Exactly what the title says. I’m pretty far behind in the math curriculum and my latest test went pretty bad. I want to get my grades up, any suggestions? I realize it’ll take hard work, but are there good ways to catch up quickly before exams?
Very novice in SPSS and statistics and I have a question please help. I have run two one-way anovas. One is running Location (Melbourne, Queenstown and Mumbai)/Creativity and the other is running (Manager, Factory workers, and Customer Care)/Creativity.
both results come up with statistically significant results. The post hoc for the first one reveals Melbourne has the highest mean (with the other two grouped together). The post hoc for the second one reveals Managers have the highest mean (with factory workers and customer care grouped together). The sig for both tests are p>0.05.
Do i need to correct the sig level to reduce type one error (family wise error rate)? as in do something like add their p values together then divide by two (because there are two tests). I’m confused because their p-values are really low anyway. Or, do you only do that for a t-test?
Thanks from a lost statistic student
I have a very hard time with the spatial reasoning aspect of physics and higher calculus. I’m fine with the math, but as soon as I have to draw graphs of 3d objects, deal with coordinate or bearing problems, or convert the data from word problems into a picture I just get lost. I make up for it by doing tons of extra problems, but I feel like this lack of skill is hurting my understanding of the material and honestly my enjoyment of the subjects.
I’ve heard that this skill can be learned, so I’m wondering if anyone has any tips, excercises or advice on how to improve this skill.
I want to build a classifier for 2 classes, Normal (17 samples) vs. Disease (19 samples). And with this small dataset, I divided it to 30 samples for training, 6 samples for test, using createDataPartition() of Caret package in R. After training the model with 10-fold cv using Random Forest, I got a final accuracy value of 1 (classified 6 test samples correctly), but the confidence interval of the accuracy was broad (0.5407 – 1). Does this mean the size of test samples is too small?
I just read about Cayley Hamilton’s Theorem. Although beforehand I’ve always seen it in the context of Linear Algebra I have never seen an Abstract Algebra formulation of it.
The version I read was: If R is a ring, I is an ideal of R, and M a finitely generated R-module (by n elements) with f: M $rightarrow$ M is an endomorphism such that f(M) is a subset of IM then there exists a monic polynomial p (deg n with coefficients in I) such that p(f) = 0 as an endomorphism on M.
And a beautiful part of it is that it shows that any surjective endomorphism of a finitely generated module M is an isomorphism. It also proves Nakayama’s Lemma which is great 🙂
Just wanted to share something nice and wonderful!