3337:
size. If, over a number of interviews, no fresh themes or insights show up, saturation has been reached and more interviews might not add much to our knowledge of the survivor's experience. Thus, rather than following a preset statistical formula, the concept of attaining saturation serves as a dynamic guide for determining sample size in qualitative research. There is a paucity of reliable guidance on estimating sample sizes before starting the research, with a range of suggestions given. In an effort to introduce some structure to the sample size determination process in qualitative research, a tool analogous to quantitative power calculations has been proposed. This tool, based on the
3329:
studies, researchers often adopt a subjective stance, making determinations as the study unfolds. Sample size determination in qualitative studies takes a different approach. It is generally a subjective judgment, taken as the research proceeds. One common approach is to continually include additional participants or materials until a point of "saturation" is reached. Saturation occurs when new participants or data cease to provide fresh insights, indicating that the study has adequately captured the diversity of perspectives or experiences within the chosen sample
6306:
3357:
726:. For example, in estimating the proportion of the U.S. population supporting a presidential candidate with a 95% confidence interval width of 2 percentage points (0.02), a sample size of (1.96)/ (0.02) = 9604 is required with the margin of error in this case is 1 percentage point. It is reasonable to use the 0.5 estimate for p in this case because the presidential races are often close to 50/50, and it is also prudent to use a conservative estimate. The
951:
For example, person 1 takes 25 minutes, person 2 takes 30 minutes, ..., person 100 takes 20 minutes. Add up all the commute times and divide by the number of people in the sample (100 in this case). The result would be your estimate of the mean commute time for the entire population. This method is practical when it's not feasible to measure everyone in the population, and it provides a reasonable approximation based on a representative sample.
526:
112:
decide that we want a 95% confidence level, meaning we are 95% confident that the true average satisfaction level falls within the calculated range. We also decide on a margin of error, of ±3%, which indicates the acceptable range of difference between our sample estimate and the true population parameter. Additionally, we may have some idea of the expected variability in satisfaction levels based on previous data or assumptions.
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1548:, as well as in many other laboratory experiments. It may not be as accurate as using other methods in estimating sample size, but gives a hint of what is the appropriate sample size where parameters such as expected standard deviations or expected differences in values between groups are unknown or very hard to estimate.
2875:
950:
Simply speaking, if we are trying to estimate the average time it takes for people to commute to work in a city. Instead of surveying the entire population, you can take a random sample of 100 individuals, record their commute times, and then calculate the mean (average) commute time for that sample.
3328:
Qualitative research approaches sample size determination with a distinctive methodology that diverges from quantitative methods. Rather than relying on predetermined formulas or statistical calculations, it involves a subjective and iterative judgment throughout the research process In qualitative
151:
Sample sizes may be evaluated by the quality of the resulting estimates, as follows. It is usually determined on the basis of the cost, time or convenience of data collection and the need for sufficient statistical power. For example, if a proportion is being estimated, one may wish to have the 95%
3336:
Unlike quantitative research, qualitative studies face a scarcity of reliable guidance regarding sample size estimation prior to beginning the research. Imagine conducting in-depth interviews with cancer survivors, qualitative researchers may use data saturation to determine the appropriate sample
2362:
There are many reasons to use stratified sampling: to decrease variances of sample estimates, to use partly non-random methods, or to study strata individually. A useful, partly non-random method would be to sample individuals where easily accessible, but, where not, sample clusters to save travel
107:
Sample size determination is a crucial aspect of research methodology that plays a significant role in ensuring the reliability and validity of study findings. In order to influence the accuracy of estimates, the power of statistical tests, and the general robustness of the research findings, it
111:
For example, if we are conducting a survey to determine the average satisfaction level of customers regarding a new product. To determine an appropriate sample size, we need to consider factors such as the desired level of confidence, margin of error, and variability in the responses. We might
128:
unknown parameters. For instance, to accurately determine the prevalence of pathogen infection in a specific species of fish, it is preferable to examine a sample of 200 fish rather than 100 fish. Several fundamental facts of mathematical statistics describe this phenomenon, including the
822:
452:
1104:
2590:
184:. It is a fundamental aspect of statistical analysis, particularly when gauging the prevalence of a specific characteristic within a population For example, we may wish to estimate the proportion of residents in a community who are at least 65 years old.
3068:
An "optimum allocation" is reached when the sampling rates within the strata are made directly proportional to the standard deviations within the strata and inversely proportional to the square root of the sampling cost per element within the strata,
160:
of a hypothesis test. For example, if we are comparing the support for a certain political candidate among women with the support for that candidate among men, we may wish to have 80% power to detect a difference in the support levels of 0.04 units.
2254:
2712:
1246:
For instance, if estimating the effect of a drug on blood pressure with a 95% confidence interval that is six units wide, and the known standard deviation of blood pressure in the population is 15, the required sample size would be
2139:
1941:
3175:
3005:
1313:
461:
for estimating proportions, the equation below can be solved, where W represents the desired width of the confidence interval. The resulting sample size formula, is often applied with a conservative estimate of
2463:
1327:
One of the prevalent challenges faced by statisticians revolves around the task of calculating the sample size needed to attain a specified statistical power for a test, all while maintaining a pre-determined
3318:
87:
using a target variance for an estimate to be derived from the sample eventually obtained, i.e., if a high precision is required (narrow confidence interval) this translates to a low target variance of the
1239:
736:
1170:
654:
724:
2026:
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53:
from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient
2474:
3875:
Galvin R (2015). How many interviews are enough? Do qualitative interviews in building energy consumption research produce reliable knowledge? Journal of
Building Engineering, 1:2â12.
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for a test, given a predetermined. As follows, this can be estimated by pre-determined tables for certain values, by Mead's resource equation, or, more generally, by the
2154:
1984:
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1600:
1319:
value. Understanding these calculations is essential for researchers designing studies to accurately estimate population means within a desired level of confidence.
314:
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2870:{\displaystyle \operatorname {Var} ({\bar {x}}_{w})=\sum _{h=1}^{H}W_{h}^{2}\operatorname {Var} ({\bar {x}}_{h})\left({\frac {1}{n_{h}}}-{\frac {1}{N_{h}}}\right),}
2620:
2277:
360:, yields a confidence interval, with Z representing the standard Z-score for the desired confidence level (e.g., 1.96 for a 95% confidence interval), in the form:
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3198:
3025:
2053:
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would equal 28, which is above the cutoff of 20, indicating that sample size may be a bit too large, and six animals per group might be more appropriate.
57:. In complex studies, different sample sizes may be allocated, such as in stratified surveys or experimental designs with multiple treatment groups. In a
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which satisfies (2). (This is a 1-tailed test.) In such a scenario, achieving this with a probability of at least 1âÎČ when the alternative hypothesis
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3995:
E122-07: Standard
Practice for Calculating Sample Size to Estimate, With Specified Precision, the Average for a Characteristic of a Lot or Process
3924:
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3102:
589:
In the figure below one can observe how sample sizes for binomial proportions change given different confidence levels and margins of error.
140:
In some situations, the increase in precision for larger sample sizes is minimal, or even non-existent. This can result from the presence of
98:
using a confidence level, i.e. the larger the required confidence level, the larger the sample size (given a constant precision requirement).
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954:
In a precisely mathematical way, when estimating the population mean using an independent and identically distributed (iid) sample of size
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3619:
Francis, Jill J.; Johnston, Marie; Robertson, Clare; Glidewell, Liz; Entwistle, Vikki; Eccles, Martin P.; Grimshaw, Jeremy M. (2010).
281:
is unknown, the maximum variance is often employed for sample size assessments. If a reasonable estimate for p is known the quantity
5729:
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3386:
3244:
6334:
5907:
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that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired
1337:
5965:
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5371:
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934:. However, the results reported may not be the exact value as numbers are preferably rounded up. Knowing that the value of the
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required for a confidence interval of width W, with W/2 as the margin of error on each side of the sample mean, the equation
92:
81:
4121:
817:{\displaystyle \left({\widehat {p}}-1.96{\sqrt {\frac {0.25}{n}}},\quad {\widehat {p}}+1.96{\sqrt {\frac {0.25}{n}}}\right)}
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1182:
192:
181:
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1004:
This expression describes quantitatively how the estimate becomes more precise as the sample size increases. Using the
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596:
447:{\displaystyle \left({\widehat {p}}-Z{\sqrt {\frac {0.25}{n}}},\quad {\widehat {p}}+Z{\sqrt {\frac {0.25}{n}}}\right)}
148:
in the data, or if the data follows a heavy-tailed distribution, or because the data is strongly dependent or biased.
6039:
6006:
1806: > 0. This is the smallest value for which we care about observing a difference. Now, for (1) to reject
659:
1989:
1099:{\displaystyle \left({\bar {x}}-{\frac {Z\sigma }{\sqrt {n}}},\quad {\bar {x}}+{\frac {Z\sigma }{\sqrt {n}}}\right)}
874:, providing a minimum sample size needed to meet the desired margin of error. The foregoing is commonly simplified:
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2585:{\displaystyle \operatorname {Var} ({\bar {x}}_{w})=\sum _{h=1}^{H}W_{h}^{2}\operatorname {Var} ({\bar {x}}_{h}).}
1555:
of the number of their concepts, and hence, their numbers are subtracted by 1 before insertion into the equation.
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38:
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1315:, which would be rounded up to 97, since sample sizes must be integers and must meet or exceed the calculated
535:
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to justify approximating the sample mean with a normal distribution yields a confidence interval of the form
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is true becomes imperative. Here, the sample average originates from a Normal distribution with a mean of
262:
125:
121:
50:
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61:, data is sought for an entire population, hence the intended sample size is equal to the population. In
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1005:
357:
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175:
134:
46:
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needed to acquire the desired result, the number of respondents then must lie on or above the minimum.
3833:
3621:"What is an adequate sample size? Operationalising data saturation for theory-based interview studies"
2622:, frequently, but not always, represent the proportions of the population elements in the strata, and
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130:
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4201:
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2249:{\displaystyle n\geq \left({\frac {z_{\alpha }+\Phi ^{-1}(1-\beta )}{\mu ^{*}/\sigma }}\right)^{2}}
1698:
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255:
153:
77:
62:
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2352:(i.e., that the total sample size is given by the sum of the sub-sample sizes). Selecting these
1960:
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entails carefully choosing the number of participants or data points to be included in a study.
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54:
45:. The sample size is an important feature of any empirical study in which the goal is to make
3515:
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rate α, which signifies the level of significance in hypothesis testing. It yields a certain
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3975:
Small Sample Size
Solutions (Open Access): A Guide for Applied Researchers and Practitioners
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sample sizes for binomial proportions given different confidence levels and margins of error
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3794:
Onwuegbuzie, Anthony J.; Leech, Nancy L. (2007). "A Call for
Qualitative Power Analyses".
3452:
1702:
727:
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258:
66:
824:
is commonly used to form a 95% confidence interval for the true proportion. The equation
2884:
within each stratum is made proportional to the standard deviation within each stratum:
2359:
optimally can be done in various ways, using (for example) Neyman's optimal allocation.
2134:{\displaystyle \Pr({\bar {x}}>z_{\alpha }\sigma /{\sqrt {n}}\mid H_{a})\geq 1-\beta }
1654:
For example, if a study using laboratory animals is planned with four treatment groups (
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3428:
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be less than 0.06 units wide. Alternatively, sample size may be assessed based on the
17:
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4146:
3974:
3709:"Clinician attitudes toward and use of electronic problem lists: A thematic analysis"
3672:
Guest, Greg; Bunce, Arwen; Johnson, Laura (2006). "How Many
Interviews Are Enough?".
3501:
The UFAW Handbook on the Care and
Management of Laboratory and Other Research Animals
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is reached. The number needed to reach saturation has been investigated empirically.
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1936:{\displaystyle \Pr({\bar {x}}>z_{\alpha }\sigma /{\sqrt {n}}\mid H_{0})=\alpha }
1329:
927:
76:
using experience â small samples, though sometimes unavoidable, can result in wide
3890:"Organizational research: Determining appropriate sample size for survey research"
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3908:
3834:"Supporting thinking on sample sizes for thematic analyses: A quantitative tool"
1359:
579:, in the case of using .5 as the most conservative estimate of the proportion.
5095:
4575:
4275:
4206:
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4131:
4051:
4015:
3807:
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3352:
1541:
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2295:, the sample can often be split up into sub-samples. Typically, if there are
242:
is the number of 'positive' instances (e.g., the number of people out of the
5248:
5100:
4720:
4515:
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4412:
4407:
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3725:
3605:
Glaser, B. (1965). The constant comparative method of qualitative analysis.
270:
188:
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3647:
890:
is the error bound on the estimate, i.e., the estimate is usually given as
4010:
1855:
is the upper α percentage point of the standard normal distribution, then
4764:
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4259:
4254:
4249:
4221:
3408:
1113:
for the desired level of confidence (1.96 for a 95% confidence interval).
266:
246:
sampled people who are at least 65 years old). When the observations are
3936:"Determining Sample Size: How to Ensure You Get the Correct Sample Size"
3782:
Sampling and choosing cases in qualitative research: A realist approach.
3760:"Sample Size and Saturation in PhD Studies Using Qualitative Interviews"
1622:, representing environmental effects allowed for in the design (minus 1)
6269:
5970:
3170:{\displaystyle {\frac {n_{h}}{N_{h}}}={\frac {KS_{h}}{\sqrt {C_{h}}}},}
926:= 10000 is required. These numbers are quoted often in news reports of
6191:
5172:
5146:
5126:
4377:
4168:
1701:
with unknown mean ÎŒ and known variance Ï. Consider two hypotheses, a
58:
3955:
3000:{\displaystyle S_{h}={\sqrt {\operatorname {Var} ({\bar {x}}_{h})}}}
1308:{\displaystyle {\frac {4\times 1.96^{2}\times 15^{2}}{6^{2}}}=96.04}
1520:(= effect size), which is the expected difference between the
2458:{\displaystyle {\bar {x}}_{w}=\sum _{h=1}^{H}W_{h}{\bar {x}}_{h},}
1612:
is the total number of individuals or units in the study (minus 1)
1544:'s resource equation is often used for estimating sample sizes of
524:
3707:
Wright, Adam; Maloney, Francine L.; Feblowitz, Joshua C. (2011).
4111:
3992:
3404:
1521:
6080:
5647:
5394:
4693:
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4024:
3592:
Sandelowski, M. (1995). Sample size in qualitative research.
1640:) being used, or the number of questions being asked (minus 1)
4020:
3313:{\displaystyle n_{h}={\frac {K'W_{h}S_{h}}{\sqrt {C_{h}}}}.}
2303:
different strata) then each of them will have a sample size
1658:=3), with eight animals per group, making 32 animals total (
1524:
of the target values between the experimental group and the
3494:
3492:
277:= 0.5. In practical applications, where the true parameter
27:
Statistical considerations on how many observations to make
4006:
A MATLAB script implementing
Cochran's sample size formula
3897:
Information
Technology, Learning, and Performance Journal
269:
of this distribution is 0.25, which occurs when the true
3888:
Bartlett, J. E. II; Kotrlik, J. W.; Higgins, C. (2001).
1172:
can be solved. This yields the sample size formula, for
1347:
69:, there may be different sample sizes for each group.
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Through careful manipulation, this can be shown (see
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37:
is the act of choosing the number of observations or
5933:
Autoregressive conditional heteroskedasticity (ARCH)
4011:
Sample Size
Calculator for various statistical tests
3841:
International
Journal of Social Research Methodology
1234:{\displaystyle n={\frac {4Z^{2}\sigma ^{2}}{W^{2}}}}
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2291:With more complicated sampling techniques, such as
1551:All the parameters in the equation are in fact the
730:in this case is 1 percentage point (half of 0.02).
3973:Rens van de Schoot, Milica MioÄeviÄ (eds.). 2020.
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3473:Statistics for the social and behavioral sciences
1165:{\displaystyle {\frac {Z\sigma }{\sqrt {n}}}=W/2}
180:A relatively simple situation is estimation of a
2057:
1865:
649:{\displaystyle Z{\sqrt {\frac {p(1-p)}{n}}}=W/2}
120:Larger sample sizes generally lead to increased
5481:Multivariate adaptive regression splines (MARS)
719:{\displaystyle n={\frac {4Z^{2}p(1-p)}{W^{2}}}}
2021:{\displaystyle z_{\alpha }\sigma /{\sqrt {n}}}
1490:The table shown on the right can be used in a
863:{\displaystyle 4{\sqrt {\frac {0.25}{n}}}=W/2}
508:{\displaystyle Z{\sqrt {\frac {0.25}{n}}}=W/2}
323:grows sufficiently large, the distribution of
65:, where a study may be divided into different
4036:
3827:
3825:
1813:with a probability of at least 1 â
994:{\displaystyle {\frac {\sigma }{\sqrt {n}}}.}
91:the use of a power target, i.e. the power of
8:
3667:
3665:
72:Sample sizes may be chosen in several ways:
3713:BMC Medical Informatics and Decision Making
3462:
3460:
3375:Engineering response surface example under
1802:for some 'smallest significant difference'
1323:Required sample sizes for hypothesis tests
95:to be applied once the sample is collected.
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3832:Fugard AJB; Potts HWW (10 February 2015).
1514:of the trial, shown in column to the left.
3852:
3734:
3724:
3529:by Michael FW Festing. Updated Sept. 2006
3499:Kirkwood, James; Robert Hubrecht (2010).
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3080:
3074:
3042:
3037:
3032:
3012:
2986:
2975:
2974:
2962:
2953:
2947:
2926:
2910:
2901:
2895:
2889:
2851:
2842:
2831:
2822:
2808:
2797:
2796:
2780:
2775:
2765:
2754:
2738:
2727:
2726:
2714:
2690:
2675:
2652:
2646:
2633:
2627:
2606:
2600:
2570:
2559:
2558:
2542:
2537:
2527:
2516:
2500:
2489:
2488:
2476:
2446:
2435:
2434:
2427:
2417:
2406:
2393:
2382:
2381:
2378:
2264:
2240:
2225:
2219:
2189:
2176:
2169:
2156:
2110:
2096:
2091:
2082:
2064:
2063:
2055:
2047:. Thus, the requirement is expressed as:
2011:
2006:
1997:
1991:
1965:
1964:
1962:
1918:
1904:
1899:
1890:
1872:
1871:
1863:
1846:is true, the following is necessary: If
1783:
1764:
1758:
1719:
1713:
1697:be independent observations taken from a
1566:
1291:
1280:
1267:
1254:
1252:
1223:
1212:
1202:
1192:
1184:
1154:
1131:
1129:
1074:
1060:
1059:
1038:
1024:
1023:
1016:
976:
974:
852:
834:
829:
797:
780:
779:
763:
746:
745:
738:
708:
679:
669:
661:
638:
603:
598:
561:
551:
545:
537:
497:
479:
474:
427:
410:
409:
393:
376:
375:
368:
358:Wald method for the binomial distribution
331:
330:
328:
286:
220:
203:
202:
200:
4016:Statulator for various statistical tests
572:{\displaystyle n={\frac {Z^{2}}{W^{2}}}}
457:To determine an appropriate sample size
3397:
6007:KaplanâMeier estimator (product limit)
3446:"Confidence Interval for a Proportion"
1650:should be somewhere between 10 and 20.
958:, where each data value has variance
914:= 3% the requirement approximates to
7:
6317:
6017:Accelerated failure time (AFT) model
3527:Isogenic.info > Resource equation
6329:
5612:Analysis of variance (ANOVA, anova)
2880:which can be made a minimum if the
2670:. For a fixed sample size, that is
1792:{\displaystyle H_{a}:\mu =\mu ^{*}}
1494:to estimate the sample sizes of an
5707:CochranâMantelâHaenszel statistics
4333:Pearson product-moment correlation
3417:e-Handbook of Statistical Methods.
2935:{\displaystyle n_{h}/N_{h}=kS_{h}}
2370:strata, a weighted sample mean is
2266:
2186:
1506:is 0.05. The parameters used are:
352:will be closely approximated by a
25:
3764:Forum Qualitative Sozialforschung
3387:Receiver operating characteristic
1646:is the degrees of freedom of the
1632:, corresponding to the number of
1536:
6328:
6316:
6304:
6291:
6290:
3594:Research in Nursing & Health
3413:"7.2.4.2. Sample sizes required"
3355:
2281:cumulative distribution function
1678:Cumulative distribution function
1338:cumulative distribution function
593:Otherwise, the formula would be
250:, this estimator has a (scaled)
5966:Least-squares spectral analysis
3503:. Wiley-Blackwell. p. 29.
3341:, is particularly tailored for
1750:and an alternative hypothesis:
1058:
778:
733:In practice, the formula :
408:
4947:Mean-unbiased minimum-variance
3339:negative binomial distribution
3231:{\displaystyle \sum {n_{h}}=n}
3058:{\displaystyle \sum {n_{h}}=n}
2992:
2980:
2970:
2814:
2802:
2792:
2744:
2732:
2722:
2576:
2564:
2554:
2506:
2494:
2484:
2440:
2387:
2325:must conform to the rule that
2210:
2198:
2116:
2069:
2060:
1970:
1924:
1877:
1868:
1065:
1029:
700:
688:
622:
610:
336:
316:may be used in place of 0.25.
303:
291:
231:{\displaystyle {\hat {p}}=X/n}
208:
82:statistical hypothesis testing
1:
6260:Geographic information system
5476:Simultaneous equations models
3960:University of Florida, PEOD-6
3934:Smith, Scott (8 April 2013).
3854:10.1080/13645579.2015.1005453
2663:{\displaystyle W_{h}=N_{h}/N}
1117:To determine the sample size
5443:Coefficient of determination
5054:Uniformly most powerful test
3988:NIST: Selecting Sample Sizes
2699:{\displaystyle n=\sum n_{h}}
1740:{\displaystyle H_{0}:\mu =0}
6012:Proportional hazards models
5956:Spectral density estimation
5938:Vector autoregression (VAR)
5372:Maximum posterior estimator
4604:Randomized controlled trial
3238:, or, more generally, when
522:, yielding the sample size
6373:
5772:Multivariate distributions
4192:Average absolute deviation
3429:"Inference for Regression"
1979:{\displaystyle {\bar {x}}}
1662:=31), without any further
1528:, divided by the expected
345:{\displaystyle {\hat {p}}}
173:
170:Estimation of a proportion
6286:
6089:
6076:
5760:Structural equation model
5668:
5643:
5414:
5390:
5122:
5096:Score/Lagrange multiplier
4702:
4689:
4511:Sample size determination
4472:
4459:
4089:
4076:
4058:
3956:"Determining Sample Size"
3954:Israel, Glenn D. (1992).
3808:10.1007/s11135-005-1098-1
3640:10.1080/08870440903194015
3475:. Boston: Little, Brown.
2146:Statistical power Example
1358:
1350:
938:is the minimum number of
31:Sample size determination
6255:Environmental statistics
5777:Elliptical distributions
5570:Generalized linear model
5499:Simple linear regression
5269:HodgesâLehmann estimator
4726:Probability distribution
4635:Stochastic approximation
4197:Coefficient of variation
3686:10.1177/1525822X05279903
3538:Kish (1965, Section 3.1)
3471:Kenny, David A. (1987).
3200:is a constant such that
3027:is a constant such that
1839:with probability α when
1595:{\displaystyle E=N-B-T,}
1537:Mead's resource equation
5915:Cross-correlation (XCF)
5523:Non-standard predictors
4957:LehmannâScheffĂ© theorem
4630:Adaptive clinical trial
3726:10.1186/1472-6947-11-36
3628:Psychology & Health
2299:such sub-samples (from
1957:if our sample average (
966:of the sample mean is:
18:Estimating sample sizes
6311:Mathematics portal
6132:Engineering statistics
6040:NelsonâAalen estimator
5617:Analysis of covariance
5504:Ordinary least squares
5428:Pearson product-moment
4832:Statistical functional
4743:Empirical distribution
4576:Controlled experiments
4305:Frequency distribution
4083:Descriptive statistics
3796:Quality & Quantity
3314:
3232:
3194:
3171:
3090:
3059:
3021:
3001:
2936:
2871:
2770:
2700:
2664:
2616:
2586:
2532:
2459:
2422:
2287:Stratified sample size
2273:
2250:
2135:
2022:
1980:
1937:
1793:
1741:
1596:
1309:
1235:
1166:
1109:where Z is a standard
1100:
995:
922:= 1% a sample size of
864:
818:
720:
650:
573:
530:
509:
448:
346:
310:
309:{\displaystyle p(1-p)}
263:Bernoulli distribution
232:
80:and risk of errors in
6357:Sampling (statistics)
6227:Population statistics
6169:System identification
5903:Autocorrelation (ACF)
5831:Exponential smoothing
5745:Discriminant analysis
5740:Canonical correlation
5604:Partition of variance
5466:Regression validation
5310:(JonckheereâTerpstra)
5209:Likelihood-ratio test
4898:Frequentist inference
4810:Locationâscale family
4731:Sampling distribution
4696:Statistical inference
4663:Cross-sectional study
4650:Observational studies
4609:Randomized experiment
4438:Stem-and-leaf display
4240:Central limit theorem
3371:Design of experiments
3315:
3233:
3195:
3172:
3091:
3089:{\displaystyle C_{h}}
3060:
3022:
3002:
2937:
2872:
2750:
2701:
2665:
2617:
2615:{\displaystyle W_{h}}
2587:
2512:
2460:
2402:
2274:
2272:{\displaystyle \Phi }
2251:
2136:
2023:
1981:
1938:
1794:
1742:
1597:
1310:
1236:
1167:
1101:
1006:central limit theorem
996:
865:
819:
721:
651:
574:
528:
510:
449:
356:. Using this and the
347:
311:
252:binomial distribution
233:
176:Population proportion
135:central limit theorem
6150:Probabilistic design
5735:Principal components
5578:Exponential families
5530:Nonlinear regression
5509:General linear model
5471:Mixed effects models
5461:Errors and residuals
5438:Confounding variable
5340:Bayesian probability
5318:Van der Waerden test
5308:Ordered alternative
5073:Multiple comparisons
4952:RaoâBlackwellization
4915:Estimating equations
4871:Statistical distance
4589:Factorial experiment
4122:Arithmetic-Geometric
3758:Mason, Mark (2010).
3547:Kish (1965), p. 148.
3324:Qualitative research
3245:
3204:
3184:
3103:
3073:
3031:
3011:
2946:
2888:
2713:
2674:
2626:
2599:
2475:
2377:
2263:
2155:
2054:
1990:
1961:
1862:
1757:
1712:
1565:
1251:
1183:
1128:
1015:
973:
946:Estimation of a mean
828:
737:
660:
597:
536:
473:
367:
327:
285:
199:
131:law of large numbers
78:confidence intervals
6222:Official statistics
6145:Methods engineering
5826:Seasonal adjustment
5594:Poisson regressions
5514:Bayesian regression
5453:Regression analysis
5433:Partial correlation
5405:Regression analysis
5004:Prediction interval
4999:Likelihood interval
4989:Confidence interval
4981:Interval estimation
4942:Unbiased estimators
4760:Model specification
4640:Up-and-down designs
4328:Partial correlation
4284:Index of dispersion
4202:Interquartile range
3583:Kish (1965), p. 94.
3574:Kish (1965), p. 93.
3565:Kish (1965), p. 81.
3556:Kish (1965), p. 78.
3377:Stepwise regression
2785:
2547:
2293:stratified sampling
1699:normal distribution
1648:error component and
1630:treatment component
898:= 10% one requires
354:normal distribution
319:As the sample size
154:confidence interval
63:experimental design
6242:Spatial statistics
6122:Medical statistics
6022:First hitting time
5976:Whittle likelihood
5627:Degrees of freedom
5622:Multivariate ANOVA
5555:Heteroscedasticity
5367:Bayesian estimator
5332:Bayesian inference
5181:KolmogorovâSmirnov
5066:Randomization test
5036:Testing hypotheses
5009:Tolerance interval
4920:Maximum likelihood
4815:Exponential family
4748:Density estimation
4708:Statistical theory
4668:Natural experiment
4614:Scientific control
4531:Survey methodology
4217:Standard deviation
3882:General references
3780:Emmel, N. (2013).
3451:2011-08-23 at the
3363:Mathematics portal
3310:
3228:
3190:
3167:
3086:
3055:
3017:
2997:
2932:
2867:
2771:
2696:
2660:
2612:
2582:
2533:
2455:
2269:
2246:
2131:
2018:
1976:
1933:
1832:), and (2) reject
1789:
1737:
1620:blocking component
1592:
1553:degrees of freedom
1546:laboratory animals
1530:standard deviation
1504:significance level
1496:experimental group
1305:
1231:
1162:
1096:
991:
918:= 1000, while for
870:can be solved for
860:
814:
716:
646:
569:
531:
505:
444:
342:
306:
228:
43:statistical sample
6344:
6343:
6282:
6281:
6278:
6277:
6217:National accounts
6187:Actuarial science
6179:Social statistics
6072:
6071:
6068:
6067:
6064:
6063:
5999:Survival function
5984:
5983:
5846:Granger causality
5687:Contingency table
5662:Survival analysis
5639:
5638:
5635:
5634:
5491:Linear regression
5386:
5385:
5382:
5381:
5357:Credible interval
5326:
5325:
5109:
5108:
4925:Method of moments
4794:Parametric family
4755:Statistical model
4685:
4684:
4681:
4680:
4599:Random assignment
4521:Statistical power
4455:
4454:
4451:
4450:
4300:Contingency table
4270:
4269:
4137:Generalized/power
3926:978-0-471-48900-9
3634:(10): 1229â1245.
3510:978-1-4051-7523-4
3482:978-0-316-48915-7
3343:thematic analysis
3305:
3304:
3193:{\displaystyle K}
3162:
3161:
3128:
3020:{\displaystyle k}
2995:
2983:
2857:
2837:
2805:
2735:
2567:
2497:
2443:
2390:
2234:
2148:) to happen when
2101:
2072:
2016:
1973:
1909:
1880:
1828:of 1 â
1558:The equation is:
1512:statistical power
1492:two-sample t-test
1488:
1487:
1297:
1229:
1146:
1145:
1089:
1088:
1068:
1053:
1052:
1032:
986:
985:
844:
843:
807:
806:
788:
773:
772:
754:
714:
656:, which yields
630:
629:
567:
489:
488:
437:
436:
418:
403:
402:
384:
339:
254:(and is also the
211:
142:systematic errors
55:statistical power
16:(Redirected from
6364:
6332:
6331:
6320:
6319:
6309:
6308:
6294:
6293:
6197:Crime statistics
6091:
6078:
5995:
5961:Fourier analysis
5948:Frequency domain
5928:
5875:
5841:Structural break
5801:
5750:Cluster analysis
5697:Log-linear model
5670:
5645:
5586:
5560:Homoscedasticity
5416:
5392:
5311:
5303:
5295:
5294:(KruskalâWallis)
5279:
5264:
5219:Cross validation
5204:
5186:AndersonâDarling
5133:
5120:
5091:Likelihood-ratio
5083:Parametric tests
5061:Permutation test
5044:1- & 2-tails
4935:Minimum distance
4907:Point estimation
4903:
4854:Optimal decision
4805:
4704:
4691:
4673:Quasi-experiment
4623:Adaptive designs
4474:
4461:
4338:Rank correlation
4100:
4091:
4078:
4045:
4038:
4031:
4022:
3970:
3968:
3966:
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3948:
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3904:
3894:
3876:
3873:
3867:
3866:
3856:
3838:
3829:
3820:
3819:
3791:
3785:
3778:
3772:
3771:
3755:
3749:
3748:
3738:
3728:
3704:
3698:
3697:
3669:
3660:
3659:
3625:
3616:
3610:
3603:
3597:
3590:
3584:
3581:
3575:
3572:
3566:
3563:
3557:
3554:
3548:
3545:
3539:
3536:
3530:
3524:
3518:
3514:
3496:
3487:
3486:
3469:, page 215, in:
3464:
3455:
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3365:
3360:
3359:
3319:
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2383:
2366:In general, for
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2086:
2074:
2073:
2065:
2027:
2025:
2024:
2019:
2017:
2012:
2010:
2002:
2001:
1985:
1983:
1982:
1977:
1975:
1974:
1966:
1942:
1940:
1939:
1934:
1923:
1922:
1910:
1905:
1903:
1895:
1894:
1882:
1881:
1873:
1824:is true (i.e. a
1798:
1796:
1795:
1790:
1788:
1787:
1769:
1768:
1746:
1744:
1743:
1738:
1724:
1723:
1634:treatment groups
1601:
1599:
1598:
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1348:
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1298:
1296:
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93:statistical test
67:treatment groups
41:to include in a
21:
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6367:
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6365:
6363:
6362:
6361:
6347:
6346:
6345:
6340:
6303:
6274:
6236:
6173:
6159:quality control
6126:
6108:Clinical trials
6085:
6060:
6044:
6032:Hazard function
6026:
5980:
5942:
5926:
5889:
5885:BreuschâGodfrey
5873:
5850:
5790:
5765:Factor analysis
5711:
5692:Graphical model
5664:
5631:
5598:
5584:
5564:
5518:
5485:
5447:
5410:
5409:
5378:
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5277:
5262:
5241:Rank statistics
5235:
5214:Model selection
5202:
5160:Goodness of fit
5154:
5131:
5105:
5077:
5030:
4975:
4964:Median unbiased
4892:
4803:
4736:Order statistic
4698:
4677:
4644:
4618:
4570:
4525:
4468:
4466:Data collection
4447:
4359:
4314:
4288:
4266:
4226:
4178:
4095:Continuous data
4085:
4072:
4054:
4049:
4002:
3984:
3982:Further reading
3964:
3962:
3953:
3944:
3942:
3933:
3927:
3915:Survey Sampling
3907:
3892:
3887:
3884:
3879:
3874:
3870:
3836:
3831:
3830:
3823:
3793:
3792:
3788:
3779:
3775:
3757:
3756:
3752:
3706:
3705:
3701:
3671:
3670:
3663:
3623:
3618:
3617:
3613:
3607:Social Problems
3604:
3600:
3591:
3587:
3582:
3578:
3573:
3569:
3564:
3560:
3555:
3551:
3546:
3542:
3537:
3533:
3525:
3521:
3511:
3498:
3497:
3490:
3483:
3470:
3465:
3458:
3453:Wayback Machine
3444:
3440:
3427:
3426:
3422:
3403:
3399:
3395:
3361:
3354:
3351:
3326:
3294:
3282:
3272:
3264:
3263:
3248:
3243:
3242:
3211:
3202:
3201:
3182:
3181:
3151:
3139:
3135:
3118:
3108:
3101:
3100:
3076:
3071:
3070:
3038:
3029:
3028:
3009:
3008:
2973:
2949:
2944:
2943:
2922:
2906:
2891:
2886:
2885:
2847:
2827:
2821:
2817:
2795:
2725:
2711:
2710:
2686:
2672:
2671:
2642:
2629:
2624:
2623:
2602:
2597:
2596:
2557:
2487:
2473:
2472:
2433:
2423:
2380:
2375:
2374:
2357:
2347:
2338:
2331:
2323:
2308:
2289:
2261:
2260:
2215:
2214:
2185:
2172:
2171:
2165:
2164:
2153:
2152:
2106:
2078:
2052:
2051:
2042:
1993:
1988:
1987:
1986:) is more than
1959:
1958:
1956:
1914:
1886:
1860:
1859:
1854:
1845:
1838:
1823:
1812:
1779:
1760:
1755:
1754:
1715:
1710:
1709:
1703:null hypothesis
1687:
1680:
1563:
1562:
1539:
1353:
1351:
1346:
1325:
1287:
1276:
1263:
1256:
1249:
1248:
1219:
1208:
1198:
1194:
1181:
1180:
1133:
1126:
1125:
1076:
1040:
1022:
1018:
1013:
1012:
971:
970:
948:
906:= 5% one needs
882: = 1/
878: = 4/
826:
825:
744:
740:
735:
734:
728:margin of error
704:
675:
671:
658:
657:
606:
595:
594:
592:
583:margin of error
557:
547:
534:
533:
471:
470:
374:
370:
365:
364:
325:
324:
283:
282:
265:). The maximum
261:of data from a
197:
196:
178:
172:
167:
118:
105:
28:
23:
22:
15:
12:
11:
5:
6370:
6368:
6360:
6359:
6349:
6348:
6342:
6341:
6339:
6338:
6326:
6314:
6300:
6287:
6284:
6283:
6280:
6279:
6276:
6275:
6273:
6272:
6267:
6262:
6257:
6252:
6246:
6244:
6238:
6237:
6235:
6234:
6229:
6224:
6219:
6214:
6209:
6204:
6199:
6194:
6189:
6183:
6181:
6175:
6174:
6172:
6171:
6166:
6161:
6152:
6147:
6142:
6136:
6134:
6128:
6127:
6125:
6124:
6119:
6114:
6105:
6103:Bioinformatics
6099:
6097:
6087:
6086:
6081:
6074:
6073:
6070:
6069:
6066:
6065:
6062:
6061:
6059:
6058:
6052:
6050:
6046:
6045:
6043:
6042:
6036:
6034:
6028:
6027:
6025:
6024:
6019:
6014:
6009:
6003:
6001:
5992:
5986:
5985:
5982:
5981:
5979:
5978:
5973:
5968:
5963:
5958:
5952:
5950:
5944:
5943:
5941:
5940:
5935:
5930:
5922:
5917:
5912:
5911:
5910:
5908:partial (PACF)
5899:
5897:
5891:
5890:
5888:
5887:
5882:
5877:
5869:
5864:
5858:
5856:
5855:Specific tests
5852:
5851:
5849:
5848:
5843:
5838:
5833:
5828:
5823:
5818:
5813:
5807:
5805:
5798:
5792:
5791:
5789:
5788:
5787:
5786:
5785:
5784:
5769:
5768:
5767:
5757:
5755:Classification
5752:
5747:
5742:
5737:
5732:
5727:
5721:
5719:
5713:
5712:
5710:
5709:
5704:
5702:McNemar's test
5699:
5694:
5689:
5684:
5678:
5676:
5666:
5665:
5648:
5641:
5640:
5637:
5636:
5633:
5632:
5630:
5629:
5624:
5619:
5614:
5608:
5606:
5600:
5599:
5597:
5596:
5580:
5574:
5572:
5566:
5565:
5563:
5562:
5557:
5552:
5547:
5542:
5540:Semiparametric
5537:
5532:
5526:
5524:
5520:
5519:
5517:
5516:
5511:
5506:
5501:
5495:
5493:
5487:
5486:
5484:
5483:
5478:
5473:
5468:
5463:
5457:
5455:
5449:
5448:
5446:
5445:
5440:
5435:
5430:
5424:
5422:
5412:
5411:
5408:
5407:
5402:
5396:
5395:
5388:
5387:
5384:
5383:
5380:
5379:
5377:
5376:
5375:
5374:
5364:
5359:
5354:
5353:
5352:
5347:
5336:
5334:
5328:
5327:
5324:
5323:
5321:
5320:
5315:
5314:
5313:
5305:
5297:
5281:
5278:(MannâWhitney)
5273:
5272:
5271:
5258:
5257:
5256:
5245:
5243:
5237:
5236:
5234:
5233:
5232:
5231:
5226:
5221:
5211:
5206:
5203:(ShapiroâWilk)
5198:
5193:
5188:
5183:
5178:
5170:
5164:
5162:
5156:
5155:
5153:
5152:
5144:
5135:
5123:
5117:
5115:Specific tests
5111:
5110:
5107:
5106:
5104:
5103:
5098:
5093:
5087:
5085:
5079:
5078:
5076:
5075:
5070:
5069:
5068:
5058:
5057:
5056:
5046:
5040:
5038:
5032:
5031:
5029:
5028:
5027:
5026:
5021:
5011:
5006:
5001:
4996:
4991:
4985:
4983:
4977:
4976:
4974:
4973:
4968:
4967:
4966:
4961:
4960:
4959:
4954:
4939:
4938:
4937:
4932:
4927:
4922:
4911:
4909:
4900:
4894:
4893:
4891:
4890:
4885:
4880:
4879:
4878:
4868:
4863:
4862:
4861:
4851:
4850:
4849:
4844:
4839:
4829:
4824:
4819:
4818:
4817:
4812:
4807:
4791:
4790:
4789:
4784:
4779:
4769:
4768:
4767:
4762:
4752:
4751:
4750:
4740:
4739:
4738:
4728:
4723:
4718:
4712:
4710:
4700:
4699:
4694:
4687:
4686:
4683:
4682:
4679:
4678:
4676:
4675:
4670:
4665:
4660:
4654:
4652:
4646:
4645:
4643:
4642:
4637:
4632:
4626:
4624:
4620:
4619:
4617:
4616:
4611:
4606:
4601:
4596:
4591:
4586:
4580:
4578:
4572:
4571:
4569:
4568:
4566:Standard error
4563:
4558:
4553:
4552:
4551:
4546:
4535:
4533:
4527:
4526:
4524:
4523:
4518:
4513:
4508:
4503:
4498:
4496:Optimal design
4493:
4488:
4482:
4480:
4470:
4469:
4464:
4457:
4456:
4453:
4452:
4449:
4448:
4446:
4445:
4440:
4435:
4430:
4425:
4420:
4415:
4410:
4405:
4400:
4395:
4390:
4385:
4380:
4375:
4369:
4367:
4361:
4360:
4358:
4357:
4352:
4351:
4350:
4345:
4335:
4330:
4324:
4322:
4316:
4315:
4313:
4312:
4307:
4302:
4296:
4294:
4293:Summary tables
4290:
4289:
4287:
4286:
4280:
4278:
4272:
4271:
4268:
4267:
4265:
4264:
4263:
4262:
4257:
4252:
4242:
4236:
4234:
4228:
4227:
4225:
4224:
4219:
4214:
4209:
4204:
4199:
4194:
4188:
4186:
4180:
4179:
4177:
4176:
4171:
4166:
4165:
4164:
4159:
4154:
4149:
4144:
4139:
4134:
4129:
4127:Contraharmonic
4124:
4119:
4108:
4106:
4097:
4087:
4086:
4081:
4074:
4073:
4071:
4070:
4065:
4059:
4056:
4055:
4050:
4048:
4047:
4040:
4033:
4025:
4019:
4018:
4013:
4008:
4001:
4000:External links
3998:
3997:
3996:
3990:
3983:
3980:
3979:
3978:
3971:
3951:
3931:
3925:
3905:
3883:
3880:
3878:
3877:
3868:
3847:(6): 669â684.
3821:
3786:
3773:
3750:
3699:
3661:
3611:
3598:
3585:
3576:
3567:
3558:
3549:
3540:
3531:
3519:
3516:online Page 29
3509:
3488:
3481:
3456:
3438:
3420:
3396:
3394:
3391:
3390:
3389:
3384:
3379:
3373:
3367:
3366:
3350:
3347:
3325:
3322:
3321:
3320:
3309:
3301:
3297:
3289:
3285:
3279:
3275:
3270:
3267:
3260:
3255:
3251:
3227:
3224:
3218:
3214:
3209:
3189:
3178:
3177:
3166:
3158:
3154:
3146:
3142:
3138:
3132:
3125:
3121:
3115:
3111:
3083:
3079:
3054:
3051:
3045:
3041:
3036:
3016:
2994:
2989:
2982:
2979:
2972:
2969:
2966:
2961:
2956:
2952:
2929:
2925:
2921:
2918:
2913:
2909:
2904:
2898:
2894:
2878:
2877:
2866:
2862:
2854:
2850:
2846:
2841:
2834:
2830:
2826:
2820:
2816:
2811:
2804:
2801:
2794:
2791:
2788:
2783:
2778:
2774:
2768:
2763:
2760:
2757:
2753:
2749:
2746:
2741:
2734:
2731:
2724:
2721:
2718:
2693:
2689:
2685:
2682:
2679:
2659:
2655:
2649:
2645:
2641:
2636:
2632:
2609:
2605:
2593:
2592:
2581:
2578:
2573:
2566:
2563:
2556:
2553:
2550:
2545:
2540:
2536:
2530:
2525:
2522:
2519:
2515:
2511:
2508:
2503:
2496:
2493:
2486:
2483:
2480:
2466:
2465:
2454:
2449:
2442:
2439:
2430:
2426:
2420:
2415:
2412:
2409:
2405:
2401:
2396:
2389:
2386:
2355:
2343:
2336:
2329:
2321:
2306:
2288:
2285:
2279:is the normal
2268:
2257:
2256:
2243:
2238:
2232:
2228:
2222:
2218:
2212:
2209:
2206:
2203:
2200:
2195:
2192:
2188:
2184:
2179:
2175:
2168:
2163:
2160:
2142:
2141:
2130:
2127:
2124:
2121:
2118:
2113:
2109:
2105:
2100:
2094:
2090:
2085:
2081:
2077:
2071:
2068:
2062:
2059:
2040:
2030:
2029:
2015:
2009:
2005:
2000:
1996:
1972:
1969:
1954:
1944:
1943:
1932:
1929:
1926:
1921:
1917:
1913:
1908:
1902:
1898:
1893:
1889:
1885:
1879:
1876:
1870:
1867:
1850:
1843:
1836:
1821:
1810:
1800:
1799:
1786:
1782:
1778:
1775:
1772:
1767:
1763:
1748:
1747:
1736:
1733:
1730:
1727:
1722:
1718:
1685:
1679:
1676:
1664:stratification
1652:
1651:
1641:
1623:
1613:
1603:
1602:
1591:
1588:
1585:
1582:
1579:
1576:
1573:
1570:
1538:
1535:
1534:
1533:
1515:
1486:
1485:
1482:
1479:
1476:
1472:
1471:
1468:
1465:
1462:
1458:
1457:
1454:
1451:
1448:
1444:
1443:
1440:
1437:
1434:
1430:
1429:
1426:
1423:
1420:
1416:
1415:
1412:
1409:
1406:
1402:
1401:
1398:
1395:
1392:
1388:
1387:
1384:
1381:
1378:
1374:
1373:
1370:
1367:
1363:
1362:
1357:
1345:
1342:
1324:
1321:
1304:
1301:
1294:
1290:
1283:
1279:
1275:
1270:
1266:
1262:
1259:
1226:
1222:
1215:
1211:
1205:
1201:
1197:
1191:
1188:
1178:
1177:
1161:
1157:
1153:
1150:
1144:
1139:
1136:
1115:
1114:
1107:
1094:
1087:
1082:
1079:
1073:
1067:
1064:
1057:
1051:
1046:
1043:
1037:
1031:
1028:
1021:
1002:
1001:
990:
984:
980:
964:standard error
947:
944:
932:sample surveys
859:
855:
851:
848:
842:
839:
833:
812:
805:
802:
796:
793:
787:
784:
777:
771:
768:
762:
759:
753:
750:
743:
711:
707:
702:
699:
696:
693:
690:
687:
682:
678:
674:
668:
665:
645:
641:
637:
634:
628:
624:
621:
618:
615:
612:
609:
602:
564:
560:
554:
550:
544:
541:
516:
515:
504:
500:
496:
493:
487:
484:
478:
455:
454:
442:
435:
432:
426:
423:
417:
414:
407:
401:
398:
392:
389:
383:
380:
373:
338:
335:
305:
302:
299:
296:
293:
290:
227:
223:
219:
216:
210:
207:
174:Main article:
171:
168:
166:
163:
117:
114:
104:
101:
100:
99:
96:
89:
85:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
6369:
6358:
6355:
6354:
6352:
6337:
6336:
6327:
6325:
6324:
6315:
6313:
6312:
6307:
6301:
6299:
6298:
6289:
6288:
6285:
6271:
6268:
6266:
6265:Geostatistics
6263:
6261:
6258:
6256:
6253:
6251:
6248:
6247:
6245:
6243:
6239:
6233:
6232:Psychometrics
6230:
6228:
6225:
6223:
6220:
6218:
6215:
6213:
6210:
6208:
6205:
6203:
6200:
6198:
6195:
6193:
6190:
6188:
6185:
6184:
6182:
6180:
6176:
6170:
6167:
6165:
6162:
6160:
6156:
6153:
6151:
6148:
6146:
6143:
6141:
6138:
6137:
6135:
6133:
6129:
6123:
6120:
6118:
6115:
6113:
6109:
6106:
6104:
6101:
6100:
6098:
6096:
6095:Biostatistics
6092:
6088:
6084:
6079:
6075:
6057:
6056:Log-rank test
6054:
6053:
6051:
6047:
6041:
6038:
6037:
6035:
6033:
6029:
6023:
6020:
6018:
6015:
6013:
6010:
6008:
6005:
6004:
6002:
6000:
5996:
5993:
5991:
5987:
5977:
5974:
5972:
5969:
5967:
5964:
5962:
5959:
5957:
5954:
5953:
5951:
5949:
5945:
5939:
5936:
5934:
5931:
5929:
5927:(BoxâJenkins)
5923:
5921:
5918:
5916:
5913:
5909:
5906:
5905:
5904:
5901:
5900:
5898:
5896:
5892:
5886:
5883:
5881:
5880:DurbinâWatson
5878:
5876:
5870:
5868:
5865:
5863:
5862:DickeyâFuller
5860:
5859:
5857:
5853:
5847:
5844:
5842:
5839:
5837:
5836:Cointegration
5834:
5832:
5829:
5827:
5824:
5822:
5819:
5817:
5814:
5812:
5811:Decomposition
5809:
5808:
5806:
5802:
5799:
5797:
5793:
5783:
5780:
5779:
5778:
5775:
5774:
5773:
5770:
5766:
5763:
5762:
5761:
5758:
5756:
5753:
5751:
5748:
5746:
5743:
5741:
5738:
5736:
5733:
5731:
5728:
5726:
5723:
5722:
5720:
5718:
5714:
5708:
5705:
5703:
5700:
5698:
5695:
5693:
5690:
5688:
5685:
5683:
5682:Cohen's kappa
5680:
5679:
5677:
5675:
5671:
5667:
5663:
5659:
5655:
5651:
5646:
5642:
5628:
5625:
5623:
5620:
5618:
5615:
5613:
5610:
5609:
5607:
5605:
5601:
5595:
5591:
5587:
5581:
5579:
5576:
5575:
5573:
5571:
5567:
5561:
5558:
5556:
5553:
5551:
5548:
5546:
5543:
5541:
5538:
5536:
5535:Nonparametric
5533:
5531:
5528:
5527:
5525:
5521:
5515:
5512:
5510:
5507:
5505:
5502:
5500:
5497:
5496:
5494:
5492:
5488:
5482:
5479:
5477:
5474:
5472:
5469:
5467:
5464:
5462:
5459:
5458:
5456:
5454:
5450:
5444:
5441:
5439:
5436:
5434:
5431:
5429:
5426:
5425:
5423:
5421:
5417:
5413:
5406:
5403:
5401:
5398:
5397:
5393:
5389:
5373:
5370:
5369:
5368:
5365:
5363:
5360:
5358:
5355:
5351:
5348:
5346:
5343:
5342:
5341:
5338:
5337:
5335:
5333:
5329:
5319:
5316:
5312:
5306:
5304:
5298:
5296:
5290:
5289:
5288:
5285:
5284:Nonparametric
5282:
5280:
5274:
5270:
5267:
5266:
5265:
5259:
5255:
5254:Sample median
5252:
5251:
5250:
5247:
5246:
5244:
5242:
5238:
5230:
5227:
5225:
5222:
5220:
5217:
5216:
5215:
5212:
5210:
5207:
5205:
5199:
5197:
5194:
5192:
5189:
5187:
5184:
5182:
5179:
5177:
5175:
5171:
5169:
5166:
5165:
5163:
5161:
5157:
5151:
5149:
5145:
5143:
5141:
5136:
5134:
5129:
5125:
5124:
5121:
5118:
5116:
5112:
5102:
5099:
5097:
5094:
5092:
5089:
5088:
5086:
5084:
5080:
5074:
5071:
5067:
5064:
5063:
5062:
5059:
5055:
5052:
5051:
5050:
5047:
5045:
5042:
5041:
5039:
5037:
5033:
5025:
5022:
5020:
5017:
5016:
5015:
5012:
5010:
5007:
5005:
5002:
5000:
4997:
4995:
4992:
4990:
4987:
4986:
4984:
4982:
4978:
4972:
4969:
4965:
4962:
4958:
4955:
4953:
4950:
4949:
4948:
4945:
4944:
4943:
4940:
4936:
4933:
4931:
4928:
4926:
4923:
4921:
4918:
4917:
4916:
4913:
4912:
4910:
4908:
4904:
4901:
4899:
4895:
4889:
4886:
4884:
4881:
4877:
4874:
4873:
4872:
4869:
4867:
4864:
4860:
4859:loss function
4857:
4856:
4855:
4852:
4848:
4845:
4843:
4840:
4838:
4835:
4834:
4833:
4830:
4828:
4825:
4823:
4820:
4816:
4813:
4811:
4808:
4806:
4800:
4797:
4796:
4795:
4792:
4788:
4785:
4783:
4780:
4778:
4775:
4774:
4773:
4770:
4766:
4763:
4761:
4758:
4757:
4756:
4753:
4749:
4746:
4745:
4744:
4741:
4737:
4734:
4733:
4732:
4729:
4727:
4724:
4722:
4719:
4717:
4714:
4713:
4711:
4709:
4705:
4701:
4697:
4692:
4688:
4674:
4671:
4669:
4666:
4664:
4661:
4659:
4656:
4655:
4653:
4651:
4647:
4641:
4638:
4636:
4633:
4631:
4628:
4627:
4625:
4621:
4615:
4612:
4610:
4607:
4605:
4602:
4600:
4597:
4595:
4592:
4590:
4587:
4585:
4582:
4581:
4579:
4577:
4573:
4567:
4564:
4562:
4561:Questionnaire
4559:
4557:
4554:
4550:
4547:
4545:
4542:
4541:
4540:
4537:
4536:
4534:
4532:
4528:
4522:
4519:
4517:
4514:
4512:
4509:
4507:
4504:
4502:
4499:
4497:
4494:
4492:
4489:
4487:
4484:
4483:
4481:
4479:
4475:
4471:
4467:
4462:
4458:
4444:
4441:
4439:
4436:
4434:
4431:
4429:
4426:
4424:
4421:
4419:
4416:
4414:
4411:
4409:
4406:
4404:
4401:
4399:
4396:
4394:
4391:
4389:
4388:Control chart
4386:
4384:
4381:
4379:
4376:
4374:
4371:
4370:
4368:
4366:
4362:
4356:
4353:
4349:
4346:
4344:
4341:
4340:
4339:
4336:
4334:
4331:
4329:
4326:
4325:
4323:
4321:
4317:
4311:
4308:
4306:
4303:
4301:
4298:
4297:
4295:
4291:
4285:
4282:
4281:
4279:
4277:
4273:
4261:
4258:
4256:
4253:
4251:
4248:
4247:
4246:
4243:
4241:
4238:
4237:
4235:
4233:
4229:
4223:
4220:
4218:
4215:
4213:
4210:
4208:
4205:
4203:
4200:
4198:
4195:
4193:
4190:
4189:
4187:
4185:
4181:
4175:
4172:
4170:
4167:
4163:
4160:
4158:
4155:
4153:
4150:
4148:
4145:
4143:
4140:
4138:
4135:
4133:
4130:
4128:
4125:
4123:
4120:
4118:
4115:
4114:
4113:
4110:
4109:
4107:
4105:
4101:
4098:
4096:
4092:
4088:
4084:
4079:
4075:
4069:
4066:
4064:
4061:
4060:
4057:
4053:
4046:
4041:
4039:
4034:
4032:
4027:
4026:
4023:
4017:
4014:
4012:
4009:
4007:
4004:
4003:
3999:
3994:
3991:
3989:
3986:
3985:
3981:
3976:
3972:
3961:
3957:
3952:
3941:
3937:
3932:
3928:
3922:
3917:
3916:
3910:
3906:
3902:
3898:
3891:
3886:
3885:
3881:
3872:
3869:
3864:
3860:
3855:
3850:
3846:
3842:
3835:
3828:
3826:
3822:
3817:
3813:
3809:
3805:
3801:
3797:
3790:
3787:
3784:London: Sage.
3783:
3777:
3774:
3769:
3765:
3761:
3754:
3751:
3746:
3742:
3737:
3732:
3727:
3722:
3718:
3714:
3710:
3703:
3700:
3695:
3691:
3687:
3683:
3679:
3675:
3674:Field Methods
3668:
3666:
3662:
3657:
3653:
3649:
3645:
3641:
3637:
3633:
3629:
3622:
3615:
3612:
3609:, 12, 436â445
3608:
3602:
3599:
3596:, 18, 179â183
3595:
3589:
3586:
3580:
3577:
3571:
3568:
3562:
3559:
3553:
3550:
3544:
3541:
3535:
3532:
3528:
3523:
3520:
3517:
3512:
3506:
3502:
3495:
3493:
3489:
3484:
3478:
3474:
3468:
3463:
3461:
3457:
3454:
3450:
3447:
3442:
3439:
3434:
3430:
3424:
3421:
3418:
3414:
3410:
3406:
3401:
3398:
3392:
3388:
3385:
3383:
3380:
3378:
3374:
3372:
3369:
3368:
3364:
3358:
3353:
3348:
3346:
3344:
3340:
3334:
3332:
3323:
3307:
3299:
3295:
3287:
3283:
3277:
3273:
3268:
3265:
3258:
3253:
3249:
3241:
3240:
3239:
3225:
3222:
3216:
3212:
3207:
3187:
3164:
3156:
3152:
3144:
3140:
3136:
3130:
3123:
3119:
3113:
3109:
3099:
3098:
3097:
3081:
3077:
3066:
3052:
3049:
3043:
3039:
3034:
3014:
2987:
2977:
2967:
2964:
2959:
2954:
2950:
2927:
2923:
2919:
2916:
2911:
2907:
2902:
2896:
2892:
2883:
2882:sampling rate
2864:
2860:
2852:
2848:
2844:
2839:
2832:
2828:
2824:
2818:
2809:
2799:
2789:
2786:
2781:
2776:
2772:
2766:
2761:
2758:
2755:
2751:
2747:
2739:
2729:
2719:
2716:
2709:
2708:
2707:
2691:
2687:
2683:
2680:
2677:
2657:
2653:
2647:
2643:
2639:
2634:
2630:
2607:
2603:
2595:The weights,
2579:
2571:
2561:
2551:
2548:
2543:
2538:
2534:
2528:
2523:
2520:
2517:
2513:
2509:
2501:
2491:
2481:
2478:
2471:
2470:
2469:
2452:
2447:
2437:
2428:
2424:
2418:
2413:
2410:
2407:
2403:
2399:
2394:
2384:
2373:
2372:
2371:
2369:
2364:
2360:
2358:
2351:
2346:
2342:
2335:
2328:
2324:
2317:
2314:= 1, 2, ...,
2313:
2309:
2302:
2298:
2294:
2286:
2284:
2282:
2241:
2236:
2230:
2226:
2220:
2216:
2207:
2204:
2201:
2193:
2190:
2182:
2177:
2173:
2166:
2161:
2158:
2151:
2150:
2149:
2147:
2128:
2125:
2122:
2119:
2111:
2107:
2103:
2098:
2092:
2088:
2083:
2079:
2075:
2066:
2050:
2049:
2048:
2046:
2039:
2035:
2034:decision rule
2013:
2007:
2003:
1998:
1994:
1967:
1953:
1949:
1948:
1947:
1930:
1927:
1919:
1915:
1911:
1906:
1900:
1896:
1891:
1887:
1883:
1874:
1858:
1857:
1856:
1853:
1849:
1842:
1835:
1831:
1827:
1820:
1816:
1809:
1805:
1784:
1780:
1776:
1773:
1770:
1765:
1761:
1753:
1752:
1751:
1734:
1731:
1728:
1725:
1720:
1716:
1708:
1707:
1706:
1704:
1700:
1696:
1693:= 1, 2, ...,
1692:
1688:
1677:
1675:
1673:
1669:
1665:
1661:
1657:
1649:
1645:
1642:
1639:
1638:control group
1635:
1631:
1627:
1624:
1621:
1617:
1614:
1611:
1608:
1607:
1606:
1589:
1586:
1583:
1580:
1577:
1574:
1571:
1568:
1561:
1560:
1559:
1556:
1554:
1549:
1547:
1543:
1531:
1527:
1526:control group
1523:
1519:
1516:
1513:
1509:
1508:
1507:
1505:
1501:
1500:control group
1497:
1493:
1483:
1480:
1477:
1474:
1473:
1469:
1466:
1463:
1460:
1459:
1455:
1452:
1449:
1446:
1445:
1441:
1438:
1435:
1432:
1431:
1427:
1424:
1421:
1418:
1417:
1413:
1410:
1407:
1404:
1403:
1399:
1396:
1393:
1390:
1389:
1385:
1382:
1379:
1376:
1375:
1371:
1368:
1365:
1364:
1361:
1356:
1349:
1343:
1341:
1339:
1335:
1331:
1322:
1320:
1318:
1302:
1299:
1292:
1288:
1281:
1277:
1273:
1268:
1264:
1260:
1257:
1244:
1243:
1224:
1220:
1213:
1209:
1203:
1199:
1195:
1189:
1186:
1175:
1159:
1155:
1151:
1148:
1142:
1137:
1134:
1124:
1123:
1122:
1120:
1112:
1108:
1092:
1085:
1080:
1077:
1071:
1062:
1055:
1049:
1044:
1041:
1035:
1026:
1019:
1011:
1010:
1009:
1007:
988:
982:
978:
969:
968:
967:
965:
961:
957:
952:
945:
943:
941:
940:sample points
937:
933:
929:
928:opinion polls
925:
921:
917:
913:
909:
905:
901:
897:
893:
889:
885:
881:
877:
873:
857:
853:
849:
846:
840:
837:
831:
810:
803:
800:
794:
791:
785:
782:
775:
769:
766:
760:
757:
751:
748:
741:
731:
729:
709:
705:
697:
694:
691:
685:
680:
676:
672:
666:
663:
643:
639:
635:
632:
626:
619:
616:
613:
607:
600:
590:
587:
586:
584:
581:(Note: W/2 =
562:
558:
552:
548:
542:
539:
527:
523:
521:
502:
498:
494:
491:
485:
482:
476:
469:
468:
467:
466:(e.g., 0.5):
465:
460:
440:
433:
430:
424:
421:
415:
412:
405:
399:
396:
390:
387:
381:
378:
371:
363:
362:
361:
359:
355:
333:
322:
317:
300:
297:
294:
288:
280:
276:
272:
268:
264:
260:
257:
253:
249:
245:
241:
225:
221:
217:
214:
205:
194:
190:
185:
183:
177:
169:
164:
162:
159:
155:
149:
147:
143:
138:
136:
132:
127:
123:
115:
113:
109:
102:
97:
94:
90:
86:
83:
79:
75:
74:
73:
70:
68:
64:
60:
56:
52:
48:
44:
40:
36:
32:
19:
6333:
6321:
6302:
6295:
6207:Econometrics
6157: /
6140:Chemometrics
6117:Epidemiology
6110: /
6083:Applications
5925:ARIMA model
5872:Q-statistic
5821:Stationarity
5717:Multivariate
5660: /
5656: /
5654:Multivariate
5652: /
5592: /
5588: /
5362:Bayes factor
5261:Signed rank
5173:
5147:
5139:
5127:
4822:Completeness
4658:Cohort study
4556:Opinion poll
4510:
4491:Missing data
4478:Study design
4433:Scatter plot
4355:Scatter plot
4348:Spearman's Ï
4310:Grouped data
3977:. Routledge.
3963:. Retrieved
3959:
3945:19 September
3943:. Retrieved
3939:
3914:
3900:
3896:
3871:
3844:
3840:
3799:
3795:
3789:
3781:
3776:
3767:
3763:
3753:
3716:
3712:
3702:
3677:
3673:
3631:
3627:
3614:
3606:
3601:
3593:
3588:
3579:
3570:
3561:
3552:
3543:
3534:
3522:
3500:
3472:
3441:
3433:utdallas.edu
3432:
3423:
3416:
3400:
3335:
3327:
3179:
3067:
2879:
2594:
2467:
2367:
2365:
2361:
2353:
2349:
2344:
2340:
2333:
2326:
2319:
2315:
2311:
2304:
2300:
2296:
2290:
2258:
2143:
2044:
2037:
2031:
1951:
1945:
1851:
1847:
1840:
1833:
1829:
1818:
1814:
1807:
1803:
1801:
1749:
1694:
1690:
1683:
1681:
1671:
1667:
1659:
1655:
1653:
1647:
1643:
1629:
1625:
1619:
1615:
1609:
1604:
1557:
1550:
1540:
1510:The desired
1489:
1330:Type I error
1326:
1316:
1245:
1241:
1179:
1173:
1118:
1116:
1003:
959:
955:
953:
949:
935:
923:
919:
915:
911:
907:
903:
899:
895:
891:
887:
883:
879:
875:
871:
732:
591:
588:
580:
532:
519:
517:
463:
458:
456:
320:
318:
278:
274:
243:
239:
186:
179:
150:
139:
119:
110:
106:
103:Introduction
71:
34:
30:
29:
6335:WikiProject
6250:Cartography
6212:Jurimetrics
6164:Reliability
5895:Time domain
5874:(LjungâBox)
5796:Time-series
5674:Categorical
5658:Time-series
5650:Categorical
5585:(Bernoulli)
5420:Correlation
5400:Correlation
5196:JarqueâBera
5168:Chi-squared
4930:M-estimator
4883:Asymptotics
4827:Sufficiency
4594:Interaction
4506:Replication
4486:Effect size
4443:Violin plot
4423:Radar chart
4403:Forest plot
4393:Correlogram
4343:Kendall's Ï
3903:(1): 43â50.
3802:: 105â121.
1636:(including
910:= 400, for
902:= 100, for
248:independent
6202:Demography
5920:ARMA model
5725:Regression
5302:(Friedman)
5263:(Wilcoxon)
5201:Normality
5191:Lilliefors
5138:Student's
5014:Resampling
4888:Robustness
4876:divergence
4866:Efficiency
4804:(monotone)
4799:Likelihood
4716:Population
4549:Stratified
4501:Population
4320:Dependence
4276:Count data
4207:Percentile
4184:Dispersion
4117:Arithmetic
4052:Statistics
3467:Chapter 13
3393:References
3331:saturation
1670:=0), then
930:and other
892:within ± B
193:proportion
182:proportion
165:Estimation
146:dependence
144:or strong
126:estimating
116:Importance
88:estimator.
51:population
47:inferences
39:replicates
35:estimation
5583:Logistic
5350:posterior
5276:Rank sum
5024:Jackknife
5019:Bootstrap
4837:Bootstrap
4772:Parameter
4721:Statistic
4516:Statistic
4428:Run chart
4413:Pie chart
4408:Histogram
4398:Fan chart
4373:Bar chart
4255:L-moments
4142:Geometric
3940:Qualtrics
3919:. Wiley.
3680:: 59â82.
3382:Cohen's h
3208:∑
3035:∑
2981:¯
2968:
2840:−
2803:¯
2790:
2752:∑
2733:¯
2720:
2684:∑
2565:¯
2552:
2514:∑
2495:¯
2482:
2441:¯
2404:∑
2388:¯
2267:Φ
2231:σ
2221:∗
2217:μ
2208:β
2205:−
2191:−
2187:Φ
2178:α
2162:≥
2129:β
2126:−
2120:≥
2104:∣
2089:σ
2084:α
2070:¯
2004:σ
1999:α
1971:¯
1931:α
1912:∣
1897:σ
1892:α
1878:¯
1785:∗
1781:μ
1774:μ
1729:μ
1584:−
1578:−
1518:Cohen's d
1360:Cohen's d
1274:×
1261:×
1210:σ
1138:σ
1081:σ
1066:¯
1045:σ
1036:−
1030:¯
979:σ
786:^
758:−
752:^
695:−
617:−
416:^
388:−
382:^
337:^
298:−
271:parameter
209:^
189:estimator
122:precision
6351:Category
6297:Category
5990:Survival
5867:Johansen
5590:Binomial
5545:Isotonic
5132:(normal)
4777:location
4584:Blocking
4539:Sampling
4418:QâQ plot
4383:Box plot
4365:Graphics
4260:Skewness
4250:Kurtosis
4222:Variance
4152:Heronian
4147:Harmonic
3911:(1965).
3909:Kish, L.
3863:59047474
3816:62179911
3745:21612639
3694:62237589
3656:28152749
3648:20204937
3449:Archived
3409:SEMATECH
3349:See also
3269:′
2942:, where
2339:+ ... +
2318:. These
1950:'Reject
267:variance
238:, where
133:and the
49:about a
6323:Commons
6270:Kriging
6155:Process
6112:studies
5971:Wavelet
5804:General
4971:Plug-in
4765:L space
4544:Cluster
4245:Moments
4063:Outline
3965:29 June
3770:(3): 8.
3736:3120635
2363:costs.
1946:and so
1628:is the
1618:is the
1605:where:
1317:minimum
1111:Z-score
6192:Census
5782:Normal
5730:Manova
5550:Robust
5300:2-way
5292:1-way
5130:-test
4801:
4378:Biplot
4169:Median
4162:Lehmer
4104:Center
3923:
3861:
3814:
3743:
3733:
3719:: 36.
3692:
3654:
3646:
3507:
3479:
3180:where
2259:where
1498:and a
1352:
1344:Tables
962:, the
894:. For
886:where
256:sample
59:census
5816:Trend
5345:prior
5287:anova
5176:-test
5150:-test
5142:-test
5049:Power
4994:Pivot
4787:shape
4782:scale
4232:Shape
4212:Range
4157:Heinz
4132:Cubic
4068:Index
3893:(PDF)
3859:S2CID
3837:(PDF)
3812:S2CID
3690:S2CID
3652:S2CID
3624:(PDF)
2468:with
2032:is a
1826:power
1817:when
1522:means
1475:0.99
1461:0.95
1447:0.90
1433:0.80
1419:0.70
1405:0.60
1391:0.50
1377:0.25
1355:Power
1334:power
1303:96.04
191:of a
158:power
124:when
6049:Test
5249:Sign
5101:Wald
4174:Mode
4112:Mean
3993:ASTM
3967:2019
3947:2018
3921:ISBN
3741:PMID
3644:PMID
3505:ISBN
3477:ISBN
3405:NIST
3007:and
2076:>
1884:>
1682:Let
1542:Mead
1372:0.8
1265:1.96
838:0.25
801:0.25
795:1.96
767:0.25
761:1.96
518:for
483:0.25
431:0.25
397:0.25
259:mean
187:The
5229:BIC
5224:AIC
3849:doi
3804:doi
3731:PMC
3721:doi
3682:doi
3636:doi
2965:Var
2787:Var
2717:Var
2549:Var
2479:Var
1484:58
1481:148
1478:920
1470:42
1467:105
1464:651
1456:34
1450:526
1442:26
1436:393
1428:20
1422:310
1414:16
1408:246
1400:13
1394:193
1369:0.5
1366:0.2
273:is
195:is
33:or
6353::
3958:.
3938:.
3901:19
3899:.
3895:.
3857:.
3845:18
3843:.
3839:.
3824:^
3810:.
3800:41
3798:.
3768:11
3766:.
3762:.
3739:.
3729:.
3717:11
3715:.
3711:.
3688:.
3678:18
3676:.
3664:^
3650:.
3642:.
3632:25
3630:.
3626:.
3491:^
3459:^
3431:.
3415:,
3411:,
3345:.
3096::
3065:.
2706:,
2348:=
2332:+
2310:,
2283:.
2058:Pr
1866:Pr
1705::
1689:,
1453:85
1439:64
1425:50
1411:40
1397:32
1386:6
1383:14
1380:84
1340::
1278:15
585:.)
137:.
5174:G
5148:F
5140:t
5128:Z
4847:V
4842:U
4044:e
4037:t
4030:v
3969:.
3949:.
3929:.
3865:.
3851::
3818:.
3806::
3747:.
3723::
3696:.
3684::
3658:.
3638::
3513:.
3485:.
3435:.
3407:/
3308:.
3300:h
3296:C
3288:h
3284:S
3278:h
3274:W
3266:K
3259:=
3254:h
3250:n
3226:n
3223:=
3217:h
3213:n
3188:K
3165:,
3157:h
3153:C
3145:h
3141:S
3137:K
3131:=
3124:h
3120:N
3114:h
3110:n
3082:h
3078:C
3053:n
3050:=
3044:h
3040:n
3015:k
2993:)
2988:h
2978:x
2971:(
2960:=
2955:h
2951:S
2928:h
2924:S
2920:k
2917:=
2912:h
2908:N
2903:/
2897:h
2893:n
2865:,
2861:)
2853:h
2849:N
2845:1
2833:h
2829:n
2825:1
2819:(
2815:)
2810:h
2800:x
2793:(
2782:2
2777:h
2773:W
2767:H
2762:1
2759:=
2756:h
2748:=
2745:)
2740:w
2730:x
2723:(
2692:h
2688:n
2681:=
2678:n
2658:N
2654:/
2648:h
2644:N
2640:=
2635:h
2631:W
2608:h
2604:W
2580:.
2577:)
2572:h
2562:x
2555:(
2544:2
2539:h
2535:W
2529:H
2524:1
2521:=
2518:h
2510:=
2507:)
2502:w
2492:x
2485:(
2453:,
2448:h
2438:x
2429:h
2425:W
2419:H
2414:1
2411:=
2408:h
2400:=
2395:w
2385:x
2368:H
2356:h
2354:n
2350:n
2345:H
2341:n
2337:2
2334:n
2330:1
2327:n
2322:h
2320:n
2316:H
2312:h
2307:h
2305:n
2301:H
2297:H
2242:2
2237:)
2227:/
2211:)
2202:1
2199:(
2194:1
2183:+
2174:z
2167:(
2159:n
2123:1
2117:)
2112:a
2108:H
2099:n
2093:/
2080:z
2067:x
2061:(
2045:Ό
2041:a
2038:H
2028:'
2014:n
2008:/
1995:z
1968:x
1955:0
1952:H
1928:=
1925:)
1920:0
1916:H
1907:n
1901:/
1888:z
1875:x
1869:(
1852:α
1848:z
1844:0
1841:H
1837:0
1834:H
1830:ÎČ
1822:a
1819:H
1815:ÎČ
1811:0
1808:H
1804:Ό
1777:=
1771::
1766:a
1762:H
1735:0
1732:=
1726::
1721:0
1717:H
1695:n
1691:i
1686:i
1684:X
1672:E
1668:B
1666:(
1660:N
1656:T
1644:E
1626:T
1616:B
1610:N
1590:,
1587:T
1581:B
1575:N
1572:=
1569:E
1532:.
1300:=
1293:2
1289:6
1282:2
1269:2
1258:4
1242:.
1225:2
1221:W
1214:2
1204:2
1200:Z
1196:4
1190:=
1187:n
1176::
1174:n
1160:2
1156:/
1152:W
1149:=
1143:n
1135:Z
1119:n
1106:,
1093:)
1086:n
1078:Z
1072:+
1063:x
1056:,
1050:n
1042:Z
1027:x
1020:(
989:.
983:n
960:Ï
956:n
936:n
924:n
920:B
916:n
912:B
908:n
904:B
900:n
896:B
888:B
884:B
880:W
876:n
872:n
858:2
854:/
850:W
847:=
841:n
832:4
811:)
804:n
792:+
783:p
776:,
770:n
749:p
742:(
710:2
706:W
701:)
698:p
692:1
689:(
686:p
681:2
677:Z
673:4
667:=
664:n
644:2
640:/
636:W
633:=
627:n
623:)
620:p
614:1
611:(
608:p
601:Z
563:2
559:W
553:2
549:Z
543:=
540:n
520:n
503:2
499:/
495:W
492:=
486:n
477:Z
464:p
459:n
441:)
434:n
425:Z
422:+
413:p
406:,
400:n
391:Z
379:p
372:(
334:p
321:n
304:)
301:p
295:1
292:(
289:p
279:p
275:p
244:n
240:X
226:n
222:/
218:X
215:=
206:p
84:.
20:)
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