# Displaying histogram plots for the 'hp' columnplt.figure()plt.subplot(3,1,1)plt.title("Histogram: Original data - 'hp''')ax1 =df_new["hp"].plot.hist(bins=50, alpha=1)plt.subplot(3,1,2)plt.title("Histogram: After min-max scaling - 'hp'')ax2 = df_scaled ["hp"].plot.hist(bins=50, alpha=1)plt.subplot(3,1,3)plt.title("Histogram: After standardization - 'hp'")df std["hp"].plot.hist(bins=50, alpha=1)plt.tight_layout ()ax3 = It is a very effective technique which re-scales a feature value so that it has distribution with 0 mean value and variance equals to 1.Standardization rescales data to have a mean (µ) of 0 and standard deviation (a) of 1 (unit variance).X stdX-µStandardization can be visualized as a shifting and stretching/shrinking process.# Perform standardization on all the columns of 'df_new'# Store the modified dataframe in a new dataframe 'df_std'#YOUR CODE HERE:# Generate descriptive statistics after standardization# Observe that the mean and standard deviation statistics have been modifieddf std.describe()

Answered: Image /qna-images/answer/8e556240-364e-4a3f-b75b-7424759635a6.jpg

Answered: It is a very effective technique which…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Consider the following Python code: # 1.a first estimate model using NWS SE # dictionary with cov options # cov_opts = {'maxlags':24, # number of NW lags for SE # 'use_correction':True} # small sample correction # reg_fit = smf.ols("LeadReal ~ 1", data=df).fit(cov_type='HAC', # cov type # # 1.b first estimate model using reg_fit = smf.ols("LeadReal ~ 1", data=df).fit() cov_kwds=cov_opts) # 2. extract info from reg object reg_fit.params reg_fit.bse # extract only coeff # extract only SE reg_fit.summary() # print nice reg table with more info a. b. C. d. This code produces HAC-adjusted standard errors as is This code features a biased estimator The commented (#) part of this code specifies the options required for the HAC-adjusted standard errors All of the above
Which of the following statement or statements are true ? Please check all that apply. Both the linkedlist and array size needs to be pre-decided Arrays have better cache locality that can make them better in terms of performance. It's easier to insert and delete elements from array than from the linkedlist Data type of the linked list doesnot need to be same, whereas in array, it must be the same memory can be allocated at runtime both for linkedlist and dynamic array Which of the following statements are true? Check all that applies 5 points Accessing any element of an array has the time complexity of O(n) Searching for an element in array and linkedlist has the same time complexity Inserting or Deleting an element from a stack or queue has the same time complexity Accessing an element from a singly…
In a 384-well plate luciferase assay, we drugged 4 receptors (A, B, C, D) with their endogenous agonists at a range of doses. Below is a subset of the resulting data frame (please use the full data frame attached to the original email as a CSV file). Each row of this data frame corresponds to a single well of the 384 well plate. The columns are as follows: receptor: the receptor tested in the well agonist: the endogenous agonist used for a receptor agonist_nM: the dose of the endogenous compound (in nanomolar) ● RLU: raw luminescence of the well (our measure of receptor activity) To answer this 3 part question, we’ll ask you to submit a PDF file containing all of your source code, plots, and written interpretation. (Using something like a Jupyter Notebook with Python or Rmarkdown is recommended, but not required.) You should provide brief explanations of your thought process at each step. Part A: Different receptors have different basal rates of activity. To better facilitate…

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