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Please login to add this item to your cart, or create an account below. Previous Next. Price: Add to Cart. Gift this Item. Add to Wishlist. ReadMe File. HeadShop This site uses cookies to deliver the best experience. Our own cookies make user accounts and other features possible. Third-party cookies are used to display relevant ads and to analyze how Renderosity is used.

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HeadShop 11 requires a Windows-based personal computer or laptop with the following minimal requirements:. HeadShop 11 features a number of breakthrough technologies that cannot be found in other similar programs:. The benefits of membership: Gain access to your control panel and to the box personal messenger Extended profiles with fully editable profile fields The ability to modify their account and notification settings directly from within their profiles Available search The ability to add posts to the blog and write comments.

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From the Blog.Tags: discountLaFemmePosersalesoftwareutility. Create 3D heads from photographs, and merge two photographs to create one all new 3D head! HeadShop has been in development since and regularly introduces features not found in other photo-to-3D software. No article comments have been posted. Sign In or Register to share your thoughts. Poser 12 Update: New features include new render engine, Material Room overhaul and more.

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Staff Pick Bundles Gaming Content. All Vendors Exclusive Vendors. Free Stuff Departments Contributors. Free Tutorials Upload Free Stuff. Tutorials Story of Rendo. All Contributors.Interpretation of Contingency Measures. An important disadvantage of measures of contingency (reviewed above) is that they do not lend themselves to clear interpretations in terms of probability or "proportion of variance," as is the case, for example, of the Pearson r (see Correlations).

There is no commonly accepted measure of relation between categories that has such a clear interpretation.

Statistics Based on Ranks. Suppose we asked a sample of respondents to indicate their interest in watching different sports on a 4-point scale with the explicit labels (1) always, (2) usually, (3) sometimes, and (4) never interested. Obviously, we can assume that the response sometimes interested is indicative of less interest than always interested, and so on.

Thus, we could rank the respondents with regard to their expressed interest in, for example, watching football.

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When categorical variables can be interpreted in this manner, there are several additional indices that can be computed to express the relationship between variables. Detailed discussions of the Spearman R statistic, its power and efficiency can be found in Gibbons (1985), Hays (1981), McNemar (1969), Siegel (1956), Siegel and Castellan (1988), Kendall (1948), Olds (1949), or Hotelling and Pabst (1936).

Kendall tau is equivalent to the Spearman R statistic with regard to the underlying assumptions. It is also comparable in terms of its statistical power. However, Spearman R and Kendall tau are usually not identical in magnitude because their underlying logic, as well as their computational formulas are very different.

Siegel and Castellan (1988) express the relationship of the two measures in terms of the inequality:More importantly, Kendall tau and Spearman R imply different interpretations: While Spearman R can be thought of as the regular Pearson product-moment correlation coefficient as computed from ranks, Kendall tau rather represents a probability.

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Specifically, it is the difference between the probability that the observed data are in the same order for the two variables versus the probability that the observed data are in different orders for the two variables.

Kendall (1948, 1975), Everitt (1977), and Siegel and Castellan (1988) discuss Kendall tau in greater detail. Two different variants of tau are computed, usually called taub and tauc. These measures differ only with regard as to how tied ranks are handled. In most cases these values will be fairly similar, and when discrepancies occur, it is probably always safest to interpret the lowest value.

The Gamma statistic is preferable to Spearman R or Kendall tau when the data contain many tied observations. Thus, Gamma is basically equivalent to Kendall tau, except that ties are explicitly taken into account.

Detailed discussions of the Gamma statistic can be found in Goodman and Kruskal (1954, 1959, 1963, 1972), Siegel (1956), and Siegel and Castellan (1988). Multiple response variables or multiple dichotomies often arise when summarizing survey data.

The nature of such variables or factors in a table is best illustrated with examples. As part of a larger market survey, suppose you asked a sample of consumers to name their three favorite soft drinks.

Also, a wide variety of soft drinks will most likely be named. The next question is how to enter the responses into a data file. Suppose 50 different soft drinks were mentioned among all of the questionnaires. This method of coding the responses would be very tedious and "wasteful. Alternatively, we could set up three variables, and a coding scheme for the 50 soft drinks. Then we could enter the respective codes (or alpha labels) into the three variables, in the same way that respondents wrote them down in the questionnaire.

To produce a table of the number of respondents by soft drink we would now treat Resp. Note that the counts in the first column of the table do not add up to 500, but rather to 842. For example, referring back to the sample listing of the data file shown above, the first case (Coke, Pepsi, Jolt) "contributes" three times to the frequency table, once to the category Coke, once to the category Pepsi, and once to the category Jolt.

The second and third columns in the table above report the percentages relative to the number of responses (second column) as well as respondents (third column). Thus, the entry 8.

Suppose in the above example we were only interested in Coke, Pepsi, and Sprite. As pointed out earlier, one way to code the data in that case would be as follows: COKE PEPSI SPRITE. In other words, one variable was created for each soft drink, then a value of 1 was entered into the respective variable whenever the respective drink was mentioned by the respective respondent.They will barely return an email.

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They have asked for the same items over and over for months, even once they had the information they were asking for earlier in the same email chain. The only reason I still have the account is because they are so slow on porting out the numbers, that I have no choice but to keep paying.

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