Answer:
Compare the predictions in terms of the predictors that were used, the magnitude of the difference between the two predictions, and the advantages and disadvantages of the two methods.
Our predictions for the two models were very simmilar. A difference of $32.78 (less than 1% of the total price of the car) is statistically insignificant in this case. Our binned model returned a whole number while the full model returned a more “accurate” price, but ultimately it is a wash. Both models had comparable accuracy, but the full regression seemed to be better trained. If we wanted to use the binned model I would suggest creating smaller bin ranges to prevent underfitting the model. However, when considering the the overall accuracy range and the car sale market both models would be
Explanation:
84÷2=42. 84÷3=28. 84÷4=21. 84÷6=14. So your answer is true.
Answer:
b. will be lower if consumers perceive mobile phones to be a necessity.
Explanation:
The price elasticity of demand is described as the percentage variation in the demanded quantity of service or goods divided by the change in the percentage of the price. And henceforth it describes the responsiveness of the demanded quantity to a price change. And now if the mobile phones are thought of as being the necessity then the price will increase as demand will increase, and hence the price elasticity of demand will be lower. And if there is an improvement in the production technology then the price will be lowered, and hence price elasticity of demand will be less as the change in the percentage of the price will be negative. And the exact definition of it as we have described above. Hence, b is correct options.
