Lecture 15 Categorical data and chi-square tests

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 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?

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. 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. 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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