Which of the following aqueous solutions has the lowest ?

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

The standard cell potential (E°cell) for the voltaic cell based on the reaction below is________ V. Sn2+ (aq) + 2Fe3+ (aq) → 2Fe2+ (aq) + Sn4+ (aq)    

Which element is oxidized in the reaction below?             I- + MnO4- + H+ → I2 + MnO2 + H2O    

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

The data in the table below were obtained for the reaction:             2 ClO2 (aq) + 2 OH- (aq) → ClO3- (aq) + ClO2- (aq) + H2O (1)     What is the order of the reaction with respect to ClO2?

Consider the following reaction at equilibrium:               2CO2 (g) 2CO (g) + O2 (g)      ΔH° = -514 kJ   Le Châtelier’s principle predicts that a decrease in temperature will ________.  

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

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.

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