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Summary notes for statistics for Masters of Public Health
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  • Course
  • Statistics
  • Institution
  • Imperial College London (ICL)
  • Book
  • Basic Biostatistics

Summary document of statistical methods. Based on lectures taken as part of the statistics module in a Masters of Public Health at Imperial College London. Notes are clearly laid out with a number of tables. Please message me if you have any questions and leave if a positive review if these notes...

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  • No
  • Xx
  • November 13, 2020
  • 3
  • 2019/2020
  • Summary

Subjects

  • statistics
  • mph
  • masters of public health
  • public health
  • epidemiology
  • icl
  • imperial college london

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  • Imperial College London (ICL)
  • Imperial College London
  • Statistics
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Types of data and statistical tests:

Comparison of means – looks for the difference between the means of variables:


Statistical test: Data type: Use:
Pearson’s correlation Continuous Look for an association
between two variables
Spearman’s correlation Continuous Tests for associations
between two ordinal
variables (doesn’t
assume normal
distribution)
Chi-square Categorical or binary Tests for strength of
association between two
categorical or binary
variables
One sample t-test Independent var: Tests for differences
categorical or nominal between two related
(compare to fixed value) variables – does the
population mean differ
from the chosen fixed
value? Comparing
population mean with a
fixed value
Paired t-test Dependent var: Does the population
continuous mean differ between two
Independent var: time related groups?
variable H0 = mean
change/difference is 0.
H1 = mean
change/difference is not
1.
Independent t-test Dependent var: Whether population
continuous mean differs between
Independent var: two groups.
categorical or nominal H0 = population means
of group 1 and 2 are the
same.
H1 = population means
of group 1 and 2 are
different.


Regression – assess if change in one variable predicts change in another variable:

Name: Data type: Use:
Linear regression Dependent variable: Gives a numerical
continuous (outcome) explanation of how

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