 Chapter 25.25.1: (a) The explanatory variable is amount of caffeine, in milligrams p...
 Chapter 25.25.2: The report says that F = 34.96, with P < 0.01.(a) What are the null...
 Chapter 25.25.3: (a) Make sidebyside stemplots of Trees for the three groups. Use ...
 Chapter 25.25.4: (a) Make stemplots of the heart rates for the three groups (round t...
 Chapter 25.25.5: (a) The middle group has larger mean than the other two. Grab its m...
 Chapter 25.25.6: (a) Use the mouse to slide the Pooled Standard Error at the top of ...
 Chapter 25.25.7: (a) The counts of trees in Exercise 25.3 and Figure 25.4.(b) The he...
 Chapter 25.25.8: Table 25.2 gives data on the species richness inrain forest plots, ...
 Chapter 25.25.9: (a) Do the sample means suggest that penetrability decreases as soi...
 Chapter 25.25.10: Logging in the rain forest, continued. Exercise 25.3 compares the n...
 Chapter 25.25.11: What music will you play? People often match their behavior to thei...
 Chapter 25.25.12: (a) The distributions of responses are somewhat rightskewed. ANOVA...
 Chapter 25.25.13: (a) Do the standard deviations satisfy the rule of thumb for safe u...
 Chapter 25.25.14: Do high school students from different racial/ethnicgroups have dif...
 Chapter 25.25.15: The purpose of analysis of variance is to compare(a) the variances ...
 Chapter 25.25.16: A study of the effects of smoking classifies subjects as nonsmokers...
 Chapter 25.25.17: The alternative hypothesis for the ANOVA F test in the previous exe...
 Chapter 25.25.18: The most striking conclusion from the numerical summaries for thetu...
 Chapter 25.25.19: We might use the twosample t procedures to compare summer and wint...
 Chapter 25.25.20: In all, we would have to give 6 twosample confidence intervals to ...
 Chapter 25.25.21: The conclusion of the ANOVA test is that(a) there is quite strong e...
 Chapter 25.25.22: Without software, we would compare F = 5.38 with critical values fr...
 Chapter 25.25.23: The Pvalue 0.014 in the output may not be accurate because the con...
 Chapter 25.25.24: How much do students borrow? A sample survey of students who had ju...
 Chapter 25.25.25: Morning or evening? Are you a morning person, an evening person, or...
 Chapter 25.25.26: Do strategies such as preparing a written outline helpstudents writ...
 Chapter 25.25.27: The Quebec (Canada) Cardiovascular Study recruited men aged 34 to 6...
 Chapter 25.25.28: (a) What are the degrees of freedom for the F statistic that compar...
 Chapter 25.25.29: (a) Make a graph that compares the mean emission rates for the four...
 Chapter 25.25.30: Can you hear these words? To test whether a hearing aid is right fo...
 Chapter 25.25.31: How do nematodes (microscopic worms) affectplant growth? A botanist...
 Chapter 25.25.32: Can you hear these words? Figure 25.11 displays the Minitab output ...
 Chapter 25.25.33: (a) This is a randomized comparative experiment. Outline the design...
 Chapter 25.25.34: Does nature heal best? Our bodies have a natural electrical field t...
 Chapter 25.25.35: Does polyester decay? How quickly do synthetic fabrics such as poly...
 Chapter 25.25.36: Analyze the data for the three processes andwrite a clear summary o...
 Chapter 25.25.37: Durable press fabrics wrinkle less. The data in Exercise 25.36 show...
 Chapter 25.25.38: (a) Make a table of the group means and standard deviations. Do the...
 Chapter 25.25.39: Plant defenses (optional). The calculations of ANOVA use only the s...
 Chapter 25.25.40: (a) Calculate the twosample t statistic for testing H0: 1 = 2 agai...
 Chapter 25.25.41: (a) Overall, 115 subjects (78% of 148 subjects randomized) complete...
Solutions for Chapter Chapter 25: OneWay Analysis of Variance: Comparing Several Means
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 25: OneWay Analysis of Variance: Comparing Several Means
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Since 41 problems in chapter Chapter 25: OneWay Analysis of Variance: Comparing Several Means have been answered, more than 7528 students have viewed full stepbystep solutions from this chapter. Chapter Chapter 25: OneWay Analysis of Variance: Comparing Several Means includes 41 full stepbystep solutions. This expansive textbook survival guide covers the following chapters and their solutions.

Acceptance region
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chisquare with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chisquare random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chisquare random variables.

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

Central tendency
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

Chisquare (or chisquared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

Comparative experiment
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.

Convolution
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

Cook’s distance
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Deming
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

Error variance
The variance of an error term or component in a model.

Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.

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.

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.

Harmonic mean
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .