when estimating population parameters, a point estimate is:
a characteristic of all confidence intervals is that:
if we wish to decrease the width (increase the precision) of a confidence interval, we would:
we wish to develop a confidence interval for the population mean. the population standard deviation is known. we have a sample of 40 observations. we decide to use the 92 percent level of confidence. the appropriate value of z is:
we wish to develop a confidence interval for the population mean. the population follows the normal distribution, the standard deviation of the population is 3, and we have a sample of 10 observations. we decide to use the 90 percent level of confidence. the appropriate statistic for the level of confidence is:
the fraction of a sample possessing a certain trait is called a:
to develop a confidence interval for a proportion:
we wish to develop a confidence interval for the population mean. the population follows the normal distribution and the population standard deviation is 3. we collect a random sample of 10 observations. we decide to use the 90 percent level of confidence. the margin of error for the confidence interval is:
we wish to develop a confidence interval for the population mean. the population follows the normal distribution. we collect a random sample of 10 observations and the sample standard deviation is 3. we decide to use the 95 percent level of confidence. the margin of error for the confidence interval is:
which of the following statements is a characteristic of the t distribution?
when we do the one sample of hypothesis test for the population mean but we don't know the population variation, what statistics we can choose to use?
for a null hypothesis statement, it always includes:
which of the following statements is true about the alternate hypothesis?
when testing a null hypothesis, a critical value is:
in a one-tailed hypothesis test:
to use a z statistic in a one-sample hypothesis test of a mean, we need to know:
a p-value is equal to the:
a type ii error occurs when we:
which of the following statements is true about the level of significance?
which of the following statements are correct when deciding whether to use the z or the t distribution?
in a hypothesis test comparing two population means, the "=" sign always appears in the:
in a hypothesis test comparing two population means, we use the z distribution when:
for the hypothesis, h0: µ1 ≤ µ2, a random sample of 10 observations is selected from the first normal population and 8 from the second normal population. what is the number of degrees of freedom?
for the hypothesis, h0: µ1 ≤ µ2, (.01 significance level), a random sample of 10 observations is selected from the first normal population and 8 from the second normal population. population standard deviations are unknown. what is (are) the critical value(s)?
when testing a hypothesis about the means for two independent populations (population standard deviations unknown), what should be true?
to conduct a test of means for two independent populations, which of the following is required?
another way to state the null hypothesis: h0: µ1 = µ2, is:
to conduct a test of hypothesis for dependent populations, we assume that:
when conducting a test of hypothesis for dependent samples:
which of the following is necessary to determine a p-value?
the shape of a f-distribution is:
in a one-way anova, the f test statistic is the ratio of the:
which of the following is a characteristic of the f distribution?
in an anova table, the term "treatment" refers to:
suppose we want to test the effect of three treatments. we randomly assign 6 observations to each treatment. for an anova, the treatment and error degrees of freedom are:
the term mse is:
anova requires that the:
when will the computed value of f be negative?
economic theory: qualitative results while econometrics: quantitative results
we choose all the rules for how our data are created. the underlying rules are the “data generating process” (dgp).we choose to use the gauss-markov assumptions. what are the followings are not necessery asumption?
which assumption of the sampling is matter to the simulation?
why we prefer the mean square error rather than absolute error and mean error in this simulation? which one is not right?
why the ols are better than the other estimators in this "horse race"?
which statment is not true?
which statement is right?
why did we do the monte carlos?
what are the two ways to screw up in econometrics?
to have a low mean squared error, what do we do?
why weight more heavily observations with high x ’s?
what are the linear estimators and efficiency?
even though the disturbance term in the clrm is not normally distributed, the ols estimators are still unbiased.
we can not use linear model to deal with the nonlinear relationship.
the alternative hypothesis makes a claim about a ________.
assumption=0, implies .
, , are equivalent to .
even though the disturbance term in the clrm is not normally distributed, the ols estimators are still unbiased.