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Since elderly people may have difficulty standing straight, a study aims to predict overall height from height to the knee. Here are data (in centimeters, cm) for five elderly men. Knee Height (cm) 57.7 47.4 43.5 44.8 Overall Height(cm) 192.1 153.3 146.4 162.7 Let's assume a regression model Overall height = Bo + B₁ x Knee height + 1. Which R command should you use to fit the model if you have defined variables as knee = c(57.7, 47.4, 43.5, 44.8, 55.2) overall = c(192.1, 153.3, 146.4, 162.7, 169.1) 55.2 169.1 (a) 1m (knee ~ overall) (b) lm (overall ~ knee) (c) t.test(overall, knee) (d) t.test (knee, overall)

Since elderly people may have difficulty standing straight, a study aims to predict overall height from height to the knee. Here are data (in centimeters, cm) for five elderly men. Knee Height (cm) 57.7 47.4 43.5 44.8 Overall Height(cm) 192.1 153.3 146.4 162.7 Let's assume a regression model Overall height = Bo + B₁ x Knee height + 1. Which R command should you use to fit the model if you have defined variables as knee = c(57.7, 47.4, 43.5, 44.8, 55.2) overall = c(192.1, 153.3, 146.4, 162.7, 169.1) 55.2 169.1 (a) 1m (knee ~ overall) (b) lm (overall ~ knee) (c) t.test(overall, knee) (d) t.test (knee, overall)

MATLAB: An Introduction with Applications
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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Please solve both subparts of the question. Part 1 is one image and part 2 is the other one. Thanks!

Since elderly people may have difficulty standing straight, a study aims to predict overall height from height to the knee. Here are data (in centimeters, cm) for five elderly men. Knee Height (cm) 57.7 47.4 43.5 44.8 Overall Height (cm) 192.1 192.1 153.3 146.4 162.7 Let's assume a regression model Overall height = Bo + B₁ x Knee height + e 1. Which R command should you use to fit the model if you have defined variables as I knee = c(57.7, 47.4, 43.5, 44.8, 55.2) overall = c(192.1, 153.3, 146.4, 162.7, 169.1) (a) 1m (knee overall) (b) lm (overall ~ knee) (c) t.test(overall, knee) (d) t.test (knee, overall) 55.2 169.1 ~
Transcribed Image Text:Since elderly people may have difficulty standing straight, a study aims to predict overall height from height to the knee. Here are data (in centimeters, cm) for five elderly men. Knee Height (cm) 57.7 47.4 43.5 44.8 Overall Height (cm) 192.1 192.1 153.3 146.4 162.7 Let's assume a regression model Overall height = Bo + B₁ x Knee height + e 1. Which R command should you use to fit the model if you have defined variables as I knee = c(57.7, 47.4, 43.5, 44.8, 55.2) overall = c(192.1, 153.3, 146.4, 162.7, 169.1) (a) 1m (knee overall) (b) lm (overall ~ knee) (c) t.test(overall, knee) (d) t.test (knee, overall) 55.2 169.1 ~
2. Test the significance of explanatory variable. (a) There is no evidence that the explanatory variable is significant (p-value 20.1) (b) There is some evidence that the explanatory variable is significant (0.05 ≤ p-value < 0.1) I (c) There is strong evidence that the explanatory variable is signifi- cant (0.01 ≤ p-value < 0.05) (d) There is very strong evidence that the explanatory variable is significant (0.001 ≤ p-value < 0.01)
Transcribed Image Text:2. Test the significance of explanatory variable. (a) There is no evidence that the explanatory variable is significant (p-value 20.1) (b) There is some evidence that the explanatory variable is significant (0.05 ≤ p-value < 0.1) I (c) There is strong evidence that the explanatory variable is signifi- cant (0.01 ≤ p-value < 0.05) (d) There is very strong evidence that the explanatory variable is significant (0.001 ≤ p-value < 0.01)
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