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two sample t-test
two separate groups
means
ASSUMPTION: normality
correlation
numeric x numeric
measuring relationship between two variables
one sample proportion test
binomial test
one sample, one proportion
ASSUMPTIONS: none
chi-squared goodness of fit
comparing proportions to each other simultaneously
fair v not fair
ASSUMPTIONS: normality, expected value of at least five successes
chi-squared test of independence
proportions
categorical x categorical
ie no religion has higher MNS proportion than others
ASSUMPTIONS: normality
null hypothesis: two categorical variables are independent
analysis of variance (ANOVA)
numeric x categorical
football conference and GPA
ASSUMPTIONS: normality, equal variances across groups
null hypothesis:
all population means are the same
numeric independent of categorical
model does not improve prediction ability
canโt tell which mean is different
hildebrand rule
symmetry test for populations
Shapiro test
normalcy test
sufficiently normal if p value is greater than 0.05
null hypothesis = data from sufficiently normal distribution
one sample t-test code
t.test(x,mu)
two sample t-test code
t.test(x,y)
fligner.tes(x ~ g)
tests for equal variances
how to reject null hypothesis
p value below alpha