Group A has a mean of 0 and a median of 15 What is most likely true? Question 8 options: Group A likely has a high outlier or right skew Group A is likely roughly symmetric and unimodal Group A likely has a low outlier or left skew Group A likely has low and high outliers. None of these In descriptive statistics, we are trying to determine what model fits our situation best. Question 12 options: True False

Group A has a mean of 0 and a median of 15 What is most likely true? Question 8 options: Group A likely has a high outlier or right skew Group A is likely roughly symmetric and unimodal Group A likely has a low outlier or left skew Group A likely has low and high outliers. None of these In descriptive statistics, we are trying to determine what model fits our situation best. Question 12 options: True False

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter13: Decision Making Under Uncertainty

Section13.5: Bayes’ Rule And Decision Trees

Problem 1P

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Question 4 A university received 1000 applications for the undergraduate program every semester. Of all applicants, 150 had a CGPA score above 3.70. 520 applicants had a CGPA score between 3.50 and 3.70. 100 applicants had a score between 3.00 and 3.50. All other applicants had a score lower than 3.00. What probability will a randomly selected applicant have a score lower than 3.00? If this semester, the total of students who get the CGPA score lower than 3.00 is 20% of the total student received, and the number of the other students who get CGPA above 3.50 is the same. Find the number of students who get CGPA between 3.00 and 3.50?
It's a Machine Learning question. Suppose we have market basket data consisting of 100 transactions and 20 items. Compute the total number of possible association rules. If the support for item A is 25%, the support for item B is 90% and the support for itemset {A,B} is 20%. Let the support and conﬁdence thresholds be 10% and 60%, respectively. Compute the conﬁdence of the association rule {A} → {B} & {B} → {A}.
Q1) -Model 1, when compared to Model 2 using a fixed number of training examples, hasa bias which is:(a) Lower(b) Higher (c) The Same- Model 1, when compared to Model 2 using a fixed number of training examples, hasa variance which is:(a) Lower (b) Higher(c) The Same- Given 100 training examples, which model is more likely to overfit the data?(a) Model 1(b) Model 2
If we evaluate a classification model(such as SVC) using the K-fold cross-validation and we set K = 9, how many times will the classification model be trained and evaluated? Group of answer choices 3 6 9 1
Question 91. Consider the following training set of m=4 training examples: x y 0.1 0.6 1 1.5 0 0.5 3 3.5 Consider the linear regression model hθ(x)=θ0+θ1x. What are the values of θ0 and θ1 that you would expect to obtain upon running gradient descent on this model? (Linear regression will be able to fit this data perfectly.)
plz run in r studio n answer all the questions biostats........ Worksheet T tests, Wilcoxon’s test, Mc Nemar’s Test 8. Choose and perform the appropriate statistical test for thefollowing two samples to determine if their populations meansdiffer significantly at a significance level of 0.05:Population Sample 1: 14, 15, 15, 15, 16, 18, 22, 23, 24, 25, 25Population Sample 2: 10, 12, 14, 15, 18, 22, 24, 27, 31, 33, 34, 34,34Before conducting any test statistics, check to see assumptions (Write upeach of the following)1. Identify your variable parameters2. State you Null and Alternate Hypotheses3. Check your data using descriptive statistics. What does the summarysay aboutyour data? Plot a box plot to visualise of the data4. Check the assumptions5. Is this a large sample n>30?6. Check whether the data follow a normal distribution by:7. Plot a Histogram or Q-Q plot to visualise the data to check for normaldistribution
Say that you have the following initial settings for binary logistic regression: x = [1, 1, 3] w = [0, -2, 0.75] b = 0.5 2. Given that x's label is 1, what is the value of w_1, w_2, and w_3 at time t + 1 if the learning rate is 1? For this problem, you may ignore the issue of updating the bias term. 3. What is the value of P(y = 1 | x) given your updated weights from the previous question? 4. Given that x's label is 1, what is the value of the bias term at time t + 1 if the learning rate is 1? 5. What is the value of P(y = 1 | x) given both your updated weights and your updated bias term? 6. Given that x's label is 0, what is the value of P(y = 0| x) at time t + 1 if the learning rate is 0.1? Round your answer to the nearest 1000th as a number [0, 1].
Melissa's null hypothesis is that the average calories in frozen Mac n Cheese is 350 (that is, the population average is 350). Since she only has one sample, she uses the bootstrap to estimate the average and ultimately decides to create a confidence interval. She also chooses an 8% p-value cutoff. What kind of confidence interval should she use? Melissa's null hypothesis is that the average calories in frozen Mac n Cheese is 350 (that is, the population average is 350). Since she only has one sample, she uses the bootstrap to estimate the average and ultimately decides to create a confidence interval. She also chooses an 8% p-value cutoff. What kind of confidence interval should she use? 95% 92% 8% Not enough information
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There is a Default dataset in library ISLR2. Perform logistic regression for predicting whether an individual will default on his or her credit card payment on the basis of annual income, balance left in credit card and student status.(Use R programming) Calculate the probability of an individual for default credit card payment if his income is $2000. (Take only one attribute “balance” for model building) Calculate the probability of an individual for default credit card payment if he is student and what is the probability for default payment if individual is not student. Use logistic regression function R that considers all attributes annual income, balance and student status.
Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the: True Positive True Negative False Positive False Negative Sensitivity (True positive rate) Specificity (True negative rate) Accuracy Precision
Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the: True Positive True Negative False Positive False Negative Sensitivity (True positive rate) Specificity (True negative rate) Accuracy Precision Draw the confusion matrix Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the…

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	Group A likely has a high outlier or right skew
	Group A is likely roughly symmetric and unimodal
	Group A likely has a low outlier or left skew
	Group A likely has low and high outliers.
	None of these