1. The boxplot shows the median and the quartile range of the data. 2. If we...
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1. The boxplot shows the median and the quartile range of the data. 2. If we double the sample size from n=50 to n=100, the length of the confidence interval is reduced by half. 3. If zero lies inside the 95% confidence interval for u, then zero is also inside the 99% confidence interval. 4. If data are lefi skewed, the mean is larger than the median. 5. If the standard two-sample t-test rejects H0: ul- p2 <$100, then we know for sure that ul is more than $100 larger than µ2. 6. If the correlation between the explanatory (independent) variable and the response(dependent) variable is zero, then the slope of the fitted line will also be zero. 7. You find that the correlation between x and y is 1, this indicates a perfect association between x and y. 8. If the correlation between x and y is larger than 0.5, then a linear equation is appropriate to describe the association. 9. To estimate the effect of advertising on sales using regression, you should look for periods with steady levels of advertising rather than periods in which advertising varies. 10. Doubling the sample size used to fit a regression can be expected to reduce the standard error of the slope by about 30%. 1. The boxplot shows the median and the quartile range of the data. 2. If we double the sample size from n=50 to n=100, the length of the confidence interval is reduced by half. 3. If zero lies inside the 95% confidence interval for u, then zero is also inside the 99% confidence interval. 4. If data are lefi skewed, the mean is larger than the median. 5. If the standard two-sample t-test rejects H0: ul- p2 <$100, then we know for sure that ul is more than $100 larger than µ2. 6. If the correlation between the explanatory (independent) variable and the response(dependent) variable is zero, then the slope of the fitted line will also be zero. 7. You find that the correlation between x and y is 1, this indicates a perfect association between x and y. 8. If the correlation between x and y is larger than 0.5, then a linear equation is appropriate to describe the association. 9. To estimate the effect of advertising on sales using regression, you should look for periods with steady levels of advertising rather than periods in which advertising varies. 10. Doubling the sample size used to fit a regression can be expected to reduce the standard error of the slope by about 30%.
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