I’m trying to get murders per 100,000 inhabitants per US state per race. I have UNODC murder rates for the US as a whole, African–Americans as percentage of total population (1790–2010) by U.S. state, and White American population as of 2000 and 2010 censuses and a year common to each of these tables. I figured it was state_race_rate = 100000*perpetrator_count/(state_population*race_percent) but the histograms of the states don’t spread out nearly as far as I supposed they would against the histograms of the countries. You can see my work on this notebook. What am I doing wrong? submitted by /u/dbabbitt [link] [comments]
I’m getting a Ph.D. in mathematics and I’m basically studying mathematical statistics. The problems I work on have economics/finance workers as the primary audience. While I like these types of problems I see climate change as the most important issue our species faces, and I would like for my talents to help address this problem. So what are the statistical problems climate change scientists would like answered? Or what’s some good background reading on the statistics of climate science? Good books and review paper references appreciated. I may not do anything on this now while I’m still getting my Ph.D., but I could see myself looking into these problems after I get the Ph.D. submitted by /u/NTGuardian [link] [comments]
At the moment I am doing analysis in some data that is (currently) divided by number of individuals in a group. More or less like: Groups w/ two individuals: Value_1, Value_2, …, Value_N Groups w/ three individuals: Value_1, Value_2, …, Value_M In this case there were N groups of 2 individuals and M groups of 3 individuals. And so on until groups of about 300 individuals. Basically, I have different amount of data for each group size. I am trying to compare these experimental results with simulations I’ve ran as a null model. Currently, I run my simulation for each different group size then do a t-test for the means. However, I am left wondering: Is there any better/more interesting way to test how the experimental data differs from my null model/simulation instead of doing individual t-tests for each group size? Is there a way to aggregate all these tests into just one thing? submitted by /u/mechanical_fan [link] [comments]
Hello everyone, Since the survdiff function of the survival package in R performs a two sided log rank test where the log rank statistic follows a chi2 distribution under the null hypothesis, is it okay to take the square root value of the test statistic (the sign depending on the difference between the observed and expected number of events in the chosen group) to perform a one sided log rank test? The square root statistic would then be compared against a standard normal distribution… Thanks for your input ! submitted by /u/OlivBad [link] [comments]
The formula for the expectation of a normal distributed function is N[mu,mean], where mu=0 and variance=1, as n goes to infinity. This is given by 1/n∑xi p->n->infinity E[X]=mu=0 but which part of the formula makes it go to zero. Is it the sum that cancels out, e.g. -inf+inf=0, or is it because of the divisor (n) is increasing? Now, if we decrease the interval [infinity, -1] and [1,infinity], what happens with the sum then for normally distributed samples (they could be in the interval [-inf,inf], but we do only study those that are [>-1 and
Is it a major flaw in a survey to provide the questionnaire in two different mediums? I am doing a study in two locations and analyzing the data from the survey online is easier on my end if I’m using the internet, but I’m not sure if I’ll have internet when I provide the survey in the second. I want to be sure that this difference in medium will not affect my research results in any meaningful way.
Title. Goal is to go into data science, or maybe even get a phd many years later on and become a professor. Or if things don’t work out use the BS in computer science as a backup degree. But there’s not much overlap between CS and statistics, so I’m wondering if grad programs will accept someone like me in the first place. submitted by /u/680links [link] [comments]
Hello people of Math reddit So basically the title says it all, I’m currently taking calc 1 at my university and I just bombed my third test( as well as my 2nd test). I got a 74 on the 1st one 52 on the 2nd and a whopping 43 on the third and final one. I felt as if I understood a majority of the concepts and even thought my test grade was gonna be around a 70… guess not. I studied significantly harder for the 2nd and third one but somehow did far worse.
My professor … I just can’t understand when she teaches it, it literally goes in one ear and out the other. So I’ve basically been teaching myself with YouTube videos like professor Leonard and organic chemistry and go to tutoring at least twice a week but obviously I need something more.
Any suggestions on how you prepared for your final?
What else could I do? I really don’t want to disappoint my parents by failing , I’m currently a comp sci major so this class combined with my java class have really been taking a toll on me. I’ve never really liked math and it’s always been my problem area but I do enjoy comp sci My algebra probably isn’t the best how could I improve that?
Any suggestions will be highly appreciated!
I want to understand the following concepts more completely: convergence of series, use of series to approximate functions, mathematical transformations (hilbert transform, fourier transform), singular integrals, oscillatory integrals, maximal averages, etc.
I tried reading something like Elias Stein’s book but it was just way too advanced for my level (b.s degree in engineering). Just want to grasp the relationship between these concepts in a unified and cohesive way, but it’s hard to find a dummy’s guide to this. Any recommendations are appreciated.