world:quiz1
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
world:quiz1 [2025/09/30 01:03] rdouc |
world:quiz1 [2025/09/30 19:02] (current) rdouc |
||
|---|---|---|---|
| Line 4: | Line 4: | ||
| We observe \((x_i,y_i)\) for \(i \in [1:n]\) where \(y_i=\beta_1 + \beta_2 x_i+\epsilon_i\) for \(i \in [1:n]\). We assume that there exists \(i\neq j\), such that \(x_i\neq x_j\) and \(\mathbb{E}[\epsilon_i]=0\) and \(\mathbb{C}\mathrm{ov}(\epsilon_i,\epsilon_j)=\sigma^2 \mathbf{1}(i \neq j)\). We use the notation: \(\bar x=\sum_{i=1}^n x_i/n\) and \(\bar y=\sum_{i=1}^n y_i/n\). | We observe \((x_i,y_i)\) for \(i \in [1:n]\) where \(y_i=\beta_1 + \beta_2 x_i+\epsilon_i\) for \(i \in [1:n]\). We assume that there exists \(i\neq j\), such that \(x_i\neq x_j\) and \(\mathbb{E}[\epsilon_i]=0\) and \(\mathbb{C}\mathrm{ov}(\epsilon_i,\epsilon_j)=\sigma^2 \mathbf{1}(i \neq j)\). We use the notation: \(\bar x=\sum_{i=1}^n x_i/n\) and \(\bar y=\sum_{i=1}^n y_i/n\). | ||
| Which of the following statements are always true: | Which of the following statements are always true: | ||
| - | |||
| - | <WRAP scroll 300px> | ||
| - | ^ Number ^ statement ^ | ||
| - | | 1 | \(\sum_{i=1}^n (x_i-\bar x)=0\) | | ||
| - | | 2 | \((\sum_{i=1}^n x_i)-\bar x=0\) | | ||
| - | | 3 | The regression coefficient estimator \(\hat \beta_2\) is given by: \(\hat \beta_2=\frac{\sum_{i=1}^n x_i(y_i-\bar y)}{\sum_{i=1}^n (x_i-\bar x)^2}\) | | ||
| - | | 4 | The regression coefficient estimator \(\hat \beta_2\) is given by: \(\hat \beta_2=\frac{(\sum_{i=1}^n x_i y_i/n) -\bar x \bar y}{\sum_{i=1}^n x_i^2/n-(\bar x)^2}\) | | ||
| - | | 5 | The regression coefficient estimator \(\hat \beta_2\) is given by: \(\hat \beta_2=\frac{(\sum_{i=1}^n x_i y_i) -\bar x \bar y}{(\sum_{i=1}^n x_i^2)-(\bar x)^2}\) | | ||
| - | | 6 | The regression line is defined by the equation \(y=\beta_1 + \beta_2 x\). | | ||
| - | | 7 | The regression line is defined by the equation \(y=\hat \beta_1 +\hat \beta_2 x\). | | ||
| - | | 8 | Assume that the regression line is \(y=5 x+4\). Assume that \(n=10\), \(\sum_{i=1}^n x_i=30\). Then, we obtain that \(\sum_{i=1}^n y_i=19\). | | ||
| - | | 9 | Assume that the regression line is \(y=5 x+4\). Assume that \(n=10\), \(\sum_{i=1}^n x_i=30\). Then, we obtain that \(\sum_{i=1}^n y_i=190\). | | ||
| - | | 10 | Assume that the regression line is \(y=5 x+4\). Assume that \(n=10\), \(\sum_{i=1}^n x_i=30\). Then, we obtain that \(\sum_{i=1}^n y_i=154\). | | ||
| - | </WRAP> | ||
| - | |||
| - | {{url>https://docs.google.com/forms/d/e/1FAIpQLSdhzll6PDvzp0Cjp62pTtYzaywzBzjrZNn6hgbihwf9S4_ReQ/viewform?embedded=true 100%,600px noborder}} | ||
| - | |||
| + | ^ Number ^ statement ^ answer ^ | ||
| + | | 1 | \(\sum_{i=1}^n (x_i-\bar x)=0\) | **TRUE** | | ||
| + | | 2 | \((\sum_{i=1}^n x_i)-\bar x=0\) | **FALSE** | | ||
| + | | 3 | The regression coefficient estimator \(\hat \beta_2\) is given by: \(\hat \beta_2=\frac{\sum_{i=1}^n x_i(y_i-\bar y)}{\sum_{i=1}^n (x_i-\bar x)^2}\) | **TRUE** | | ||
| + | | 4 | The regression coefficient estimator \(\hat \beta_2\) is given by: \(\hat \beta_2=\frac{(\sum_{i=1}^n x_i y_i/n) -\bar x \bar y}{\sum_{i=1}^n x_i^2/n-(\bar x)^2}\) | **TRUE** | | ||
| + | | 5 | The regression coefficient estimator \(\hat \beta_2\) is given by: \(\hat \beta_2=\frac{(\sum_{i=1}^n x_i y_i) -\bar x \bar y}{(\sum_{i=1}^n x_i^2)-(\bar x)^2}\) | **FALSE** | | ||
| + | | 6 | The regression line is defined by the equation \(y=\beta_1 + \beta_2 x\). | **FALSE** | | ||
| + | | 7 | The regression line is defined by the equation \(y=\hat \beta_1 +\hat \beta_2 x\). | **TRUE** | | ||
| + | | 8 | Assume that the regression line is \(y=5 x+4\). Assume that \(n=10\), \(\sum_{i=1}^n x_i=30\). Then, we obtain that \(\sum_{i=1}^n y_i=19\). | **FALSE** | | ||
| + | | 9 | Assume that the regression line is \(y=5 x+4\). Assume that \(n=10\), \(\sum_{i=1}^n x_i=30\). Then, we obtain that \(\sum_{i=1}^n y_i=190\). | **TRUE** | | ||
| + | | 10 | Assume that the regression line is \(y=5 x+4\). Assume that \(n=10\), \(\sum_{i=1}^n x_i=30\). Then, we obtain that \(\sum_{i=1}^n y_i=154\). | **FALSE** | | ||
world/quiz1.1759186991.txt.gz · Last modified: 2025/09/30 01:03 by rdouc
Page Tools
