Helps identify what is best for a given product based on the historical data.
Download the repository and unzip it.
In your terminal, run the following commands:
cd src
Then:
python main.py
OLS Regression Results
==============================================================================
Dep. Variable: CM $ R-squared: 0.839
Model: OLS Adj. R-squared: 0.838
Method: Least Squares F-statistic: 868.6
Date: Wed, 28 Jun 2023 Prob (F-statistic): 8.18e-198
Time: 08:05:04 Log-Likelihood: -5255.5
No. Observations: 504 AIC: 1.052e+04
Df Residuals: 500 BIC: 1.054e+04
Df Model: 3
Covariance Type: nonrobust
=======================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------
const 2218.4080 490.609 4.522 0.000 1254.499 3182.317
Gross Revenue 0.1840 0.004 41.726 0.000 0.175 0.193
Promotion Expense -0.6399 0.057 -11.310 0.000 -0.751 -0.529
Advertising Expense 0.3072 0.187 1.641 0.102 -0.061 0.675
==============================================================================
Omnibus: 199.580 Durbin-Watson: 2.226
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2268.621
Skew: -1.391 Prob(JB): 0.00
Kurtosis: 13.014 Cond. No. 1.85e+05
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.85e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
The Ordinary Least Squares (OLS) regression model helps to understand how 'Gross Revenue', 'Promotion Expense', and 'Advertising Expense' are related to 'CM
Looking at the results:
'Gross Revenue' has a positive coefficient of 0.1840, indicating that for each unit increase in 'Gross Revenue', there is a 0.1840 unit increase in 'CM $', holding all else constant.
'Promotion Expense' has a negative coefficient of -0.6399, suggesting that for each unit increase in 'Promotion Expense', there is a decrease of 0.6399 units in 'CM $', holding all else constant.
'Advertising Expense' has a positive coefficient of 0.3072. This means for each unit increase in 'Advertising Expense', there is an increase of 0.3072 units in 'CM $', holding all else constant.
However, we can't directly compare these coefficients to determine which variable contributes more to 'CM $', because the units of these variables might be different.
To further analyze the contributions of 'Advertising Expense' and 'Promotion Expense' to 'CM
Also, you might want to consider additional statistical techniques or models (like multivariate regression, interaction terms, etc.) and conducting a more detailed exploratory data analysis. Please consult a statistician or data scientist for a more in-depth analysis.