final Q2.docx - Final Q2 12/5/2020 #Capture a Regression...

Final Q2
12/5/2020
#Capture a Regression Model with Only Significant Variables. Set the
dependent variable to 'Species'. Even though the variable is
categorical and regression is not the proper model to use we will
ignore this fact for the purpose of the assignment.
data = read.csv ( "iris_exams.csv" )
str (data)
## 'data.frame': 300 obs. of 6 variables:
## $ id : chr "S001" "S002" "S003" "S004" ...
## $ Species : chr "setosa" "setosa" "setosa" "setosa" ...
## $ Sepal.Length: num 4.75 5.07 5.24 5.48 4.9 ...
## $ Sepal.Width : num 3.3 3.68 3.44 3.96 2.81 ...
## $ Petal.Length: num 1.44 1.21 1.59 1.53 1.49 ...
## $ Petal.Width : num 0.235 0.111 0.405 0.272 0.345 ...
## 'data.frame': 300 obs. of 6 variables:
## $ id : chr "S001" "S002" "S003" "S004" ...
## $ Species : chr "setosa" "setosa" "setosa" "setosa" ...
## $ Sepal.Length: num 4.75 5.07 5.24 5.48 4.9 ...
## $ Sepal.Width : num 3.3 3.68 3.44 3.96 2.81 ...
## $ Petal.Length: num 1.44 1.21 1.59 1.53 1.49 ...
## $ Petal.Width : num 0.235 0.111 0.405 0.272 0.345 ...
data $ Species = as.numeric ( as.factor (data $ Species))
model = lm (Species ~ Petal.Width, data = data)
summary (model)
##
## Call:
## lm(formula = Species ~ Petal.Width, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.68016 -0.12698 -0.00481 0.12121 0.77207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.78346 0.02362 33.17 <2e-16 ***
## Petal.Width 1.01522 0.01655 61.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##