Say you have several differently ordered polynomial models. Which of the following statistics will best help you decide which model to use?1 pointMean-squared errorCoefficient of determinationCorrelation coefficientAlpha

What order polynomial regression has been fit to this data?

Question 3What’s the correct order for using a model? 1 pointSplit the data into training and test sets, fit the model on the train set, evaluate model accuracy.Clean the data, split the data into training and test sets, fit the model on the train set, evaluate model accuracy.Split the data into the training and test sets, fit the model on the train set, clean the data, evaluate model accuracy.Clean the data, fit the model on the entire dataset, split the data into training and test sets, evaluate model accuracy.

Which of the following models will you choose for forecasting the sales of the mobile phone?Model 1 with RMSE 2.33e+02Model 2 with RMSE 0.235e+03Model 3 with RMSE 2.32+e03Model 4 with RMSE 2.33e+05Answer choicesSelect only one optionREVISITModel 3Model 2Model 1Model 4

Question 6All of these options are useful error measures to compare regressions except:1 pointSSER squaredTSSROC index

(b) Check the goodness of fit of the following hierarchical models:• (M1) main effects only• (M2) all two-way interaction terms• (M3) all three-way interaction termsWhat is the lowest order model that reasonably fits the data? Give reasons.(c) What is the error message if you fit the four- and five-way interaction terms?What may be causing this?(d) Based on the model chosen in part (a), perform forward selection using partitionedG2 statistics to choose a best model. Justify your steps in a manner similar to thelecture notes