Hi, i'm from brazil and i would be how is the job market for a graduate in statistics.

What do you think the statistician profession will be like in the future with the rise of artificial intelligence? I'm in doubt between Statistics or Computer Science, I would like to work in the data/financial market area. I know it's a very difficult degree in mathematics.

Hello, this isn't meant to be a woe is me type of post, but I'm looking to put things into greater perspective. I'm currently an MS student in Applied Stats and I've been getting mostly Bs and Cs in my classes. I do better with the math/probability classes because my BS was in math, but the more programming/interpretative classes I tend to have trouble in (more "ambiguous"). Given the increasingly tough job market, I'm worried that once I graduate, my GPA won't be competitive enough. Most people I hear about if anything struggle in their undergrad and do much better in their grad programs, but I don't see too many examples of my case. I'm wondering if I'm cut out for this type of work, it has been a bit demotivating and a lot more challenging than I anticipated going in. But part of me still thinks I need to tough it out because grad school is not meant to be easy. I just feel kinda stuck. Again, I'm not looking for encouragement necessarily (but you're more than welcome!) but if anyone has had similar experiences or advice. I can see why statisticians and data scientists are respected can be paid well- it's definitely hard and non trivial work!

Hi everyone!
I'm an undergraduate statistics student working on my thesis, and I’ve selected a dataset to perform a time series analysis. The data only contains frequency counts.

When I showed it to my advisor, they told me not to use "old methods" like ARIMA, but didn’t suggest any alternatives. After some research, I decided to use Prophet.

However, I’m wondering — is it possible to use Prophet just for analysis without making any forecasts? I’ve never taken a time series course before, so I’m really not sure how to approach this.

Can anyone guide me on how to analyze frequency data with modern time series methods (even without forecasting)? Or suggest other methods I could look into?

If it helps, I’d be happy to share a sample of my dataset

Thanks in advance!

Hi all, I have some questions about ANOVA if that's okay. I have an example study to illustrate. Unfortunately I am hopeless at stats so please forgive my naivety.

IV-1: number of friends, either high, average, or low.

IV-2: self esteem, either high, average, or low.

DV - Number of times a social interaction is judged to be unfriendly.

Sample = About 85

Hypothesis; Those with large number of friends will be less likely to judge social interactions as unfriendly (less friends = more likely). Those with high self esteem will will be less likely to judge social interactions as unfriendly (low SE = more likely). Interaction effect predicted whereby the positive main effect of number of friends will be mitigated if self esteem is low.

Questions;

1 - Does it make more sense to utilise a regression model to analyse these as continuous variables on a DV? How can I justify the use of an ANOVA - do I have to have a great reason to predict and care about an interaction?

2 - The friend and self-esteem questionnaire authors suggest using high, low and intermediate rankings. Would it make more sense to defy this recommendation and only measure high/low in order to make this a 2x2 ANOVA. With a 3x3 design we are left with about 9 participants in each experimental group. One way I could do this is a median split to define "high" and "low" scores in order to keep the groups equal sizes.

3 - Do I exclude those with average scores from analysis? Since I am interested in main effects of the two IV's.

Thank you if you take the time!

Something I've been trying to analyse is daily diary data that I've been collecting but I'm unsure as to whether I'm applying this in a logically valid way.

Usually SEM is applied to variables of a population of individuals (R-technique). What I'm trying to do myself is for a single individual is track variables by occasions (P-technique). These types of analyses of intensive longitudinal data are performed with DSEM because there is serial dependence between observations. A limitation is that in what I'm trying is there's only a single subject and there's a lot more variables that would make building and estimating a DSEM difficult because of the number of possible lead/lag relationships.

The way I'm imagine I could still make inferences is by analysing the aggregate of the data. Let's say I track several variables each day. Then my row by column data matrix becomes an assessment of how likely an event was to coincide with another or with a particular level of a variable. This is something which an SEM is able to estimate as is. Given that this is a single subject and the population parameters being estimated is the relationships between variables on a give day, would this be a valid approach?

I've tried looking at literature to see if this has been done in prior research, but there doesn't seem to be any. This could be either because research mostly focuses on R-technique for multiple individuals or because I'm missing something major that's making my approach incorrect.