Calculate the estimate of σεWhat is the estimated resting heart rate for someone who is a smoker (Smoke=1) and exercises 1 hour a day (Exercise=1)? **Study Context**An observational study was conducted where subjects were randomly sampled. Their resting heart rate, smoking status (coded as 0 for non-smoker and 1 for smoker), and average daily exercise (measured in hours) were recorded.**Objective**A linear regression model was fitted using:- Response Variable: Resting heart rate- Explanatory Variables: Smoking status, exercise duration, and an interaction term between smoking status and exercise duration.**Regression Output**- **Coefficients:** | Term | Estimate | Std. Error | t value | Pr(>|t|) | |------------------|----------|------------|---------|------------| | (Intercept) | 84.8172 | 1.9553 | 43.377 | <2e-16 *** | | Smoke | -2.2645 | 5.0665 | -0.447 | 0.6551 | | Exercise | -7.3684 | 0.8075 | -9.125 | <2e-16 *** | | Smoke:Exercise | 1.9562 | 2.5510 | 0.767 | 0.4442 |- **Significance Codes:** - "***" : p-value < 0.001 - "**" : p-value < 0.01 - "*" : p-value < 0.05 - "." : p-value < 0.1- **Model Statistics:** - Residual standard error: 8.396 on 228 degrees of freedom - Multiple R-squared: 0.2971 - Adjusted R-squared: 0.2879 - F-statistic: 32.13 on 3 and 228 DF, p-value: < 2.2e-16**Fitted Model**The linear regression model is formulated as:\[ \text{Rest}_i = \beta_0 + \beta_1 \text{Smoke}_i + \beta_2 \text{Exercise}_i + \beta_3 \text{Smoke}_i \times \text{Exercise}_i + \epsilon_i \]Where:- \(\beta_0\), \(\beta_1\), \(\beta

An observational study was conducted where subjects were randomly sampled and then had their resting heart rate recorded, as well as their smoking status (0 for non-smoker and 1 for smoker) and how much they exercise on average each day (in hours). A linear regression model is fit where we have response variable of resting heart rate and explanatory variables of smoking status (0 for non-smoker and 1 for smoker) and exercise amount per day in hours, along with an interaction between smoking status and exercise amount. The output is below: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 84.8172 1.9553 43.377 <2e-16 *** Smoke -2.2645 5.0665 -0.447 0.6551 Exercise -7.3684 0.8075 -9.125 <2e-16 *** Smoke:Exercise 1.9562 2.5510 0.767 0.4442 Signif. codes: O ***> 0.001 (*** 0.01 (*) 0.05 . 0.1 1 Residual standard error: 8.396 on 228 degrees of freedom Adjusted R-squared: 0.2879 F-statistic: 32.13 on 3 and 228 DF, p-value: < 2.2e-16 Multiple R-squared: 0.2971, The model that was fit is: Rest; = Bo + B1 Smoke; + B2Exercise; + B3Smoke; * Exercise; + E;

An observational study was conducted where subjects were randomly sampled and then had their resting heart rate recorded, as well as their smoking status (0 for non-smoker and 1 for smoker) and how much they exercise on average each day (in hours). A linear regression model is fit where we have response variable of resting heart rate and explanatory variables of smoking status (0 for non-smoker and 1 for smoker) and exercise amount per day in hours, along with an interaction between smoking status and exercise amount. The output is below: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 84.8172 1.9553 43.377 <2e-16 * Smoke -2.2645 5.0665 -0.447 0.6551 Exercise -7.3684 0.8075 -9.125 <2e-16 * Smoke:Exercise 1.9562 2.5510 0.767 0.4442 Signif. codes: O > 0.001 ( 0.01 () 0.05 . 0.1 1 Residual standard error: 8.396 on 228 degrees of freedom Adjusted R-squared: 0.2879 F-statistic: 32.13 on 3 and 228 DF, p-value: < 2.2e-16 Multiple R-squared: 0.2971, The model that was fit is: Rest; = Bo + B1 Smoke; + B2Exercise; + B3Smoke; Exercise; + E;

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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