In order to address scale dependency of the correlation formula Corr, y = ENE x[n]y[n]we useO ConvolutionO Length Normalized CorrelationCorrelation with j=lagO Normalized correlation

Answered: In order to address scale dependency of…

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 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?
A method to measure how well predictions fit actual data is tracking signal decomposition regression smoothing
Is this statement true? When outliers are important then it is important not to change the current minimum and maximum for normalization. False True
Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1 - 5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE Model 2 Model 3 Model 5 Model 4 Model 1 1 <0.000001 0.955978 0.792802 18.277907 1 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 4 2 <0.000001 0.928791 0.759583 17.253086 3 <0.000001 <0.000001 0.928295 0.855568 0.761951 0.652212 17.234646 14.639962 Test Data…
A Ridge Linear Regression adds the sum of the squared values of the coefficients to the loss function to penalize large coefficients. Group of answer choices True False
The first principal component is a normalized linear combination of the original variables with the largest varianceTrueFalse
A threshold of total variability explained has been set at 85%. How many principal components must you select?
4. Outliers can be detected using a statistical approach, one of popular approaches is the Inter Quartile Range (IQR) method. A sample is categorized as an outlier if its value is less than (Q1 - 1.5 * IQR) or greater than (Q3 + 1.5 * 1QR). Use these conditions to detect outliers in the following data: 130,132,138,136,131,153,131,133,129,133,110,132,129,134,135,132, 135,134,133,132,130,131,134,135,135,134,136,133,133,130 note: Q1 = quartile 1, Q3 = quartile 3 Draw the Boxplot to show the outliers %3D
We estimate the following LRM for the relationship between COURSE_EVAL and AGE of the instructor: E(COURSE_EVAL | AGE) = 4.774 -0.2154 * log(AGE) where the AGE variable is transformed with the logarithmic function. Provide an interpretation of the coefficient estimate -0.2154.
2. Can you design a binary classification experiment with 100 total population (TP+TN+FP+ FN), with precision (TP/(TP+FP)) of 1/2, with sensitivity (TP/(TP+FN)) of 2/3, and specificity (TN/(FP+TN)) of 3/5? (Please consider the population to consist of 100 individuals.)
Why do we use correlations? To see if variable A causes changes in variable B O To see if there is a relationship between variable A and variable B como hom 200 1) QUE GRAN C O To find out where variable A and variable B came from
A bank has a new potential customer seeking a personal loan. To help pre-screen their customers, they performed a k-Nearest Neighbor analysis to determine if the new customer is more similar to those customers who received a loan (indicated by a "Yes") or those whose applications were rejected ("No"). The 10 customers which most closely resembled the new customer are listed in the table provided. This table also includes the Euclidean distance betwee the new customer and the indicated prior applicant as well as the indicator whether or not that prior customer received a loan: Customer Applicant Number 1 2 3 4 5 6 7 8 9 10 O No Closeness with Received Loan? New Applicant 5.38 1.37 6.89 6.9 2.67 8.28 0.77 3.35 0.14 3.03 Yes YES YES YES YES YES INO NO INO INO If the bank used k-Nearest Neighbor analysis when k=7, then how would the new potential customer be classified? NO O Need more information to decide

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In order to address scale dependency of the correlation formula Corr,y = EN= x[n]y[n] we use Convolution Length Normalized Correlation Correlation with j-lag O Normalized correlation