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One-Way Chi-Square in .NET Generator GTIN - 12 in .NET One-Way Chi-Square




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One-Way Chi-Square using vs .net topaint upc-a supplement 5 for asp.net web,windows application Overview of GS1 General Specification These two variables will appear in the new data set. Figure 27.5. The Select Data tab of the Query screen. tab currently active. Drag bo UPC A for .NET th id and destination into the Select Data panel.

This is shown in Figure 27.5. Click the Filter Data tab.

Drag destination, the variable which we wish to lter, to the Filter Data panel. This action automatically opens the Edit Filter dialog screen. Set the Operator to Not equal to and set the Value equal to 2 as shown in Figure 27.

6. Click OK to return to the main Query window and click Run to execute the procedure. The resulting data set is shown in Figure 27.

7. Our ltered data set, still sorted by destination, now contains only Destinations 1 and 3. Although we cannot see the full data set on the screen, we have taken a screenshot of a location toward the middle of the data set.

You can see that the id code jumps from 61 (the last data point typed into the le representing a student endorsing Category 1) to 77 (the. Figure 27.6. We have edited the lter to select the values of destination that are not equal to 2. Note that the id codes jump f GS1 - 12 for .NET rom 61 to 77 because those students who chose Cancun (Category 2) are not in this filtered data set..

Figure 27.7. A portion of the data set with those students endorsing Category 2 excluded. One-Way Chi-Square Figure 27.8. The output of the two-category chi-square analysis. rst data point typed into th Universal Product Code version A for .NET e le representing a student endorsing Category 3). We are thus ready to perform the follow-up chi-square analysis on this newly created data set.

. 27.5.3 Performing the chi-square analysis We perform this analysis exac .NET UPC-A tly as we described the process in Section 27.3.

We will therefore not present any screenshots here, as they are identical to the ones we have shown earlier.. 27.6 Comparing the two most preferred categories: chi-square output The output of the chi-square visual .net upc a analysis is presented in Figure 27.8.

The upper table provides the observed frequencies for each category and their percentages of the total. Given our coding scheme, we can see that Panama City Beach and South Padre Island were selected by 61 and 36 students, respectively, matching our previous output. Because we have only those cases in the data set, their respective percentages are now 62.

89% and 37.11%. In the lower table we see the chi-square statistics.

Against the null hypothesis of equal cell frequencies, the chi-square value is 6.4433. With 1 df (two categories have.

Nonparametric Procedures 1 df), that chi-square value Visual Studio .NET UCC - 12 is likely to occur with a probability (Pr > ChiSq) of < .0111 if the null hypothesis is true, which is statistically signi cant.

We can therefore conclude that Panama City Beach was a more preferred spring break destination over South Padre Island when the three destinations of Panama City Beach, Cancun, and South Padre Island were offered to students.. 28 Two-Way Chi-Square 28.1 Overview A chi-square test can be appl ied to two-way designs as well as to the one-way designs we covered in 27. The simplest two-way design is a 2 2 and we illustrate it in Figure 28.1.

Assume we asked business travelers which of two attributes they valued most in a hotel when they were traveling on business. The row and column variables each have two levels, and the uppercase letters in the cells represent the observed frequencies. Each row and column has a total frequency (e.

g., A + B is the total number of women in the study), and the total sample size (N) is the sum of all cell frequencies. Frequency tables such as we have drawn in Figure 28.

1 are called contingency tables. This is because the observed frequency is contingent on two (or more) conditions. For example, the frequency of selecting location over service may depend (be contingent) on whether the business traveler is a woman or a man.

In two-way contingency tables, such as shown in Figure 28.1, the null hypothesis on which the expected frequencies is based can be stated in several different ways:. r r r r Preference for hotel location and service is independent of (unrelated to) the gender of the traveler. The variables of hotel attribute and gender are independent (not related). Women and men business travelers have comparable preferences for hotel location and service.

The proportion of women preferring location to service is not statistically different from the proportion of men preferring location to service.. The last bullet in our list o VS .NET upc barcodes f alternative ways to express the null hypothesis captures the general strategy of deriving the expected cell frequencies. Speci cally, we would follow these steps to derive the expected frequencies: 279.

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