Hick's law states that the time I for a person to choose between n choices is given by the equation: data for a particular person: T = blog(n+1) where b is a constant estimated from data. The table shows
Hick's law states that the time I for a person to choose between n choices is given by the equation: data for a particular person: T = blog(n+1) where b is a constant estimated from data. The table shows
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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![**Understanding Hick's Law through Nonlinear Regression**
Hick's Law states that the time \(T\) for a person to choose between \(n\) choices is given by the equation:
\[T = b \log(n+1)\]
where \(b\) is a constant estimated from data. The table below shows the data for a particular person:
| n (choices) | 2 | 4 | 8 | 16 | 32 | 64 |
|-------------|------|-----|-----|-----|-----|-----|
| T (seconds) | 2.51 | 3.9 | 5.57 | 7.49 | 8.44 | 10.83 |
Using nonlinear regression, it was found that \(b \approx 5.830\).
**b) Finding the Residuals for Each Point**
To evaluate the fit of the nonlinear regression model, we compute the residuals for each data point:
| n (choices) | 2 | 4 | 8 | 16 | 32 | 64 |
|-------------|------|-----|-----|-----|-----|-----|
| T (seconds) | 2.51 | 3.9 | 5.57 | 7.49 | 8.44 | 10.83 |
| residuals | | | | | | |
*(Note: Residuals must be calculated using the formula \( \text{residual} = T_{\text{observed}} - T_{\text{predicted}} \).)*
**c) Calculating the Sum of Squares of the Residuals (\(SS_{\text{res}}\))**
The sum of squares of the residuals measures how well the model fits the data. It is calculated using the residuals obtained from the model predictions.
**d) Commenting on Model Fit**
The fit of the model can be assessed by examining the magnitude of the residuals and the \(SS_{\text{res}}\). A lower \(SS_{\text{res}}\) implies a better fit, indicating that the model's predictions are closer to the observed values. Further statistical tests or graphical analyses may be necessary for a comprehensive evaluation.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fdff7b543-7c53-4567-b2e2-82ba64a4a215%2F0985c8ab-eae4-45b6-8d85-689a67e2874d%2F82h78cd_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Understanding Hick's Law through Nonlinear Regression**
Hick's Law states that the time \(T\) for a person to choose between \(n\) choices is given by the equation:
\[T = b \log(n+1)\]
where \(b\) is a constant estimated from data. The table below shows the data for a particular person:
| n (choices) | 2 | 4 | 8 | 16 | 32 | 64 |
|-------------|------|-----|-----|-----|-----|-----|
| T (seconds) | 2.51 | 3.9 | 5.57 | 7.49 | 8.44 | 10.83 |
Using nonlinear regression, it was found that \(b \approx 5.830\).
**b) Finding the Residuals for Each Point**
To evaluate the fit of the nonlinear regression model, we compute the residuals for each data point:
| n (choices) | 2 | 4 | 8 | 16 | 32 | 64 |
|-------------|------|-----|-----|-----|-----|-----|
| T (seconds) | 2.51 | 3.9 | 5.57 | 7.49 | 8.44 | 10.83 |
| residuals | | | | | | |
*(Note: Residuals must be calculated using the formula \( \text{residual} = T_{\text{observed}} - T_{\text{predicted}} \).)*
**c) Calculating the Sum of Squares of the Residuals (\(SS_{\text{res}}\))**
The sum of squares of the residuals measures how well the model fits the data. It is calculated using the residuals obtained from the model predictions.
**d) Commenting on Model Fit**
The fit of the model can be assessed by examining the magnitude of the residuals and the \(SS_{\text{res}}\). A lower \(SS_{\text{res}}\) implies a better fit, indicating that the model's predictions are closer to the observed values. Further statistical tests or graphical analyses may be necessary for a comprehensive evaluation.
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