Answer:
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())
Step-by-step explanation:
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())
95 cents all you do is divide 6.45/7 correct me if I read it wrong
The answer is c. See explanation below.
