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TEAS allows you to save your work as a portable form at the Validation Page. This page appears at the end of the application after you have entered all your information in the form. To download and save the form data, you must click on the \"Download Portable Data\" button at the bottom of the Validation Page. The information will be saved to your local drive. To begin the submission process with saved data, you must open a new form, and click on the \"Browse/Choose File\" button displayed on the initial form wizard page, at \"[OPTIONAL]\" To access previously-saved data, use the \"Browse/Choose File\" button below to access the file from your local drive.\" Clicking on the \"Continue\" button at the bottom of the first page will then properly open the saved version of the form.Alternatively, you may create a \"dummy\" form and enter the \"real\" data at a later point. You can create a \"dummy\" form by (1) pulling up the regular TEAS form; (2) entering either an \"x\" for any mandatory field, or \"fake\" numbers where a numerical entry is required, or attaching a \"dummy\" image file; (3) validating this version of the form; (4) using the button on the bottom of the validation page to save the form to your local drive; (5) pulling up the saved form, and (6) entering \"real\" data for any field wherein an \"x\" was initially used as a \"data placeholder.\" WARNING: If you create a \"dummy\" form, and the USPTO then later upgrades the form, the saved template may not be usable for future filings, and a new template based on the upgraded version of the form may have to be created.
Well, it's actually pretty much the same as for the random intercepts model. So for the fixed part, our estimate of βk is significant at the 5% level if the modulus of βk divided by its standard error is >1.96, so that's also just the same as the single level regression model. For the random part, again we need to use the likelihood ratio test, so this time we're comparing the model with u1x1 to the model without u1x1. So we're basically just comparing the random slope model, that's the model with u1x1, with the random intercept model, that's the model without u1x1. So those two models are exactly the same in terms of explanatory variables, the only difference is that the random slopes model has this u1x1in it. So again the test statistic is 2 times the log likelihood of the first model minus the log likelihood of the second model and this time we have 2 degrees of freedom because we've got 2 extra parameters in the first model compared to the second model: we've got σ2u1 this time and σu01. So we compare the test statistic against the 2(2) distribution with 2 degrees of freedom, and again we can divide the p-value by 2 if we want. So the null hypothesis is that σ2u1 and σu01 are both 0 and if that's the case than a random intercept model would be more appropriate than a random slope model. 153554b96e
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