Part 1. The following data represent a random sample of 5 observations. Assume that Y is a linear function of X and a normally distributed disturbance term with a mean of zero and a constant variance. In other words: Y = a + bX + u, u~ N(0,0²) Observation 1 17

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## Part 1. 
The following data represents a random sample of 5 observations. Assume that Y is a linear function of X and a normally distributed disturbance term with a mean of zero and a constant variance. In other words: \( Y = a + bX + u, \text{ where } u \sim N(0, \sigma^2) \).

| Observation | Y | X  |
|-------------|---|----|
| 1.          | 1 | 17 |
| 2.          | 3 | 13 |
| 3.          | 5 | 8  |
| 4.          | 7 | 10 |
| 5.          | 9 | 2  |

Create a scatterplot of this dataset (either manually, or by using Excel graphing functions). Calculate the sample means, variances, and standard deviations of the variables \( X \) and \( Y \) either “by hand” or by using Excel and write one sentence describing what each of these means in terms of the dataset.

### Calculations: 
The calculations below should be done by applying the bivariate regression formulas. In other words, do them “by hand” using a calculator or in Excel in a manner that I can clearly see the formulas you have used. You should also check your calculations by entering the data into an Excel sheet and using the regression analysis function under Data Tools.

1. **Calculate the standard errors for \( a \) and \( b \) (\( s_{a} \) and \( s_{b} \) ). Discuss the interpretation of each of them.**
   
2. **Compute the confidence interval (alpha = 0.05) and the t-statistic for \( b \). Again, explain the meaning of each of them in a sentence.**
   
3. **Calculate the \( R^2 \) for the regression and describe its meaning.**
   
4. **Graph the data points and the sample regression function. Identify the slope and the intercept term. Label the data points and graphically indicate the residual for each observation. Add a vertical dashed line for the mean of \( X \) and the horizontal dashed line for the mean of \( Y \). Does the regression function pass through the intersection of these two lines?**
Transcribed Image Text:## Part 1. The following data represents a random sample of 5 observations. Assume that Y is a linear function of X and a normally distributed disturbance term with a mean of zero and a constant variance. In other words: \( Y = a + bX + u, \text{ where } u \sim N(0, \sigma^2) \). | Observation | Y | X | |-------------|---|----| | 1. | 1 | 17 | | 2. | 3 | 13 | | 3. | 5 | 8 | | 4. | 7 | 10 | | 5. | 9 | 2 | Create a scatterplot of this dataset (either manually, or by using Excel graphing functions). Calculate the sample means, variances, and standard deviations of the variables \( X \) and \( Y \) either “by hand” or by using Excel and write one sentence describing what each of these means in terms of the dataset. ### Calculations: The calculations below should be done by applying the bivariate regression formulas. In other words, do them “by hand” using a calculator or in Excel in a manner that I can clearly see the formulas you have used. You should also check your calculations by entering the data into an Excel sheet and using the regression analysis function under Data Tools. 1. **Calculate the standard errors for \( a \) and \( b \) (\( s_{a} \) and \( s_{b} \) ). Discuss the interpretation of each of them.** 2. **Compute the confidence interval (alpha = 0.05) and the t-statistic for \( b \). Again, explain the meaning of each of them in a sentence.** 3. **Calculate the \( R^2 \) for the regression and describe its meaning.** 4. **Graph the data points and the sample regression function. Identify the slope and the intercept term. Label the data points and graphically indicate the residual for each observation. Add a vertical dashed line for the mean of \( X \) and the horizontal dashed line for the mean of \( Y \). Does the regression function pass through the intersection of these two lines?**
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