Hi! I’m a PhD student in political geography with strong interests in geostatistics. However, neither my own nor neighboring universities explicitly offer any such courses. I have a strong empirical background—probability theory and statistical inference, causal inference, machine learning, etc., as well as various programming courses. Might anyone have an idea of what sort of courses might be helpful for more strongly learning geostatistics without any explicit such offerings? I figured perhaps stochastic and time series statistics and network theory would be helpful, but I’d appreciate any more insight. Thanks!

# Month: August 2022

Hi all, I’m working with some temperature data, and wanted to see if anyone has recommendations for how to better deal with my situation. I am comparing a rare historical data set with only single mean/max/min values for each month to full records taken continuously over the past 10 years. Assume the data are directly comparable . We basically want to see if the most recent 10 years differ from the one historical year, with the hypothesis that the recent years would be warmer in most months .

Obviously having a single year’s worth of data isn’t ideal, but it is valuable for the system I’m studying as there are no comparable records elsewhere.

So far I have calculated the same metrics for the recent data , modeled these and put a 95% confidence interval around it, and visually looked at which historical data points are outside that interval. From this it appears some months are warmer in the recent years than in the historical year, but I can’t really say much beyond qualitative statements.

I’m not well versed in Bayes models, but is there a better way to compare these data and get some actual quantitative results? Or should I just stick to a more quantitative approach given the data’s limitations?

The data was pulled from patients records charts, sample size is over 20,000. The data has so many missing data to the point that some columns are all blank, other columns have 10 values. I thought there are many reasons for missing data, either because the population of our study patients are young so they don’t have all those diseases so the docs didn’t have to add it in their records, or the people who pulled the data forgotten something. But I have nothing to do now except that I ask the people who pulled the data.

In the mean while, what else should I do to handle categorical missing data? Majority are Y/N binary data

Hi everybody,

I’d like to model a dependent variable which a continuous value between 0 and 1 , see . It is not a count proportion, it’s the concentration of a drug needed to kill a disease.

I tried the following/my thoughts:

1. GLM with binomial family and logit link, i.e. logistic regression. The logit link to render my mean in the region makes sense, but the binomial random component does not, since my error is continuous and must be bouneded between 0 and 1.As you can see, the in-sample prediction on the train data looks quite different from the real label distribution. Also, there is a strange offset in my prediction.

2. Beta Regression also does not work, because I have 0 labels.

3. I tried different links, different regularizers , with intercept and without, but nothing seems to work.

Hence, I would be happy if someone could help me with the following questions:

1. Does it make sense to have a *compound* model, i.e. one model that predicts if the response is 0 or >0, and in case of >0 another model that predicts the label using a beta regression?

2. Logistic Regressions seems to be the go-to approach if my labels are binary. However, they are not in my case. How can I determine a proper family for my problem?

3. Are GLMs not useful at all for my problem?

Preface: It’s been a while since I’ve last needed to use data analysis and my memory has come up blank. I’m using SPSS for statistical analysis so any help that also uses this software would be much appreciated. Apologies if I have forgotten the correct lingo, again it’s been a while.

Context: I have a large set of mainly nominal data spread over a range of variables. I’ve performed a Chi squared test to check for significant variables then an ANOVA on each of those variables to determine which other variables interact significantly with them. I now want to find out which factors within each variable interact with each other significantly. How can I do this?

Hey everyone, I have a Cox survival regression model and due to non linear martingale residuals I had to add extra variables that are quadratic and cubic transformations of a variable already used in the model. How do I interpret such variable? As an example I have:

>exp exp lower .95 upper .95

>

>axil_nodes 1.332 0.7506 1.1861 1.4965

>

>I 0.986 1.0142 0.9782 0.9938

>

>I 1.000 0.9998 1.0001 1.0003

I get that normally, increasing ‘axil_nodes’ by one, would increase the chance of event by 1.332, but how do I interpret it, if I have transformations of this variable as other variables in the same model?

Hi, doing my psychology dissertation and it’s due so soon and I’m so stuck!!

Looking at predictors of theory of mind performance. IV/Predictor variables were: executive function , 5 personality traits, autistic traits and empathy. Was going to put in the IVs that are significantly correlated with my DV

4 IVs correlated with my DV but some of these are significantly correlated with eachother and some of my other IVs that did NOT correlate with my DV.

e.g. TOM significantly correlated with agreeableness, but agreeableness also significantly correlated with: empathy, EF, extraversion, autistic traits and neuroticism

Do I put all the IVs correlated with eachother into a regression so I am controlling for them? Problem is that would give me 8 predictors not including age or gender?

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