The ABX Company is interested in conducting a study of the factors that affect absenteeism among its≈α= 0.05
production employees. Data on 77 employees of the ABX Company have been collected. These data areavailable in the worksheet entitled "ABSENT7R". The variable ABSENT is the number of distinct occasionsthat the worker was absent during 2003. (Each occasion consists of one or more consecutive days ofabsence.) The following possible explanatory variables are:In this exercise, use which is the reciprocal of the seniority variable, and COMPLXas two of the explanatory variables. The variable SATIS should be transformed into indicator variables asfollows:HOWEVER, recall that five indicator variables couldbe created to represent all five supervisorsatisfaction categories, but only four need to be used in the regression.Therefore, fit thisregression model for absenteeism:ABSENT = β0+ β1COMPLX + β2SENINV + β3FS1 + β4FS2 + β5FS3 + β6FS4Run the regression with the explanatory variables described here. Answer the following questions.(a) Is there a difference in average absenteeism for employees in different supervisorsatisfaction groups? Perform a hypothesis test to answer this question. Use a 5% level ofsignificance. State the hypotheses to be tested

Using python you can load the dataframe using pandas library. Once you have your pandas library imported to the system you can also import the statsmodels.api module. What you do is this. You fit the model using the variables mentioned "COMPLX" ,"SENINV","FS1","FS2","FS3", once you fit the model you use .summary() and that will give you a summary of each coefficient and the level of significance, the level of significance must be less than 5% in order to be significant. The code would look like this.

import pandas as pd

import import statsmodels.api as sm

dataframe = pd.read_csv(Your model)

mod = sm.OLS(formula = ABSENT ~ COMPLX + SENING+FS1+FS2+FS3+FS4)

res = mod.fit()

print(res.summary())