Linear Regression

1. Choose your Outcome column




2. Choose your Numeric columns




3. Choose your Factor columns

Current linear model formula

                  

4. (Optional) Add factor interactions to the formula

n-way Interaction= (a+b+..)^n . i.e(output_col ~ (numeric_col1+numeric_col2)^2 + as.factor("factor_col1")

Interactive effect = a:b . i.e(output_col ~ +numeric_col1:numeric_col2 + as.factor("factor_col1")

Factorial Interactin = a*b . i.e(output_col ~ numeric_col1*numeric_col2 + as.factor("factor_col1")

Minus effect = -a:b . i.e(output_col ~ -numeric_col1:numeric_col2 + as.factor("factor_col1")

Nesting Term = a %in% b . i.e(output_col ~ numeric_col1 %in% numeric_col2 + as.factor("factor_col1")



4. Go to Results once 1-3 are picked, results change automatically when columns are changed

Raw summary


                  





Added Variable Plots



Box Plot



ROC Plot

Logistic Regression

1. Choose your Outcome column




2. Choose your Numeric columns




3. Choose your Factor columns

Current logistic model formula

                  

4. (Optional) Add factor interactions to the formula

n-way Interaction= (a+b+..)^n . i.e(output_col ~ (numeric_col1+numeric_col2)^2 + as.factor("factor_col1")

Interactive effect = a:b . i.e(output_col ~ +numeric_col1:numeric_col2 + as.factor("factor_col1")

Factorial Interactin = a*b . i.e(output_col ~ numeric_col1*numeric_col2 + as.factor("factor_col1")

Minus effect = -a:b . i.e(output_col ~ -numeric_col1:numeric_col2 + as.factor("factor_col1")

Nesting Term = a %in% b . i.e(output_col ~ numeric_col1 %in% numeric_col2 + as.factor("factor_col1")



4. Go to Results once 1-3 are picked, results change automatically when columns are changed

Raw summary


                  



Diagnostic Plots





Added Variable Plots

Box Plot





ROC Plot