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A Research Team Wanted to Assess the Relationship Between Age

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A research team wanted to assess the relationship between age, systolic blood pressure, smoking, and risk of stroke. A sample of 150 patients who had a stroke is selected and the data collected are given below. Here, for the variable Smoker, 1 represents smokers and 0 represents nonsmokers.
A research team wanted to assess the relationship between age, systolic blood pressure, smoking, and risk of stroke. A sample of 150 patients who had a stroke is selected and the data collected are given below. Here, for the variable Smoker, 1 represents smokers and 0 represents nonsmokers.                 Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the Risk of stroke using k-nearest neighbors with up to k = 20. Use Risk as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data.  a. What value of k minimizes the root mean squared error (RMSE) on the validation data? b. What is the RMSE on the validation data and test data? c. What is the average error on the validation data and test data? What does this suggest?
A research team wanted to assess the relationship between age, systolic blood pressure, smoking, and risk of stroke. A sample of 150 patients who had a stroke is selected and the data collected are given below. Here, for the variable Smoker, 1 represents smokers and 0 represents nonsmokers.                 Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the Risk of stroke using k-nearest neighbors with up to k = 20. Use Risk as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data.  a. What value of k minimizes the root mean squared error (RMSE) on the validation data? b. What is the RMSE on the validation data and test data? c. What is the average error on the validation data and test data? What does this suggest?
A research team wanted to assess the relationship between age, systolic blood pressure, smoking, and risk of stroke. A sample of 150 patients who had a stroke is selected and the data collected are given below. Here, for the variable Smoker, 1 represents smokers and 0 represents nonsmokers.                 Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the Risk of stroke using k-nearest neighbors with up to k = 20. Use Risk as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data.  a. What value of k minimizes the root mean squared error (RMSE) on the validation data? b. What is the RMSE on the validation data and test data? c. What is the average error on the validation data and test data? What does this suggest?
A research team wanted to assess the relationship between age, systolic blood pressure, smoking, and risk of stroke. A sample of 150 patients who had a stroke is selected and the data collected are given below. Here, for the variable Smoker, 1 represents smokers and 0 represents nonsmokers.                 Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the Risk of stroke using k-nearest neighbors with up to k = 20. Use Risk as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data.  a. What value of k minimizes the root mean squared error (RMSE) on the validation data? b. What is the RMSE on the validation data and test data? c. What is the average error on the validation data and test data? What does this suggest?
Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the Risk of stroke using k-nearest neighbors with up to k = 20. Use Risk as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data.
a. What value of k minimizes the root mean squared error (RMSE) on the validation data?
b. What is the RMSE on the validation data and test data?
c. What is the average error on the validation data and test data? What does this suggest?


Definitions:

Autopsy

A post-mortem examination to discover the cause of death or the extent of disease.

Research Design

A structured plan that outlines how a study will be conducted to investigate a particular problem or hypothesis.

Quasi-Experiment

An empirical study that appears to involve some, but incomplete, experimental control (e.g., through nonrandom assignment of participants to conditions).

Clinical Interview

A research paradigm in which an investigator begins by asking participants a series of open-ended questions but follows up on the responses with specific questions that have been prepared in advance.

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