import pandas as pdimport statsmodels.formula.api as smimport matplotlib.pyplot as pltX1 = np.random.normal (0, 1, 100)X2 =X1 + 3Y= X1 + X2 + np.random.normal(0, 1, 100)df = pd.DataFrame ({"Y": Y, "X1": X1, "X2": X2})linmodel = sm.ols(formula"YX1 + X2",datadf).fit()linmodel.summary()Output:Intercept-0.0272X11.1074X21.0257Warnings:[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.[2] The smallest eigenvalue is 3.27e-31. This might indicate that there arestrong multicollinearity problems or that the design matrix is singular.Which of the following statements are correct?a) Perfectly correlated regressors X1 and x2 are used.b) Either X1 or X2 should be excluded, as the second regressor does not add any information to the model.6.c) The second warning indicates that the model fit to the data is perfect.

Here Option A will be the right answer Explanation: Generally Multi collinearity problem occurs when…

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.

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.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Similar questions

Which statement about k-fold cross-validation is FALSE? Group of answer choices is typically used to tune and select the best hyper-parameters for the model On each step, one fold is used as the training data and the remaining k − 1 folds are used as testing data partitions the data into k non-overlapping folds The last step of the k-fold cross-validation is to compute the average performance estimate All observations are used for both training and validation
Consider a plot of a model of the form Y i = B 0 +B1T i + B2(X 1i-C) + e i. Which of the following is true? A. B2 is the bump at the cutoff B. B2 is the slope of the line C. B1 is the slope of the line D. B0 is the bump at the cutoff
given the observed data (obsX,obsY), learning rate (alpha), error change threshold, and delta from the huber loss model,write a function returns theta0 and theta1 that minimizes the error. Use pseudo huber loss function
If you are implementing regularised linear regression and when you tested your hypothesis in a new dataset you found that it suffer from high variance. How can you rectify the model ? Give 3 Solutions
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?
For classification of cancer or benign tumors, the training set has 1% patients have cancer, when trained logistic regression model, the error found to be 1%. Is the results are excellent or not? Why? Use the editor to format your answer
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
Problem 6: Suppose, you are given with a dataset of 5,000 images. Split the dataset into Training and Validation set using 6-fuld cross validation. Hint: How many images will be in training set and how many in validation set.
The benefits of switching to all-subsets regression from stepwise regression are broken forth in great depth below. .
plz run in r studio n answer all the questions biostats........ Worksheet T tests, Wilcoxon’s test, Mc Nemar’s Test Population Sample 1: 14, 15, 15, 15, 16, 18, 22, 23, 24, 25, 25Population Sample 2: 10, 12, 14, 15, 18, 22, 24, 27, 31, 33, 34, 34,34 8. Conduct a formal normality test and state the Null and alternateHypotheses9. Is the Test a two tailed, or one tailed (left tailed or right tailed)10. Choose and run the appropriate test Statistics on the data11. Interpret results and Accept/Reject your Null Hypothesis.12. Write your conclusion as discussed in the lectures and include yourHypothesis, pvalve (alpha value), test statistics and degrees of freedom (df).13. Set all significance level at 0.05 or where appropriate for nonparametric tests14. Do you understand how now how to choose the appropriate statisticsand why?