Can someone help me answer these econometrics questions using python and only python language please and thank you! I'm willing to pay extra I really need the help! thank you in advance again this is the link needed to answer the questions

https://raw.githubusercontent.com/AlR0d/courses/main/4421/br.csv

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4. Housing sales data, including sale price, house size, age, whether it has a pool, or it is on the waterfront can be found here. The data also includes a variable traditional which indicates whether the architecture of the home is traditional or modern. a. Plot a scatter plot of housing prices against square footage. b. Estimate the linear regression model, price; = o + sqft; + . Report and interpret your results. c. Now, estimate the quadratic model price; o + sqft + ; and derive the marginal effect of sq ft on price. Report and interpret your results. = d. Derive the predicted value of price for both models, and on one graph, plot the sample regression lines (SRL) of both models, along with a scatterplot of the data. e. One basis of choosing how well the models, in parts (b) and (c), fit the data, other than R, is by looking at which model has a lower residual sum-of-squares (RSS). Calculate the RSS for both models and report and interpret your findings. 4. Housing sales data, including sale price, house size, age, whether it has a pool, or it is on the waterfront can be found here. The data also includes a variable traditional which indicates whether the architecture of the home is traditional or modern. a. Plot a scatter plot of housing prices against square footage. b. Estimate the linear regression model, price; = o + sqft; + . Report and interpret your results. c. Now, estimate the quadratic model price; o + sqft + ; and derive the marginal effect of sq ft on price. Report and interpret your results. = d. Derive the predicted value of price for both models, and on one graph, plot the sample regression lines (SRL) of both models, along with a scatterplot of the data. e. One basis of choosing how well the models, in parts (b) and (c), fit the data, other than R, is by looking at which model has a lower residual sum-of-squares (RSS). Calculate the RSS for both models and report and interpret your findings.