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2. Test the significance of explanatory variable. (a) There is no evidence that the explanatory variable is significant (p-value > 0.1) (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)

2. Test the significance of explanatory variable. (a) There is no evidence that the explanatory variable is significant (p-value > 0.1) (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)

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 of the attached subparts of the problem. They are all from one question that is above question 1. Refer to that part for data. Thanks!

**Testing the Significance of an Explanatory Variable** 1. **Understanding p-values for Significance Testing** - **(a)** There is no evidence that the explanatory variable is significant \ \( \text{(p-value} \geq 0.1) \) - **(b)** There is some evidence that the explanatory variable is significant \ \( (0.05 \leq \text{p-value} < 0.1) \) - **(c)** There is strong evidence that the explanatory variable is significant \ \( (0.01 \leq \text{p-value} < 0.05) \) - **(d)** There is very strong evidence that the explanatory variable is significant \ \( (0.001 \leq \text{p-value} < 0.01) \) The text outlines different levels of evidence for the significance of an explanatory variable based on the p-value obtained from statistical testing. Each level of p-value corresponds to the strength of evidence against the null hypothesis.
Transcribed Image Text:**Testing the Significance of an Explanatory Variable** 1. **Understanding p-values for Significance Testing** - **(a)** There is no evidence that the explanatory variable is significant \ \( \text{(p-value} \geq 0.1) \) - **(b)** There is some evidence that the explanatory variable is significant \ \( (0.05 \leq \text{p-value} < 0.1) \) - **(c)** There is strong evidence that the explanatory variable is significant \ \( (0.01 \leq \text{p-value} < 0.05) \) - **(d)** There is very strong evidence that the explanatory variable is significant \ \( (0.001 \leq \text{p-value} < 0.01) \) The text outlines different levels of evidence for the significance of an explanatory variable based on the p-value obtained from statistical testing. Each level of p-value corresponds to the strength of evidence against the null hypothesis.
**Text Transcription for Educational Website** 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 | 55.2 | |-------------------|------|------|------|------|------| | Overall Height (cm)| 192.1 | 153.3 | 146.4 | 162.7 | 169.1 | Let's assume a regression model: \[ \text{Overall height} = \beta_0 + \beta_1 \times \text{Knee height} + \epsilon \] 1. **Which R command should you use to fit the model if you have defined variables as:** ```r knee = c(57.7, 47.4, 43.5, 44.8, 55.2) overall = c(192.1, 153.3, 146.4, 162.7, 169.1) ``` **Explanation:** The table provides measurements of knee height and overall height for a sample of five elderly men. The task involves using these measurements to predict overall height based on knee height by applying a regression model. The proposed regression model is a linear equation with coefficients \(\beta_0\) and \(\beta_1\), and \(\epsilon\) representing the error term. The R command needed would typically be used in a statistical analysis to fit this linear regression model using the provided data.
Transcribed Image Text:**Text Transcription for Educational Website** 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 | 55.2 | |-------------------|------|------|------|------|------| | Overall Height (cm)| 192.1 | 153.3 | 146.4 | 162.7 | 169.1 | Let's assume a regression model: \[ \text{Overall height} = \beta_0 + \beta_1 \times \text{Knee height} + \epsilon \] 1. **Which R command should you use to fit the model if you have defined variables as:** ```r knee = c(57.7, 47.4, 43.5, 44.8, 55.2) overall = c(192.1, 153.3, 146.4, 162.7, 169.1) ``` **Explanation:** The table provides measurements of knee height and overall height for a sample of five elderly men. The task involves using these measurements to predict overall height based on knee height by applying a regression model. The proposed regression model is a linear equation with coefficients \(\beta_0\) and \(\beta_1\), and \(\epsilon\) representing the error term. The R command needed would typically be used in a statistical analysis to fit this linear regression model using the provided data.
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