year Month Palestinians_Killed Israelis_Killed Palestinian_Injured
## 1 2000 DECEMBER 51 8 102
## 2 2000 NOVEMBER 112 22 224
## 3 2000 OCTOBER 104 10 208
## 4 2000 SEPTEMBER 16 1 32
## 5 2001 DECEMBER 67 36 134
## 6 2001 NOVEMBER 39 14 78
## 7 2001 OCTOBER 89 14 178
## 8 2001 SEPTEMBER 59 13 118
## 9 2001 AUGUST 37 26 74
## 10 2001 JULY 32 10 64
## Israelis.Injuries
## 1 n/a
## 2 n/a
## 3 n/a
## 4 n/a
## 5 n/a
## 6 n/a
## 7 n/a
## 8 n/a
## 9 n/a
## 10 n/a- Combined 2 set of dataframe (Palestine2) & (PalestineCombine) into one dataframe by using (dplyr::bind_cols(dataframe1, dataframe2))