6.29 Remind me of calculating degree of freedom

http://ron.dotsch.org/degrees-of-freedom/

When reporting the F statistics, follow this format: F(df1, df2) = ...

• df1: # of levels (or factors) - 1
• df2: residual; # of subjects - # of levels

6.23 Paired t-test versus ANCOVA

Paired t-test compares differences between means, doing less than ANCOVA. ANCOVA can compare differences between means, controlling covariates, and making predictions according to the coefficients.

6.23 About choosing model

Always think about what question one needs to address. A different model may no longer fit for the question.
Considering covariate does not mean it has to stay in the model forever. If not significant, can be removed and report the raw value (which is statistically equivalent to considering it in the model).

6.20 Can we study brain cultivated from stem cells?

Excited when reading Dr.Guy Mckhann's article on how differentiating iPSCs and developing them into brain cells can inspire many questions such as studying the Zika virus.

The most exciting part of this research involves creating "mini-brains." These mini-brains, about the size of the head of a pin, can be used to study the development of the human brain and how development is altered by a virus. Hopkins researchers are currently using mini-brains to study the Zika virus. This study is just the tip of the iceberg.

 

6.20 Better skill of organizing is urgently needed

Inevitably, I write pieces of draft papers everyday during the daily work. If not promptly archived, they will start to fly everywhere on my desk, and I simply can't get a clear mind out of these residues. I need to keep the desk clean which helps me keep the mind sharp.

Usually there are multiple steps in the image processing pipelines, so when I run the pipeline I need to remind myself.
Also, sometimes there are short-hand calculations for example the adjusted p-value after multiple correction.
It also happens when I need to construct a big table format that I should follow so as to smoothly run the statistical analysis.
There are also printed reports with figures and tables to be discussed with PI.
It's also important to keep several notebook, for daily recording, for technical details, etc.

6.16 Notes of Statistics

• It is not appropriate to use standard Bonferroni correction for the non-apriori regions, because like always, our neuroimaging dependent measures will be at least moderately intercorrelated. The standard Bonferroni assumes orthogonality (independence), which is not the case for our FA measures. It would be much better to use the modified Bonf method that I've used forever (refer to the Sankoh's paper), or FDR.
• Given there are multiple ways to modify the Bonf which seems to be a black box to me, just use FDR for now.
• I used Spearman's rho to investigate associations between smoking and drinking measures and FA in all groups. All of these associations must be adjusted for age because of the age association with FA. It does not matter if the groups are or are not different with age. We want to know if the association are significant after adjusting for the influence of age. Additionaly, lifetime years of smoking is related to age, so age absolutely must be used as a covariate. You cannot use covariates with the standard Spearman method. These analyses must be repeated with linear regression, using age as a covariate.
• In SPSS, there's something called part correlation (i.e. semi-partial correlation, different from partial correlation).
• Maybe scores of zero were assigned to non-smokers for lifetime years of smoking. If this did happen, this is a fatal design flaw. You can't assign a score of zero to someone who does not have the behavior, i.e., history of smoking. A score of zero is meaningless and creates a "zero clumping" issue that will absolutely lead to spurious results for simple correlations or linear regression.
• e.g. non-smokers can't have "0" for lifetime years of smoking, which would otherwise be equivalent to the smokers who does not have a history of smoking. This will confuse group design and mess the analysis up.

6.6 Reasons that I unenrolled in ML Classification

I already finished the case-study course and the regression course, get an idea of what machine learning about. When have time, I can always come back and review the context that I've learnt, or picking up new knowledge without knowing nothing of it. The regression course is of particular importance as I will learn most of the materials again in my PhD.

I cannot grade the Quiz unless pay $79 to Coursera to upgrade to a verified certificate. It is pointless to just watch the video and doing ungraded quiz, and it is unfair for learners who do not wish to pay for verified certificate to pay for grading the quiz. This is a total nonsense.

I have more tasks on the plate, processing more images and analyzing more data, as well as manuscript to work on. I am simply running out of time. The classification courses won't finish until the end of July when I will be already on the road trip. Either I will be studying while traveling (which is weird), or I shall finish the course earlier to make time for the trip, which adds up stress.

To this stage, completing the first two courses are sufficient for me to pick up python. I am thinking about strengthening my understanding in data structure and algorithm so as to write better codes. I should think about what's the best strategy to fit that in. But now I am so disappointed with Coursera. That company tried to squeeze money rather than benefiting the learning community.