Inferential Statistics, however, helps in understanding how the various variables are related and if the relationship that pertains amongst them is significant or not. This includes watching over the mean, mode or median along with the averages and graphical plots for the vast information that the data frame entails. It has made us, as analysts or as curious folks look at the highly complex data sets and get to know a lot about it in a single glance. Descriptive statistics have helped to make the descriptions of our data sets very easy. We start analyzing data while simultaneously deriving statistical reports, Descriptive and Inferential being the two forms for the same. To kick off with understanding the intricate details of this concept, let’s start from the very beginning. While exploring the data, one of statistical test we can perform between churn and internet services is chi-square - a test of the relationship between two variables - to know if internet services could be one of the strong predictors of churn. One of the variables we have got in our data is a binary variable (two categories 0,1) which indicates whether the customer has internet services or not.
Overall, high level simulations, excel add-in, and pricing are the drawbacks of JMP.Let’s think of a scenario - we are looking to build a predictive model which will predictive the probability of a telecom customer attrition. Also, JMP is quite expensive for small business, the pricing is suitable for midsize and large companies.
SAS JMP CUSTOMER CHURN ANSWERS SOFTWARE
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. Overall JMP is must use tool for statisticians.Ĭons: JMP doesn't provide any tools to analyze data using high level simulations.
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. Pros: JMP comes with lots of feature and functionality.