Going back to the scatterplot in Q2, explain how adding a data point (0,2) will impact correlation and why. Please be concise. This can be explained in 1-2 sentences.  

Using the regression equation in Q9, calculate the residuals for the data points (3,12) and (5,2), and include your calculations.

Calculate the regression equation for predicting the risk of engaging in cyberbullying from self-esteem. Use precise numbers for all calculations. Round the final answers to two decimals. Ŷ = X +

Let’s now work with both numerical and categorical features.   Create a predictive model where: The numerical data must be scaled. The categorical data must be one-hot encoded. Use the “infrequent_if_exist” strategy to handle unknown categories. For such purpose, set  `min_frequency=0.05` to group categories concerning less than 5% of the total samples. The predictor is a `LogisticRegressionCV` with same parameters as before,  except that you may need to increase the number of `max_iter`, which is 100 by default. Use the same 10-fold cross-validation strategy with `return_estimator=True` as above to evaluate the accuracy of this new pipeline. By comparing the cross-validation test scores of both models fold-to-fold, count the number of times the model using both numerical and categorical features has a better test score than the model using only numerical features.

Which of the following pairs of features is most impacting the predictions of this new pipeline based on the absolute magnitude of its coefficients?

A switch/router marks packets if its queue size exceeds a threshold/buffer.

Which of the following is not correct about the experimental result below?

DCTCP, router uses IP ECN bits

What are these OpenFlow Flow Table mean:         Write in one line.

chunk size is fixed as 64 MB.