The labs are heavily simulation-based - we introduce estimation and testing through bootstrapping and randomization procedures. Most students take only the first course in the two-part sequence, which introduces exploratory data analysis and the simplest cases of estimation and hypothesis testing. Overall, high level simulations, excel add-in, and pricing are the drawbacks of JMP.I am a lab TA for an introductory stat sequence that is geared toward students who will ostensibly need to produce statistical results rather than just consume them - mostly students in the biological and social sciences. Also, JMP is quite expensive for small business, the pricing is suitable for midsize and large companies. JMP's parent company SAS follows different set of integration codes because of which the intergration with most large scale software for data access becomes a challenge. Other software integrations for JMP are quiet challenging. Loading large amount of data is very tedious as it takes lot of time and it crashes very frequently. Error and bug fixing requires exhaustive customer support and it is very time consuming. It is easy to upload an excel sheet in JMP, but the excel add-in takes more time to work, sometimes it even fails to operate. ![]() For example, JMP sometimes fails to recognize the difference between number and a string. Variable value designation is a big problem in JMP, the software fails to recognize the type of data when it comes to numeric value. The available simulations are basic simluations which are capable to produce certain amount of data predictions. JMP doesn't provide any tools to analyze data using high level simulations. Overall JMP is must use tool for statisticians. New methods like segmentation and conjoint analysis are also available in JMP. The profiler component of the JMP is very useful to share analysis and reports using web interface. The results displayed are very good graphic display, easy to save and copy graphs other documents. Drag and drop functionality is very useful when it comes to handling lots of data. The dashboard application makes it very easy for the user to carry out the complex operations for the advance multivariate methods. For multivariate statistics JMP is a good tool when it comes to analyzing data using linear regression, ANOVA, MANOVA, and logistic regression. The tool accepts data in different formats csv, xlsx, and dat format. Data mining, data cleaning, and descriptives statistics can be very easily performed on JMP. It has a dashboard which is easy to navigate and feature enabled. Anybody with no experience and training can use JMP with ease. ![]() It is is one of the statistical package software which doesn't require coding skills. It is very easy to use and requires no prior experience. ![]() JMP comes with lots of feature and functionality. Reports are very interactive and easy to export. Easy to operate method mechanism and drag and drop function through dashboard is a plus. JMP gives full control and flexibility over data manipulation and analysis. For statisticians who are involved in industrial applications, JMP is a option for them. ![]() Overall JMP is a very good statistical tool in it features and functionalities.
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