Zusammenfassung der Ressource
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S_x ̅ =s⁄√n is an estimate of:
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The 68-95-99.7 rule indicates that for a normal distribution N(,o):
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68% of the observations fall within 1 o of the mean
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95% of the observations fall within 1 o of the mean
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99.7% of the observations fall within 1 o of the mean
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none of the above
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The Law of the large numbers implies that
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As the simple size increases will change being closer to x ̅
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As the simple size increases the difference between the parameter and the statistic will increase
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As the simple size increases the simple proportion p ̂ will be closer to the population proportion p
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All of the above
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When estimating the populational mean, as we increase the simple size
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The simple distribution becomes more concentrated (less disperse)
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The standard error decreases
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The accuracy of the inference increases
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All of the above
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Suppose we toss a fair coin 100 times. Knowing the true population of heads is 0,5 find out the 2 decimal approximation of the probability that the sample proportion of heads is between 0.4 and 0.6:
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0.05
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0.95
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0.5
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none of the above
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for a fixed simple size and standard error a confidence interval with a 99% confidence level is:
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wider than a 95% confidence level interval
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narrower than a 95% confidence level interval
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of the same width than a 95% confidence level interval
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wider or narrower depending on the standardized statistic (in relation to the parameter that is estimated)
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the F statistic in an ANOVA used to test an average differences always increases when:
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the within group variability increases
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the between group variability increases
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the total variability increases
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all of the above
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We are willing to conduct a paired-samples two tailed test on the difference between the populational means of x and y. The simple mean of x is 9 and the sample mean of y is 10; the simple size is 40. The variance of the difference in sample values is s_d^2=16. What is the conclusion of our test?
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We reject the null hypothesis, the populational means are significantly different
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We fail to reject the null hypothesis, the populational means are not significantly different
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We accept the null hypothesis, the populational means are the same
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We reject the null hypothesis, the populational means are not significantly different
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When the correlation coefficient between two variables x and y is larger
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The estimate for the slope of the regression line has a larger value (even when the slope is negative)
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The determination coefficient is also larger (even when the correlation is negative)
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The determination coefficient is smaller (even when the correlation is positive)
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None of the above
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If we find the following confidence interval for the slope of a regression β∁ (-0.5,1.5) at a 95% confidence level:
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The simple slope β ̂ is 0.5
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There is no significant linear association between x and y at a 5% significance level
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The simple correlation coefficient r is positive
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All of the above
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When the determination coefficient equal 1:
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There’s a perfect linear association between x and y
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When y increases x increases
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When y increases x decreases
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None of the above
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Knowing that for a linear regression model Σ(