My regression model has the correct numbers, how would I answer the following questions? The only two discount offers is the discount and BOGO.

OLS Regression Results Dep. Variable: np. log (transaction_amount) R-squared: 0.327 Model: OLS Adj . R-squared: 0.327 Method: Least Squares F-statistic: 3018. Date: Fri, 16 Dec 2022 Prob (F-statistic) : 0.00 Time: 00:30:13 Log-Likelihood: -26101. No. Observations: 31026 AIC: 5.221e+04 Df Residuals: 31020 BIC: 5.226e+04 Df Model: 5 Covariance Type: nonrobust coef std err t P>| t ] [0. 025 0.975] Intercept -10. 1452 0.258 -39.387 0.000 -10.650 -9.640 offer_type [T. discount] -5.3469 0.243 -22.047 0.000 -5.822 -4.872 np. log (income) 1. 1484 0. 023 49.477 0.000 1. 103 1. 194 np. log ( income) : offer_type [T. discount] 0. 4627 0.022 21. 173 0.000 0. 420 0.506 difficulty 0. 4092 0. 029 14.255 0.000 0.353 0.465 np. log(income) : difficulty -0.0344 0. 003 -13.321 0. 000 -0. 039 -0.029 Omnibus : 6505. 395 Durbin-Watson: 1.980 Prob (Omnibus) : 0.000 Jarque-Bera (JB) : 125123.403 Skew: 0.505 Prob (JB) : 0.00 Kurtosis: 12. 786 Cond. No. 9. 44e+03 Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 9.44e+03. This might indicate that there are strong multicollinearity or other numerical problems.Edit this cell Demonstrate your understanding of interpretation of the regression here by filling the blanks in the statements below: 1. In terms of the the estimated coefficients, 1% increase in income when discount offer is made and qualification difficulty is $10 leads to approximately _ in transaction amount. 2. In terms of the estimated coefficients, 1% increase in income when BOGO offer is made and qualification difficulty is $5 leads to _in transaction amount. Basically, substitute the 's from the answer above with the estimated coefficients