ANOVA and Spearman rho interpretation (Minitab)

Looking for a little help interpreting some data I have produced using Minitab for my final year dissertation (I study Wildlife Conservation and Zoo Biology). I am trying to correlate some primate biological traits with their extinction risk (LC – least concern, NT – near threatened, VU – vulnerable, EN – endangered, CR – critically endangered). For categorical data, a one-way ANOVA with a boxplot of data was carried out. For numerical data, a simple scatterplot was created and a Spearman rho correlation. All statistical analyses used a p-value of 0.05 to find statistical significance. I used Minitab 18. I am unsure of how to correctly interpret my results… My graphs show an overlap in the results however my p-values are significant, so at this point, I am really confused and am sure if it is my interpretation of the results or if the initial input is what is incorrect. If someone could kindly nudge me in the right direction that would be great. ​ Here is a box plot of diet and extinction risk… Stats: Source DF F-value P-value Diet 5 4.47 0.001 Error 150 Total 155 ​ Here is a scatter plot of average body size and extinction risk… Stats: P-value 0.007 Spearman rho 0.235 submitted by /u/lauralottie [link] [comments]

Stationary data, ADF test and interpretation, first difference ,OLS regression?

I am currently working my master’s thesis and am studying bitcoin price determinants using OLS regression. My statistics level is pretty much at a beginner level. I have gave stationary tests a go however I am a bit unsure as to what I am looking for. I have attached screenshots of the ADF test. I hope you can help me understand this. In the ADF test menu, do I need to concern myself with the lag order? I am not quite sure what that means. What is the truncation lag? In the ADF test, my original log of bitcoin prices (lnBitcoinprice) p-value was 0.33397 meaning its non-stationary because its greater than 0.05? And once I took the first difference of lnBitcoinprice, the p-value is now 0.01 meaning its stationary? Once I do check all the variables separately, do I put the first difference variables in the regression model instead of the original? Is it ok if I use the first difference of a log transformed variable? Thanks submitted by /u/maiq1112 [link] [comments]

Cholesky & EigenValue Decomposition: Dealing with Non-Full Rank Covariance Matrix

Hey folks, I am trying to calculate the density of an Item Response model by using a non-full rank covariancematrix. Because the matrix is not invertible, due to its non-full rank nature, the Cholesky Decomposition cannot be calculated. As such it is also not possible to draw samples from density and calculate. Strangely when using the dvnorm package, the non-full matrix can be decomposed using an eigenvalue decomposition, but the density can still not be calculated. I was curious if there is a workaround when dealing with non-full Rank matrixes to calculate the Density. I would assume either 1) there is a way to invert such a matrix with a few tweaks and then use the Cholesky Decomposition or 2) use an alternative decomposition to calculate the density. Any suggestions? submitted by /u/dnzsn [link] [comments]

I am starting an MS in Statistics soon, and I need some advice

Hi everyone! I hope you all you are doing well. Just a little about myself, I am currently a senior in statistics. I was notified last week that I got accepted to the MS Statistics at the same university I am going to now. I have taken quite a few statistics courses from a semester-long intro applied statistics, probability theory, mathematical statistics to grad-level statistical programming, regression, and multivariate statistics, but no intro proofs nor analysis. I do get some A (mostly undergrad and programming classes, more B in grad classes so far), but overall, I believe I am more like a B student (GPA ~ 3.2). As I worked hard and got along well with my professors, I got into a year-long research project, sponsored by a local company, under my department. Initially, I just wanted to pursue an MS in statistics because I love statistical concepts so much that I want to learn more and get a satisfying data job ultimately. However, thanks to this project, I realized that I really enjoy research and consider doing a PhD, and possibly having a research job in the future. I am having some concerns that I hope that you all can give me some advice. I am still debating whether I should include a sequence of intro proofs – elementary analysis in my study or just take as many statistics courses as I can (the program itself is more applied, so it offers a wide range of statistical courses). I asked two professors regarding this. Both told me that there are programs that are more applied, and like students to have many statistics courses. One of them told me that having analysis will certainly look better (which I’ve heard a lot). The other one suggested me to take many statistics, and said that it is better to let the statistical topics point me to the math I need, rather than concerning about equipping myself with a lot of overkilled math. I feel like I am more into this advice. Given my subpar GPA, when I looked at some programs that seem more applied, like UC Santa Cruz, I thought they may be more reachable for me. Plus, if I could not make to a PhD program, having many statistics courses can give me a safe way out to the industry. I wonder what you guys think about this. And it would be great if you could recommend some schools for me. Currently, there are 2 professors, that are willing to help me to finish the Masters’ thesis. I actually enjoy both of their works, and really want to gain more research experience. I am thinking about working with one for the thesis, and one for side research. The reason is that one of them told me she will be okay if I do research with her without any class credits or thesis, although she does warn me that it can be too much, given my coursework and teaching job. I actually have a mixed feeling about this. I really want to use research experience to make up my subpar GPA, but I am not sure if they will be happy if I have two things at the same time. They are all great professors, and I don’t want to lose good relationships with them. And to be honest, I am sure not if I can handle both. Please give me some advice on this. I actually asked pretty similar questions at other places, but no one replied. In general, I find our community is very willing to help. I hope you all can help me. Thank you so much. submitted by /u/fantasticsky_hng [link] [comments]

Logistic Regression: Negative DV Coefficient & Negative IV Coefficient


I created a simple Logistic Regression and my intercept coefficient is -1.349, while my IV coefficient (binary as well) is -.527.

  • Can I interpret this as a positive relationship between my DV & IV since they’re both negative?
  • I was advised to turn log odds into odds by exp(log odds). Do I do this for both my intercept and IV or just my IV?
  • I read on wikipedia, to turn log odds into probability, I would use this formula: 1/(1+exp(-(intercept + coefficient*IV))) is this correct? I would prefer to use probability than odds.

Additional information: I’m using excel with a XLSTAT plug in.

I just started learning regression so I apologize if this question is elementary.

submitted by /u/Calvin_klein_2593
[link] [comments]