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Objective: Analyze the temporal dynamics between Income and HealthScore to identify trends, seasonal patterns, and potential predictive relationships. Assumption: Assume that the dataset includes multiple records per participant over different time periods (e.g., monthly, quarterly), with an additional variable: • TimePeriod: Indicates the time point of the record (e.g., January 2020, February 2020, ..., December 2023). Tasks: 1. Data Structuring: Restructure the statsnew.csv dataset to create time series objects for each participant, capturing Income and HealthScore across TimePeriod S. • Handle any missing time points or irregular intervals. 2. Exploratory Time Series Analysis: • . Plot the time series for Income and HealthScore for a representative sample of participants. Decompose the time series to identify trend, seasonal, and residual components using STL decomposition. 3. Cross-Correlation Analysis: Compute the cross-correlation function (CCF) between Income and HealthScore to identify any leading or lagging relationships. . Interpret the significance of the correlations at different lags. 4. Predictive Modeling: • Develop Vector Autoregression (VAR) models to capture the interdependencies between Income and HealthScore over time. . Determine the appropriate order of the VAR model using criteria like AIC or BIC. • Forecast future HealthScore based on projected Income and vice versa. 5. Causality Testing: Perform Granger Causality tests to examine whether Income can predict HealthScore and/or if HealthScore can predict Income.

Objective: Analyze the temporal dynamics between Income and HealthScore to identify trends, seasonal patterns, and potential predictive relationships. Assumption: Assume that the dataset includes multiple records per participant over different time periods (e.g., monthly, quarterly), with an additional variable: • TimePeriod: Indicates the time point of the record (e.g., January 2020, February 2020, ..., December 2023). Tasks: 1. Data Structuring: Restructure the statsnew.csv dataset to create time series objects for each participant, capturing Income and HealthScore across TimePeriod S. • Handle any missing time points or irregular intervals. 2. Exploratory Time Series Analysis: • . Plot the time series for Income and HealthScore for a representative sample of participants. Decompose the time series to identify trend, seasonal, and residual components using STL decomposition. 3. Cross-Correlation Analysis: Compute the cross-correlation function (CCF) between Income and HealthScore to identify any leading or lagging relationships. . Interpret the significance of the correlations at different lags. 4. Predictive Modeling: • Develop Vector Autoregression (VAR) models to capture the interdependencies between Income and HealthScore over time. . Determine the appropriate order of the VAR model using criteria like AIC or BIC. • Forecast future HealthScore based on projected Income and vice versa. 5. Causality Testing: Perform Granger Causality tests to examine whether Income can predict HealthScore and/or if HealthScore can predict Income.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.1: Measures Of Center
Problem 3BGP
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These question need to be solved using R with the given data, please do not provide AI solution , also i need detailed solution , do everything in detail which is required, answer it as soon as possible.

Objective: Analyze the temporal dynamics between Income and HealthScore to identify trends, seasonal patterns, and potential predictive relationships. Assumption: Assume that the dataset includes multiple records per participant over different time periods (e.g., monthly, quarterly), with an additional variable: • TimePeriod: Indicates the time point of the record (e.g., January 2020, February 2020, ..., December 2023). Tasks: 1. Data Structuring: Restructure the statsnew.csv dataset to create time series objects for each participant, capturing Income and HealthScore across TimePeriod S. • Handle any missing time points or irregular intervals. 2. Exploratory Time Series Analysis: • . Plot the time series for Income and HealthScore for a representative sample of participants. Decompose the time series to identify trend, seasonal, and residual components using STL decomposition. 3. Cross-Correlation Analysis: Compute the cross-correlation function (CCF) between Income and HealthScore to identify any leading or lagging relationships. . Interpret the significance of the correlations at different lags. 4. Predictive Modeling: • Develop Vector Autoregression (VAR) models to capture the interdependencies between Income and HealthScore over time. . Determine the appropriate order of the VAR model using criteria like AIC or BIC. • Forecast future HealthScore based on projected Income and vice versa. 5. Causality Testing: Perform Granger Causality tests to examine whether Income can predict HealthScore and/or if HealthScore can predict Income.
Transcribed Image Text:Objective: Analyze the temporal dynamics between Income and HealthScore to identify trends, seasonal patterns, and potential predictive relationships. Assumption: Assume that the dataset includes multiple records per participant over different time periods (e.g., monthly, quarterly), with an additional variable: • TimePeriod: Indicates the time point of the record (e.g., January 2020, February 2020, ..., December 2023). Tasks: 1. Data Structuring: Restructure the statsnew.csv dataset to create time series objects for each participant, capturing Income and HealthScore across TimePeriod S. • Handle any missing time points or irregular intervals. 2. Exploratory Time Series Analysis: • . Plot the time series for Income and HealthScore for a representative sample of participants. Decompose the time series to identify trend, seasonal, and residual components using STL decomposition. 3. Cross-Correlation Analysis: Compute the cross-correlation function (CCF) between Income and HealthScore to identify any leading or lagging relationships. . Interpret the significance of the correlations at different lags. 4. Predictive Modeling: • Develop Vector Autoregression (VAR) models to capture the interdependencies between Income and HealthScore over time. . Determine the appropriate order of the VAR model using criteria like AIC or BIC. • Forecast future HealthScore based on projected Income and vice versa. 5. Causality Testing: Perform Granger Causality tests to examine whether Income can predict HealthScore and/or if HealthScore can predict Income.
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