Perfectly correlated regressors X1 and X2 are used. Either X1 or X2 should be excluded, as the second regressor does not add any The second warning indicates that the model fit to the data is perfect.

Outline both the null and alternative hypotheses for the Augmented Dickey-Fuller (ADF) test and KPSS (Kwaitowski, Phillips, Schmidt and Shin) test.

Please provide steps to answer this question: Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set

You trained the regression model with 100 regressors and 1000 observations in the training and another 1000 in the test sample. You found that in-sample R2 over the training sample is 70% and the out-of-sample R2 over the test sample only - 30%. (select all that apply) a) Do you think there is any problem and how would you characterize it? Can adding more regressors (if you have them) help the model? b) Which approaches you may use to solve the problem? c) What would you expect the in-sample R2 to increase or decrease after that? What about the out-of-sample (test) R2?

Question Study the dataset given in file ‘x03.csv’, read it in as a data frame. Use the linear regression method to find a possible linear relation between blood pressure and age. First plot the given sample points, then plot the linear model graph as only 2 variables; blood pressure and age are in the dataset. Finally, plot the residuals points and counts of the residuals, and evaluate the normality assumption for the dataset. x03.csv x03.csv Index Systolic age bp 1 1 39 144 2 1 47 220 3 1 45 138 4 1 47 145 5 1 65 162 6 1 46 142 7 1 67 170 8 1 42 124 9 1 67 158 10 1 56 154 11 1 64 162 12 1 56 150 13 1 59 140 14 1 34 110 15 1 42 128 16 1 48 130 17 1 45 135 18 1 17 114 19 1 20 116 20 1 19 124 21 1 36 136 22 1 50 142 23 1 39 120 24 1 21 120 25 1 44 160 26 1 53 158 27 1 63 144 28 1 29 130 29 1 25 125 30 1 69 175

The benefits of switching to all-subsets regression from stepwise regression are broken forth in great depth below.   .

You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy = (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives + false positives) Recall = (true positives)/ (true positives + false negatives) F1 score = (2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10

The following is true about sensitivity: Group of answer choices a) The output of the model is said to be inversely sensitive if the output of the model changes a small amount for a large change in an input variable b) Sensitivity is not an important concept in modeling c) It can help the modeler tell, on a relative basis, what are the important variables d) A variable is considered NOT very sensitive if a small change in the variable results `in a large change in the output of the model.

The non-parametric density-based approach assumes that the density around a normal data observation within a cluster (relatively big) is similar to the density around its neighbours, and the density around an outlier (relatively small) is considerably different to the density around its neighbours. If we had the density of observation within a cluster smaller than the density around an outlier, explain why we would have such a situation. And provide a solution to this problem.

You are developing a simulation model of a service system and are trying to create aninput model of the customer arrival Process, You have the following four observations of the process of interest [86, 24,9, 50] and you are considering either an exponential distributionOf a uniform distribution for the model. Using the data to estimate any necessary distributionParameters, write the steps to plot Q-Q plots for both cases.