UCF QMB 3200 COMPREHENSIVE EXAM ALL
ACTUAL QUESTIONS AND ANSWERS SURE A+
✔✔Confidence interval - ✔✔Another name for an interval estimate.
✔✔margin of error - ✔✔The ± value added to and subtracted from a point estimate in
order to develop an interval estimate of a population parameter.
✔✔Confidence coefficient - ✔✔The confidence level expressed as a decimal value. For
example, .95 is the confidence coefficient for a 95% confidence level.
✔✔Degrees of freedom - ✔✔A parameter of the t distribution. When the t distribution is
used in the computation of an interval estimate of a population mean, the appropriate t
distribution has n − 1 degrees of freedom, where n is the size of the sample.
✔✔Confidence level - ✔✔The confidence associated with an interval estimate. For
example, if an interval estimation procedure provides intervals such that 95% of the
intervals formed using the procedure will include the population parameter, the interval
estimate is said to be constructed at the 95% confidence level.
✔✔Null hypothesis - ✔✔The hypothesis tentatively assumed true in the hypothesis
testing procedure.
, ✔✔Two-tailed test - ✔✔A hypothesis test in which rejection of the null hypothesis
occurs for values of the test statistic in either tail of its sampling distribution.
✔✔Alternative hypothesis - ✔✔The hypothesis concluded to be true if the null
hypothesis is rejected.
✔✔p-value - ✔✔A probability that provides a measure of the evidence against the null
hypothesis provided by the sample. Smaller p-values indicate more evidence against
H0. For a lower tail test, the p-value is the probability of obtaining a value for the test
statistic as small as or smaller than that provided by the sample. For an upper tail test,
the p-value is the probability of obtaining a value for the test statistic as large as or
larger than that provided by the sample. For a two-tailed test, the p-value is the
probability of obtaining a value for the test statistic at least as unlikely as or more
unlikely than that provided by the sample.
✔✔Type I error - ✔✔The error of rejecting H0 when it is true.
✔✔Type II error - ✔✔The error of accepting H0 when it is false.
✔✔Level of significance - ✔✔The probability of making a Type I error when the null
hypothesis is true as an equality.
✔✔Critical value - ✔✔A value that is compared with the test statistic to determine
whether H0 should be rejected.
✔✔One-tailed test - ✔✔A hypothesis test in which rejection of the null hypothesis
occurs for values of the test statistic in one tail of its sampling distribution.
✔✔Dependent variable - ✔✔The variable that is being predicted or explained. It is
denoted by y.
✔✔Independent variable - ✔✔The variable that is doing the predicting or explaining. It is
denoted by x.
✔✔Simple linear regression - ✔✔Regression analysis involving one independent
variable and one dependent variable in which the relationship between the variables is
approximated by a straight line.
✔✔Regression model - ✔✔The equation that describes how y is related to x and an
error term; in simple linear regression, the regression model is y = β0 + β1x + ϵ.
ACTUAL QUESTIONS AND ANSWERS SURE A+
✔✔Confidence interval - ✔✔Another name for an interval estimate.
✔✔margin of error - ✔✔The ± value added to and subtracted from a point estimate in
order to develop an interval estimate of a population parameter.
✔✔Confidence coefficient - ✔✔The confidence level expressed as a decimal value. For
example, .95 is the confidence coefficient for a 95% confidence level.
✔✔Degrees of freedom - ✔✔A parameter of the t distribution. When the t distribution is
used in the computation of an interval estimate of a population mean, the appropriate t
distribution has n − 1 degrees of freedom, where n is the size of the sample.
✔✔Confidence level - ✔✔The confidence associated with an interval estimate. For
example, if an interval estimation procedure provides intervals such that 95% of the
intervals formed using the procedure will include the population parameter, the interval
estimate is said to be constructed at the 95% confidence level.
✔✔Null hypothesis - ✔✔The hypothesis tentatively assumed true in the hypothesis
testing procedure.
, ✔✔Two-tailed test - ✔✔A hypothesis test in which rejection of the null hypothesis
occurs for values of the test statistic in either tail of its sampling distribution.
✔✔Alternative hypothesis - ✔✔The hypothesis concluded to be true if the null
hypothesis is rejected.
✔✔p-value - ✔✔A probability that provides a measure of the evidence against the null
hypothesis provided by the sample. Smaller p-values indicate more evidence against
H0. For a lower tail test, the p-value is the probability of obtaining a value for the test
statistic as small as or smaller than that provided by the sample. For an upper tail test,
the p-value is the probability of obtaining a value for the test statistic as large as or
larger than that provided by the sample. For a two-tailed test, the p-value is the
probability of obtaining a value for the test statistic at least as unlikely as or more
unlikely than that provided by the sample.
✔✔Type I error - ✔✔The error of rejecting H0 when it is true.
✔✔Type II error - ✔✔The error of accepting H0 when it is false.
✔✔Level of significance - ✔✔The probability of making a Type I error when the null
hypothesis is true as an equality.
✔✔Critical value - ✔✔A value that is compared with the test statistic to determine
whether H0 should be rejected.
✔✔One-tailed test - ✔✔A hypothesis test in which rejection of the null hypothesis
occurs for values of the test statistic in one tail of its sampling distribution.
✔✔Dependent variable - ✔✔The variable that is being predicted or explained. It is
denoted by y.
✔✔Independent variable - ✔✔The variable that is doing the predicting or explaining. It is
denoted by x.
✔✔Simple linear regression - ✔✔Regression analysis involving one independent
variable and one dependent variable in which the relationship between the variables is
approximated by a straight line.
✔✔Regression model - ✔✔The equation that describes how y is related to x and an
error term; in simple linear regression, the regression model is y = β0 + β1x + ϵ.
