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What does researcher want of statistics?

What does researcher want of statistics?. What does researcher want of statistics?. “ I had a fun and get it in addition to my cool microscope images!” “I have done a statistical analysis of my results and now give me my PhD, pleeeease!.. ”. How variable it is? Does “my pet thing” work?

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What does researcher want of statistics?

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  1. What does researcher want of statistics?

  2. What does researcher want of statistics? “I had a fun and get it in addition to my cool microscope images!” “I have done a statistical analysis of my results and now give me my PhD, pleeeease!..” • How variable it is? • Does “my pet thing” work? • Why do the things differ? • Why does it fail from time to time? • Why patients have different fate and where is the hope for them? • What would the outcome of a perturbation? Generally speaking, all the statistics is about finding relations between variables

  3. Basic concepts to understand • Variability • Variable • Relation • Signal vs. noise • Factor vs. response (outcome), independent vs. dependent variables • Statistical test • Null hypothesis • Power • Experimental design • Distribution

  4. Deterministic vs. stochastic data

  5. Two graph concepts: Histograms: show quantities of objects of particular qualities as variable-height columns

  6. Two graph concepts: Scatterplots: show objects arranged by 2 particular qualities as coordinates

  7. Two graph concepts: Histograms vs. scatterplots

  8. Normal distribution –––––– +++––– ++++++ +-+–+– …………… ---+++

  9. Not a normal distribution

  10. Variance: Var = Sum(deviation from mean)2 • Standard deviation: SD = Square root from Var • Skewness: deviation of the distribution from symmetry • Kurtosis: “peakedness” of the distribution • Standard error: e.g. SE = SD / square root from N

  11. Kurtosis: positive

  12. Kurtosis: negative

  13. Skewness

  14. Analysis of correlations

  15. Simple linear correlation (Pearson r): r = Mean(CoVar) / (StDev(X) x StDev(Y)) CoVar = (Deviation Xifrom mean X) x (Deviation Yifrom mean Y)

  16. How to interpret the values of correlations • Positive: the higher X, the higher Y • Negative: the higher X, the lower Y • ~0: no relation Confidence: • |r| > 0.7: strong • 0.25 < |r| < 0.7: medium • |r| < 0.25: weak

  17. Outliers • Correlations in non-homogeneous groups

  18. Nonlinear relations between variables • Measuring nonlinear relations

  19. Spurious correlations • Multiple comparisons and Bonferroni correction • Coefficient of determination: r2 • How to determine whether two correlation coefficients are significant • Other correlation coefficients

  20. When it should not work? • Graphs • 2D graphs • Scatterplots w/Histograms

  21. Exploratory examination of correlation matrices

  22. When it should not work?

  23. Normalize it! E.g. NewX = log(X)

  24. Causality There is no way to establish from a correlation which variable affects which. It is just about arelation.

  25. Casewise vs. pairwise deletion of missing data • How to identify biases caused by the bias due to pairwise deletion of missing data • Pairwise deletion of missing data vs. mean substitution

  26. Statsoft’s Statistica • A perfect, almost universal tool for the researchers in the range for “very beginner” to ”advanced professional”. • An old software with intrinsic development history • Most of the methods can be found in >1 module • Most of the modules contain >1 method • No method is perfect • No module is complete • Most of the special modules are unavailable in the basic “budget” license

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