# Search results

1. ### P-value of intercept = 0 meaning? Can age be independent var in simple regression?

You have a large enough sample that it shouldn't come into play - however, at times outliers at the end of a fit can have 'leverage'. I would fit the model with and without it and see if anything changes. If estimates shift and you get a better fit, I would remove and document it.
2. ### P-value of intercept = 0 meaning? Can age be independent var in simple regression?

Yeah, you shouldn't remove outliers unless they are erroneous. But in your set, if you have just one car that is 8 years older than the next oldest car - I would drop it and report that if disseminating your results. Also, per @miners comment, the residuals clustered on years is what it is, and...
3. ### Should I include year effects in my logistic regression?

If you don't know if you may need it - you either need to do more research or you don't need it. A simple thing you could do, is run the model for each year and see if there is much difference between the model estimates. If differences are trivial, then you are likely fine not including it...
4. ### P-value of intercept = 0 meaning? Can age be independent var in simple regression?

Given your sample size, everything seems fine. A q-q plot of residuals would also be nice. The model seems fine given age predicts price not price predicts age. The significant intercept, just means that the average price of a brand new car (age =0) is not equal to zero. Which makes sense...
5. ### Power Calculation for a Multilevel Linear Regression

My data generating function for simulating multilevel (repeated measure) data. I simulated more data than the desired sample size called for, then grouped data into sets the size of the desired n-value in order to generate more than one simulation set. I am guessing this is appropriate enough...
6. ### Power Calculation for a Multilevel Linear Regression

My first crack at the simulation is below. I am sure there are better approaches but the coefficients are pretty close to my target values. I just need to tune this up and replicate over and over. I am guessing that requires an apply or one of those functions. set.seed(1) #Period 1 Tx1 Y111 =...
7. ### Power Calculation for a Multilevel Linear Regression

Well, I am getting jealous by all of the posting on this topic. I have my own power calculation that I need to run and thought I would see if you all could provide a little guidance. I haven't done one for MLM data before. Study: -Two treatments groups (Treatment1 = Tx1; Treatment2 = Tx2) - 30...
8. ### A coincidence isn't that unlikely?

Miracles in this content could equate to 'rare' event as @Karabiner mentioned. Could also be called anomaly or in engineering, values far from the mean are sigma six or greater events. Sigma being standard deviation. I have always likened the gambler's fallacy to independence like @GretaGarbo...

10. ### I have a 2x2 crossover design comparing 2 treaments but don't have a clue what statistical analysis to use in SPSS

Can you provide a sample of your data so that can see the variables and how you have them formatted. You can make up these data if you want.
11. ### Power analysis for mixed-effects model

@spunky - what are you doing? Answer this question NOW.
12. ### simple count - type of data

What is the study purpose? Tell us the goal of the project - so we aren't the blind leading the blind. IMHO - this actually seems like count data that should be examined using Poisson regression.
13. ### Is this considered data leakage?

Correct, if the screening variable is not that specific, you can get a false positive. We are talking about a epistemological setting where everything is not known down to a micro-level, so things are not deterministic. We are in statistics land!
14. ### Is this considered data leakage?

If you are looking solely for an overall prediction value - it is relevant to include cause of the target, effects of the target, and other causes of the effects of the target (spouses). So for me to understand you genetics, knowing your parents genes, kids genes, and wife's genes (other cause...
15. ### Testing log-linearity

Well nothing is perfect, think about a Q-Q plot. So given it isn't out of control it is likely assumed horizontal.
16. ### Testing log-linearity

Yeah that sounds right, horizontal projection of residuals. Post your figure.
17. ### Hi everyone!

Welcome to the forum!
18. ### Hi everyone!

Welcome to the forum!
19. ### LASSO regression for variable selection

Using your content knowledge is better than LASSO since it does not under stand mediators, proxies, confounders, and if effects of outcomes are in the model. It helps withcollinearity, does now the source. Side noe, it is best practice not to get estimates from the same sample you build the...
20. ### ps matching and mcnemar test in spss

The conditional logistic regression would allow you to actually have an effect estimate with precision bounds instead of a trite pvalue. Also, it would allow you to match more than one control to a case (given you are case limited). It would require the same input.