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The equilibrium expression for Kp for the reaction below is ________.               N2 (g) + O2 (g)   2NO (g)

Which reaction will shift to the left in response to a decrease in volume?  

The reaction    2NO2 → 2NO + O2  follows second-order kinetics. At 300 °C, [NO2] drops from 0.0100 M to 0.00650 M in 100.0 s. The rate constant for the reaction is ________ M-1s-1.  

The Keq for the equilibrium below is 7.52 × 10-2 at 480.0 °C.               2Cl2 (g) + 2H2O (g)   4HCl (g) + O2 (g)   What is the value of Keq at this temperature for the following reaction?               2HCl (g) + O2 (g) Cl2 (g) + H2O (g)  

Which solution has the greatest buffering capacity?  

Selecciona el mandato correcto Empleado (employee) a su jefe (boss): “Por favor _____ su nombre en el documento” 

Midterm Exam 2 – Open Book Section – Part 2 Instructions Save the .ipynb file in your working directory – the same directory where you will download the data files into. Read the question and create the code necessary within the code chunk section immediately below each question. Type your answer to the questions in the text block provided immediately after the response prompt. Once you’ve finished answering all questions, submit the file as a HTML on Canvas.  Ready? Let’s begin… Data Sets For Questions 1-5: Use the dataset “fraud_detection”. For Question 6: Use the dataset “poisson_data”.  The files of the two datasets are provided below: fraud_detection.csv poisson_data.csv Jupyter Notebook Python starter template: Spring2025_Midterm2_Python_Starter_Template-2.ipynb   Jupyter Notebook R starter template: Spring2025_Midterm2_R_Starter_Template-2.ipynb

Q3. Goodness of fit tests (Use trainData for this question) (12 points) a.i (1 point) State why we cannot perform goodness-of-fit (GOF) tests for model2. ii. (1 point) Describe how we can modify the dataset to enable goodness-of-fit testing. Provide a general approach. iii. ( 5 points) Convert the dataset accordingly and fit a logistic regression model. Name it ‘model3’, and display its summary. Note: Use the same predictors as used for model2. b. (2 points) Use the Deviance residuals to form goodness-of-fit hypothesis tests on model3. What do you conclude from the result of the test? c. (3 points) Compare the summary of model2 and model3. What changes do you observe? Explain.

Q1 Data Exploration (9 points) Use the dataset “fraud_detection” for this question. a.(3 points) What is the overall proportion of fraudulent vs. non-fraudulent transactions? Is the dataset imbalanced? b. (3 points) Compare the percentage of fraud cases with respect to the type of device used for transaction? Use also a visual approach to compare the percentage values. Which devices are more vulnerable? c. (3 points) If we group transactions by Transaction_Hour, can we identify fraud-prone time windows? Also use a visual apporach for your analysis.

Name of Test : Unit 8 & 9 Summative Assessment Duration of Test : 60 minutes The next steps will guide you through : 360° room scan Checking the annotations of your papers used Clearing your calculator.