How Do I Know What Model My Jvc Tv Is
Consuelo Dular
I want to know which model (BSIM3.3, BSIM4.6, BSIM SOI 4.0 or EKV 2.6) of transistor (MOS) is being used by the simulator .how can i do that . Further how can i change the model to EKV 2.6 if possible. what are the steps to be followed.
Changing them to EKV will involve you having to provide a different set of model files (you'd have to create these yourself) and reference the models in Setup->Model Libraries in ADE. Given that model files are "characterized" (although in GPDK it's an artificial process, so they're effectively made up), that's not a trivial task unless you have some EKV models from elsewhere.
I am a beginner user of JMP since I have recently finished the DOE course provided by Coursera. However, I have to use it in my project and so, I am trying to use JMP community forums to find my responses. But, in this case, I could not find the response that I am looking for.
I have a 2-level full factorial design with 3 variables and with 2 center points. I considered 2 replicates and therefore, totally 20 runs. I made the data table with DOE > Classical > Full factorial design. For analyzing my data: Analyze > Fit model, Personality: Standard Least Squares, Emphasis: Effect Screening. My purpose was to know if my variables are fitted in a linear model or non-linear model? Because in Minitab, I think using "Analysis of Variance", the "quadratic term" shows if it is significance or not. I tried different Macros is JMP, but no one shows any sign of linearity or non-linearity as I knew.
Ok, the values for the response make a lot more sense now and I'm able to reproduce your results. So based on these values and on your questions, I think you might be interested in general on how to validate your regression model, and what to take care of. There are great ressources about this topic to dive deeper :
You can also check multicollinearity by displaying Variance Inflation Factor (VIF) of the terms in your model : go into the "Parameter Estimates" panel, right click on the table displaying results, click on "Columns", and then choose "VIF". High VIFs indicate a collinearity issue among the terms in the model. While multicollinearity does not reduce a model's overall predictive power, it can produce estimates of the regression coefficients that are not statistically significant, as the inputs are not independent. In your case, nothing to worry about, VIFs are equal to 1, so effect terms are not correlated :
As mentioned earlier, the Lack of Fit test can be helpful to assess if your model fits the data well or not. If not (indicated by a statistically significant p-value), this may be an indication that a term could be missing in the model (like an higher order term, interaction or quadratic effect), or that some terms might have incorrectly been added in the model.
There are several metrics and statistics helping you assess if your model seems reliable and relevant, depending on your objective : Statistical significance of the model (Analysis of Variance (jmp.com)), Summary of Fit (with R, R adjusted, RMSE), and possibly other indicators accessible through other platforms (like Information criteria with Generalized Regression platform)...
Note that you can find several equally fitting models, so using Domain Expertise in combination with Statistics can help you compare and select the most interesting model(s). Just for illustration, I have saved 2 scripts in your datatable showing 2 similar but different models (with their prediction formula added in the table):
It seems the statistical significance of the terms in the model is somehow reversed compared to what you obtained ? I am not able to find similar results as you have. Has the table changed between the file you send and the screenshot you send ?
I also added a new response column "Area of Sticking (formula)" based on your outputs to create data that could closely match what you obtain. So even if there was a problem in the data collection/recording, you can fit a model using this column to have close results compared to what you have (script "Fit Least Squares Area of Sticking (formula)").
Sorry, the screenshot about my data table was wrong. But the .jmp file was correct. Because first I tried to attached both files for data table and the analysis, but I could only attach data table. So, please don't consider the previous screen shot about my data table.
To me, the first option makes sense. The job of the Controller is to co-ordinate between the view and the model. From that point of view, it makes sense for the controller to be the one that controls references to the view and model.
You can't really have a controller without a Model and a View, however it makes a lot more sense to just have a View or just have a Model (for example, in unit testing). That's why you want to pass in those dependencies into the Controller, and not the other two.
Don't think of the Controller as the brains of the MVC structure. Think of it as the dispatcher which handles the requests from the browser, and dispatches them to the Model. It then takes the data from the Model and packages it in a template friendly way, and then sends it to a View.
The Model is the brains in the MVC structure, and this is where you should put your business rules. Business rules are common across multiple controllers. So a document controller, and a reports controller may both use a User model to see who has access to those things. You wouldn't want to repeat those rules in both controllers.
The View should use a HTML template to present the data in a non-datasource specific way. It should not be tightly bound to the schema of your database. To show the title of a document you would have the view output the contents of a template variable called document_title, and only the Controller knows how that variable was set, and only the Model knows why that document has that title.
MVC was originally defined to ease the programming of desktop applications. The view subscribed to model events, updating the presentation when the model changed. The controller merely translated user interface events (e.g. a button press) into calls to the model. So the controller and view depended on the model, but were independent of each other. The model was independent of both. This allowed multiple views and controllers to work on the same model.
The "MVC" architecture used for web 1.0 applications (full page refresh, no AJAX) is somewhat different. A web request is dispatched to a controller. The controller somehow modifies the model state, then sends one or more models to be rendered by a view. The controller and view both depend on the model, but the controller also depends on the view.
With web 2.0 applications, we are returning to the classic MVC architecture, on the client side. The model, view, and controller all reside on the client side as Javascript objects. The controller translates user events to model actions. The model actions may or may not result in an AJAX request to the server. Again, the view subscribes to model events and updates the presentation accordingly.
The view should subscribe to changes in the model. There is latitude in the richness of subscriptions as they may be detailed (show me inventory changes for this particular item) or generic (the model has changed); the view may query the model in response to change notification. The view presents its desired set of model elements on the screen, updating the screen as when handling change notifications.
There also needs to be a mechanism to create new views and controllers (since in MVC you should be able to have two or more views of the same model (they could be the same view(point) or different view(point)s). Logically, we can consider that the controller needs to perform or have access to a view & controller (pair) factory, which can be part of the controller or another component.
MVC is more like a modularity pattern. Its purpose is that whenever you want to change the user interface layout (view), you don't have to change de application logic (controller) or the internal data processings (model).
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