A Simple Regression Problem (a) Find point estimates for a, B, and o?.

MATLAB: An Introduction with Applications
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**3. A Simple Regression Problem**

**(a)** Find point estimates for α, β, and σ².

In this section, students are expected to determine the point estimates for the parameters α (alpha), β (beta), and σ² (sigma squared) in the context of a simple linear regression problem. These point estimates are typically derived using statistical methods such as Ordinary Least Squares (OLS).
Transcribed Image Text:**3. A Simple Regression Problem** **(a)** Find point estimates for α, β, and σ². In this section, students are expected to determine the point estimates for the parameters α (alpha), β (beta), and σ² (sigma squared) in the context of a simple linear regression problem. These point estimates are typically derived using statistical methods such as Ordinary Least Squares (OLS).
### Data Table for Analysis

Below is a dataset presented in two columns, \( X \) and \( Y \), each containing numerical values. The dataset can be utilized for various statistical analyses such as correlation, regression, and trend analysis. 

#### Data Values:

|    **X**    |   **Y**   |
|:--------:|:--------:|
|      5      |     16.1    |
|      5      |     17.4    |
|      5      |     18.7    |
|      9      |     25.9    |
|      7      |     20.3    |
|      5      |     16.1    |
|      6      |     18.2    |
|     10    |     32.1    |
|      5      |     17.3    |
|      6      |     21.6    |
|      7      |     20.9    |
|      5      |     15.9    |
|      5      |     14.2    |
|      9      |     28.1    |
|      8      |     23.2    |
|      4      |     14.8    |
|      6      |     19.7    |
|      6      |     19.0    |
|      7      |     22.8    |
|      5      |     17.9    |
|      5      |     16.3    |
|     10    |     27.9    |
|      8      |     22.6    |
|      5      |     16.8    |
|      8      |     24.1    |

#### Interpreting the Data:

This dataset can be plotted on a graph to visually examine the relationship between the \( X \) and \( Y \) variables. For example, a scatter plot will help reveal patterns, trends, or correlations. Statistical tests can be applied to determine if the relationship between \( X \) and \( Y \) is significant. 

The repeated values for \( X \) and their corresponding \( Y \) values suggest that there may be an underlying pattern or other factors
Transcribed Image Text:### Data Table for Analysis Below is a dataset presented in two columns, \( X \) and \( Y \), each containing numerical values. The dataset can be utilized for various statistical analyses such as correlation, regression, and trend analysis. #### Data Values: | **X** | **Y** | |:--------:|:--------:| | 5 | 16.1 | | 5 | 17.4 | | 5 | 18.7 | | 9 | 25.9 | | 7 | 20.3 | | 5 | 16.1 | | 6 | 18.2 | | 10 | 32.1 | | 5 | 17.3 | | 6 | 21.6 | | 7 | 20.9 | | 5 | 15.9 | | 5 | 14.2 | | 9 | 28.1 | | 8 | 23.2 | | 4 | 14.8 | | 6 | 19.7 | | 6 | 19.0 | | 7 | 22.8 | | 5 | 17.9 | | 5 | 16.3 | | 10 | 27.9 | | 8 | 22.6 | | 5 | 16.8 | | 8 | 24.1 | #### Interpreting the Data: This dataset can be plotted on a graph to visually examine the relationship between the \( X \) and \( Y \) variables. For example, a scatter plot will help reveal patterns, trends, or correlations. Statistical tests can be applied to determine if the relationship between \( X \) and \( Y \) is significant. The repeated values for \( X \) and their corresponding \( Y \) values suggest that there may be an underlying pattern or other factors
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