MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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**Principal Component Analysis Exercise**

1. **Compute the First Principal Component Score for Observation 1**
   - *Instruction*: Round intermediate calculations to at least 4 decimal places and your final answer to 4 decimal places. Negative value should be indicated by a minus sign.
   - Formula Box: \(PC_{1,1}\) [Input box]

2. **Compute the Second Principal Component Score for Observation 2**
   - *Instruction*: Round intermediate calculations to at least 4 decimal places and your final answer to 4 decimal places. Negative value should be indicated by a minus sign.
   - Formula Box: \(PC_{2,2}\) [Input box]

3. **Compute the Third Principal Component Score for Observation 3**
   - *Instruction*: Round intermediate calculations to at least 4 decimal places and your final answer to 4 decimal places. Negative value should be indicated by a minus sign.
   - Formula Box: [Input box]
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Transcribed Image Text:**Principal Component Analysis Exercise** 1. **Compute the First Principal Component Score for Observation 1** - *Instruction*: Round intermediate calculations to at least 4 decimal places and your final answer to 4 decimal places. Negative value should be indicated by a minus sign. - Formula Box: \(PC_{1,1}\) [Input box] 2. **Compute the Second Principal Component Score for Observation 2** - *Instruction*: Round intermediate calculations to at least 4 decimal places and your final answer to 4 decimal places. Negative value should be indicated by a minus sign. - Formula Box: \(PC_{2,2}\) [Input box] 3. **Compute the Third Principal Component Score for Observation 3** - *Instruction*: Round intermediate calculations to at least 4 decimal places and your final answer to 4 decimal places. Negative value should be indicated by a minus sign. - Formula Box: [Input box]
The following table displays the weights for computing the principal components and the standardized data (z-scores) for three observations.

| Weight | \(PC_1\) | \(PC_2\) | \(PC_3\) |
|--------|--------|--------|--------|
| \(x_1\) | -0.65  | 0.69   | 0.60   |
| \(x_2\) | -0.70  | 0.12   | -0.84  |
| \(x_3\) | 0.72   | 0.86   | -0.33  |

---

|        | \(Z_1\) | \(Z_2\) | \(Z_3\) |
|--------|--------|--------|--------|
| Observation 1 | 0.82   | 0.40   | -0.31  |
| Observation 2 | 2.81   | 2.61   | -1.22  |
| Observation 3 | -0.89  | -1.12  | 1.39   |

The first table contains the weights assigned to each variable (\(x_1, x_2, x_3\)) for the computation of the principal components (\(PC_1, PC_2, PC_3\)). 

The second table shows the standardized data or z-scores (\(Z_1, Z_2, Z_3\)) for three observations. Each cell represents the z-score of a particular observation for the corresponding principal component.
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Transcribed Image Text:The following table displays the weights for computing the principal components and the standardized data (z-scores) for three observations. | Weight | \(PC_1\) | \(PC_2\) | \(PC_3\) | |--------|--------|--------|--------| | \(x_1\) | -0.65 | 0.69 | 0.60 | | \(x_2\) | -0.70 | 0.12 | -0.84 | | \(x_3\) | 0.72 | 0.86 | -0.33 | --- | | \(Z_1\) | \(Z_2\) | \(Z_3\) | |--------|--------|--------|--------| | Observation 1 | 0.82 | 0.40 | -0.31 | | Observation 2 | 2.81 | 2.61 | -1.22 | | Observation 3 | -0.89 | -1.12 | 1.39 | The first table contains the weights assigned to each variable (\(x_1, x_2, x_3\)) for the computation of the principal components (\(PC_1, PC_2, PC_3\)). The second table shows the standardized data or z-scores (\(Z_1, Z_2, Z_3\)) for three observations. Each cell represents the z-score of a particular observation for the corresponding principal component.
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