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Sedentary behavior & reduced medial temporal lobe thickness

  1. Apr 15, 2018 #1

    Buzz Bloom

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    My wife keeps telling me that I need to avoid sitting at the computer so much. She sent me the following link.
    I confess that I don't find the data presented in the article very convincing, and I would appreciate comments regarding the statistical analysis from knowledgeable participants.

    As I see it, the data consists of two scatter diagrams (Figs. 1 and 2) and two data summaries (Tables 1 and 2). Table 1 describes the characteristics of the population, and Table 2 (shown below) gives the statistical results. As I look at the scatter diagrams, the steepness of the least mean square fit seems to me to be strongly influenced by a few data points I would call outliers. The data in Table 2 gives values for β and p-value. I do not understand what β represents, but the range of 95% CI seems seems odd that it is so much larger than the value of β.

    Sitting.png
     
  2. jcsd
  3. Apr 15, 2018 #2

    Ygggdrasil

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  4. Apr 15, 2018 #3

    Buzz Bloom

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    Hi Ygggdrasil:

    You are probably right about that, but I would like to understand, if I can, whether the data in the Table 2 reliably implies that there is good support for the premise that avoiding excessive sitting improves memory. In particular:
    (1) Do you know what β represents?
    (2) For example looking at the first row, do the numbers {-0.02, (-0.04,-0.002)} mean that:
    (a) β=-0.02 is the mean or median of a probability distribution of possible values,
    (b) the probability that the true value of β ε {-0.04,-0.02} is approximately 0.475, and
    (c) the probability that the true value of β ε {-0.02,-0.002} is approximately 0.475?​
    (3) Is the p-value of 0.03 a reliable predictor of the premise, or it it only an indication (weak?) that the data is not consistence with the null hypothesis? Am I correct that this support against the null hypothesis implies only that the two correlated variables were not likely to have been generated by random numbers from a single distribution? Or is there some stronger implication?

    I would appreciate any comments you would care to make.

    Regards,
    Buzz
     
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