When it comes to measures of central tendency or location, the arithmetic mean and the median get a lot of praise. Other measures such as the trimmed mean, geometric mean and so on also get some mention. But what about the mode?

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Gail was doing research for a District Reading Assessment and needed Peter to help decipher data from bilingual and linguistic text use research.

**On Speed, Efficiency as well as Proactiveness when responding to requests:**

Dr. Peter Flom usually gets back to me within 2 to 6 hours of my email request. Unbelievable, and beyond what I have experienced with other statisticians.

**On Attention to Detail and Thoroughness:**

Dr. Peter Flom looks closely at the data I provide. He asks questions so he can be sure to analyze the results correctly.

For example:Dr. Flom is currently looking at what is called Developmental Reading Assessment (DRA)…noting the research data currently being analyzed does not quite “make sense” (meaning children able to read more words, are scoring lower on the DRA). Peter cares about research…am finding through his expert feedback, questions, and dialog that the DRA assessment has issues with reliability and validity. Dr. Flom wants research,

our researchto be high quality!

**On how Peter has helped Gail learn:**

Okay…I am a dummy at statistics and statistical analysis. Dr. Peter Flom never makes me feel that way….all my questions are welcomed and answered. What I love about Dr. Peter Flom is how he uses analogies. “What is and effect size?” I ask. Dr. Peter Flom explains, “Okay, say your research is on a diet pill…people taking the pill lose .1 of a pound in 3 months.” This “effect” size is minimal.

**On Providing Recommendations**

Dr. Peter Flom has not only kept up with emails when reviewing my research. He is available via phone to talk with me about data and research. Dr. Peter Flom has taken the time to read grant proposals, dissertations, and other parts of a huge research.

I asked Gail **what she liked most** about working with Peter, her response:

Dr. Peter Flom, has the rare combination of being a genius at statistical analysis with the ability to explain the statistics to those (like me) who do research as a passion on a subject… who do not really understand quantitative analysis.

Dr. Flom cares, cares about the research integrity!

All in all, Gail said Yes, should would recommend Peter to her friends and colleagues.

Question: What is principal component analysis in super-layman terms?

My answer: Principal component analysis is a dimension reduction method.

Suppose you have a great many variables – too many to deal with effectively. If you want to replace them with a smaller number of variables, while losing as little information as possible, PCA is one way to do it.

It is different from (but related to) factor analysis, which attempts to find latent factors – that is, things that cannot be directly measured.

The language used in these two methods is extremely confusing.

Question: How does ridge regression work?

My answer: OLS models are BLUE – best linear unbiased estimateors.

But sometimes forcing unbiasedness causes other problems. In particular, if the independent variables are fairly collinear, then the variances of the parameter estimates will be huge and small differences in the input data can make huge differences in the parameter estimates.

Ridge regression allows some bias in order to lower the variance.

Q: What does it mean when standard deviation is higher than the mean?

My answer: It depends on the nature of the data.

If all of the values are positive, then it indicates that there is quite a bit of spread, and the ratio of sd/mean is the coefficient of variation. This can be useful to compare the degree of “spreadoutedness” of two distributions with different means.

But if some of the data are negative, then the comparison of sd to mean stops having any meaning.

E.g. if there are 3 variables X, Y and Z

X: 1, 2, 3, 4, 5

Y: 10, 20, 30, 40, 50

Z: -2, -1, 0, 1, 2

Intuitively, these all seem to have the same spread in comparison to their size.

X has mean = 3, sd = 1.58, CV = 0.53

Y has mean = 30, sd = 15.81, CV = 0.53

Z has mean = 0, sd = 1.58, CV = infinite