- 10.6.1: Explain what it means to make a Type II error.
- 10.6.2: Explain the term power of the test
- 10.6.3: To test H0: p = 0.30 versus H1: p 6 0.30, a simple random sample of...
- 10.6.4: To test H0: p = 0.40 versus H1: p 7 0.40, a simple random sample of...
- 10.6.5: To test H0: p = 0.65 versus H1: p 7 0.65, a simple random sample of...
- 10.6.6: To test H0: p = 0.75 versus H1: p 6 0.75, a simple random sample of...
- 10.6.7: To test H0: p = 0.45 versus H1: p 0.45, a simple random sample of n...
- 10.6.8: To test H0: p = 0.25 versus H1: p 0.25, a simple random sample of n...
- 10.6.9: Worried about Retirement? In April 2009, the Gallup organization su...
- 10.6.10: Ready for College? There are two major college entrance exams that ...
- 10.6.11: Quality of Education In August 2002, 47% of parents who had childre...
- 10.6.12: Eating Together In December 2001, 38% of adults with children under...
- 10.6.13: Taught Enough Math In 1994, 52% of parents of children in high scho...
- 10.6.14: Taught Enough Math In 1994, 52% of parents of children in high scho...
- 10.6.15: Effect of A Redo 3(b) with a = 0.01. What effect does lowering the ...
- 10.6.16: Effect of A Redo 4(b) with a = 0.01. What effect does lowering the ...
- 10.6.17: Power Curve Draw a power curve for the scenario in by finding the p...
- 10.6.18: . Power Curve Draw a power curve for the scenario in by finding the...
- 10.6.19: . Power in Tests on Means To test H0: m = 50 versus H1: m 6 50 a si...
- 10.6.20: What happens to the power of the test as the true value of the para...
- 10.6.21: What effect does increasing the sample size have on the power of th...
- 10.6.22: How do the probability of making a Type II error and effect size pl...
- 10.6.23: Explain the difference between accepting and not rejecting a null h...
- 10.6.24: According to the American Time Use Survey, the mean number of hours...
- 10.6.25: Explain the procedure for testing a hypothesis using the Classical ...
- 10.6.26: Explain the procedure for testing a hypothesis using the P-value Ap...
Solutions for Chapter 10.6: THE PROBABILITY OF A TYPE II ERROR AND THE POWER OF THE TEST
Full solutions for Statistics: Informed Decisions Using Data | 4th Edition
Solutions for Chapter 10.6: THE PROBABILITY OF A TYPE II ERROR AND THE POWER OF THE TESTGet Full Solutions
Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.
Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data
Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.
Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
Another term for the conidence coeficient.
Formulas used to determine the number of elements in sample spaces and events.
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
A probability distribution for a discrete random variable
Estimate (or point estimate)
The numerical value of a point estimator.
A subset of a sample space.
Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.
A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model
Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.
In statistical quality control, that portion of a number of units or the output of a process that is defective.
A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function