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Lecture 15 Categorical data and chi-square tests

Lecture 15 Categorical data and chi-square tests. Continuous variable : height, weight, gene expression level, lethal dosage of anticancer compound, etc --- ordinal Categorical variable : sex, profession, political party, blood type, eye color, phenotype, genotype

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Lecture 15 Categorical data and chi-square tests

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  1. Lecture 15 Categorical data and chi-square tests • Continuous variable : height, weight, gene expression level, lethal dosage of anticancer compound, etc --- ordinal • Categorical variable : sex, profession, political party, blood type, eye color, phenotype, genotype • Questions : do smoke cause lung cancer? Do smokers have a high lung cancer rate? • Do the 4 nucleotides, A, T, G, C, occur equally likely?

  2. Sample space : the set of possible basic outcomes • To study categorical variables, the first thing is to know what the categories are. • face of coin : head, tail • face of a die : 1, 2,3, 4, 5, 6 • Nucleotide : A, T, G,C • Sex : male, female • Blood type: A,B, O, AB The set of possible outcome of a categorical variable forms a sample space When two categorical variables are involved, then the sample space is the set of all possible combinations.

  3. Subjective probability and assumption of independence • Symmetry : if two outcomes are deemed symmetrical, then they should be assigned with an equal probability • Sum of probability is equal to 1 • If two variables are independent, then you can multiply the probability. • Statistical questions : can symmetry be assumed? Can independence be assumed? • Solution : Collect data and conduct a chi-square test.

  4. Examples • A random sample of 100 nucleotides is obtained. There are 24 A, 21 T, 30 G, 25 C. • Are the data compatible with the assumption of equal occurrence? • Suppose G and C are mixed up by error. So we have 24 A, 21T, 55 G/C. What is the answer ?

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