world:quiz1
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world:quiz1 [2025/09/30 17:24] francois_bertholom ↷ Page moved from emines2024:quiz1 to world:quiz1 |
world:quiz1 [2025/09/30 19:02] (current) rdouc |
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| - | ^ Number ^ statement ^ | + | ^ Number ^ statement ^ answer ^ |
| - | | 1 | \(\sum_{i=1}^n (x_i-\bar x)=0\) | | + | | 1 | \(\sum_{i=1}^n (x_i-\bar x)=0\) | **TRUE** | |
| - | | 2 | \((\sum_{i=1}^n x_i)-\bar x=0\) | | + | | 2 | \((\sum_{i=1}^n x_i)-\bar x=0\) | **FALSE** | |
| - | | 3 | The regression coefficient estimator \(\hat \beta_2\) is given by: \(\hat \beta_2=\frac{\sum_{i=1}^n x_i(y_i-\bar y)}{\sum_{i=1}^n (x_i-\bar x)^2}\) | | + | | 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: \(\hat \beta_2=\frac{(\sum_{i=1}^n x_i y_i/n) -\bar x \bar y}{\sum_{i=1}^n x_i^2/n-(\bar x)^2}\) | | + | | 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: \(\hat \beta_2=\frac{(\sum_{i=1}^n x_i y_i) -\bar x \bar y}{(\sum_{i=1}^n x_i^2)-(\bar x)^2}\) | | + | | 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 \(y=\beta_1 + \beta_2 x\). | | + | | 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 \(y=\hat \beta_1 +\hat \beta_2 x\). | | + | | 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 \(\sum_{i=1}^n y_i=19\). | | + | | 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 \(\sum_{i=1}^n y_i=190\). | | + | | 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 \(\sum_{i=1}^n y_i=154\). | | + | | 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** | |
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| - | {{url>https://docs.google.com/forms/d/e/1FAIpQLSdhzll6PDvzp0Cjp62pTtYzaywzBzjrZNn6hgbihwf9S4_ReQ/viewform?embedded=true 100%,600px noborder}} | + | |
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world/quiz1.1759245879.txt.gz · Last modified: 2025/09/30 17:24 by francois_bertholom
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