ANLY500_Q3.docx - ANLY Q3 Khulan Solongozaya library(readr...

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ANLY Q3 Khulan Solongozaya library (readr) iris <- read_csv ( "iris_exams.csv" ) ## Parsed with column specification: ## cols( ## id = col_character(), ## Species = col_character(), ## Sepal.Length = col_double(), ## Sepal.Width = col_double(), ## Petal.Length = col_double(), ## Petal.Width = col_double() ## ) str (iris) ## tibble [300 x 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame) ## $ id : chr [1:300] "S001" "S002" "S003" "S004" ... ## $ Species : chr [1:300] "setosa" "setosa" "setosa" "setosa" ... ## $ Sepal.Length: num [1:300] 4.75 5.07 5.24 5.48 4.9 ... ## $ Sepal.Width : num [1:300] 3.3 3.68 3.44 3.96 2.81 ... ## $ Petal.Length: num [1:300] 1.44 1.21 1.59 1.53 1.49 ... ## $ Petal.Width : num [1:300] 0.235 0.111 0.405 0.272 0.345 ... ## - attr(*, "spec")= ## .. cols( ## .. id = col_character(), ## .. Species = col_character(), ## .. Sepal.Length = col_double(), ## .. Sepal.Width = col_double(), ## .. Petal.Length = col_double(), ## .. Petal.Width = col_double() ## .. ) head (iris) ## # A tibble: 6 x 6 ## id Species Sepal.Length Sepal.Width Petal.Length Petal.Width ## <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 S001 setosa 4.75 3.30 1.44 0.235 ## 2 S002 setosa 5.07 3.68 1.21 0.111 ## 3 S003 setosa 5.24 3.44 1.59 0.405
## 4 S004 setosa 5.48 3.96 1.53 0.272 ## 5 S005 setosa 4.90 2.81 1.49 0.345 ## 6 S006 setosa 5.58 3.86 1.88 0.306 iris $ Species <- factor (iris $ Species, levels = c ( "setosa" , "versicolor" , "virginica" ), labels = c ( 1 , 2 , 3 )) str (iris $ Species) ## Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ... iris $ Species <- as.numeric (iris $ Species) str (iris $ Species) ## num [1:300] 1 1 1 1 1 1 1 1 1 1 ... (1) ANOVA: Capture an ANOVA model on the iris dataset. Set the dependent variable to ‘Species’. Capture a summary. Provide an interpretation of the results in your own words. Support your response with results captured from running ANOVA.

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