1. Suppose that the data for analysis include the attribute age. The age values for the datatuples are 6, 11, 12, 13, 13, 25, 47, 55, 72, 81, 87, 90. Sort it in increasing order thenSmooth the data using following methods using a bin depth of 3.(i)smoothing by bin means(ii)smoothing by bin medians(iii)smoothing by bin mode(iv)smoothing by bin boundaries

Answered: Suppose that the data for analysis…

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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you will learn how to manually classify and cluster data based on a specific variable using R programming language. consists of retrieving data, classifying, plot a graph and interpretation of the outcomes. The dataset that are going to be used in this project is “mtcar” that is available in R studio. Methodology: Using the number of cylinders (cyl column) in mtcar dataset(below), classify cars as 4-, 6- or 8-cylinders car. For each class, give a color to the values that belong to that class. Then plot a graph for using “mpg” column as x-axis and “qsec” column as y-axis. Steps: Read the file using R programming language. Read values in column “cyl”. If x =8, then paint as red, if x =6, then paint as green, If x =4, then paint as blue. Plot the values of the columns mpg and qsec into an x&y graph. Plot painted values of “cyl” on the same graph. Finally, you have to add your interpretations of the results. This is very important!
Task 4: Given the data set with two dimensions X and Y: Calculate every step and not using libraries X Y HE 1435 4232 Use a linear regression method to calculate the parameters a and ß where y = a + Bx. (Show every step and not using libraries)
We use the Breast Cancer Wisconsin dataset from UCI machine learning repository: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 Data File: breast-cancer-wisconsin.data (class: 2 for benign, 4 for malignant) Data Metafile: breast-cancer-wisconsin.names Please implement this algorithm for logistic regression (i.e., to minimize the cross-entropy loss as discussed in class), and run it over the Breast Cancer Wisconsin dataset. Please randomly sample 80% of the training instances to train a classifier and then testing it on the remaining 20%. Ten such random data splits should be performed and the average over these 10 trials is used to estimate the generalization performance. You are expected to do the implementation all by yourself so you will gain a better understanding of the method. Please submit: (1) your source code (or Jupyter notebook file) that TA should be able to (compile and) run, and the…
How do you construct a good and bad match table with Boyer Moore? What do the tables mean and can you please provide an example.
The first principal component is a normalized linear combination of the original variables with the largest varianceTrueFalse
PEAR: sort the data according to the employees salary (the last column). Use the Schwartzian transform method.
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…
Please implement Multinomial Logistic Regression on the following data. Please continue from the given code:
Which statement best describes k-means cluster analysis? Question 9 options: It is the process of organizing observations into distinct groups based on a measure of similarity or dissimilarity. It is the process of agglomerating observations into a series of nested groups based on a measure of similarity or dissimilarity. It is the process of reducing the number of variables to consider in data-mining. It is the process of estimating the value of a continuous outcome variable.
The following dataset is a historic record of 14 houses that were sold in a small town in BC. The dataset is used to predict whether a new house in the same town will be sold in 10 days if listed with a specific price based on certain attributes. We are considering only four attributes (price, number of bedrooms, size, and distance to bus stop) just to simplify the calculations in this assignment but more attributes should be considered in real applications. House Price Number of Bedrooms Size (sqft) Distance to Bus-Stop House sold in 10 days? House 1 $300,000 1 3,500 sqft far No House 2 $300,000 1 3,500 sqft near No House 3 $250,000 1 3,500 sqft far Yes House 4 $350,000 2 3,500 sqft far Yes House 5 $350,000 3 5,000 sqft far Yes House 6 $350,000 3 5,000 sqft near No House 7 $250,000 3 5,000 sqft near Yes House 8 $300,000 2 3,500 sqft far No House 9…
The following dataset is a historic record of 14 houses that were sold in a small town in BC. The dataset is used to predict whether a new house in the same town will be sold in 10 days if listed with a specific price based on certain attributes. We are considering only four attributes (price, number of bedrooms, size, and distance to bus stop) just to simplify the calculations in this assignment but more attributes should be considered in real applications. Build a decision tree to predict whether a new house listing in the same town will be sold in 10 days based on the given attributes. Use ID3 algorithm. To answer this question, you need to complete the following steps: Calculate the entropy of the whole dataset After identifying the first attribute, repeat the same steps to identify the next attribute to split on in every leaf of the tree based on information gain analysis. Repeat this step until you complete the tree. Draw the final tree
In the DBSCAN clustering algorithm, consider three border points A, B, and C. The numbers of other points within Eps distance from A, B, and C are 7, 2, and 5, respectively. What is the minimum possible value for the minpoints parameter of the DBSCAN algorithm?

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