Midterm Flashcards
p-value
is the probability of obtaining a result as or more extreme (more imbalance) than you did by chance. P-value tells you if the observed result can be explained by chance.
Relative risk
P1/P2 used ONLY in cohort studies.The relative risk tells you the magnitude of the disease-exposure association. A big relative risk does not necessarily mean that the p-value is small.
p-value depends
both on the magnitude of the relaitve risk as well as the sample size.
Relative Risk Interpretation
OC users are nearly 5 times more likely to have circulatory disease than non-users.
Relative Risk CI
If the 95% CI for the relative risk (RR) does not include 1 then you would reject the null which is RR=1 adn that there is evidence of a disease-exposure evidence.
Odds Ratio can be done in which study
case-control or cohort studies.
Odds Ratio Confidence Interval
CI intervalsl can be calculated. If the 96% condifence interal for the odds ratio does not include 1, it suggests that there is a significant association.
P-VALUE
A P value that is very small indicates that the observed effect is very unlikely to have arisen purely by chance, and therefore provides evidence against the null hypothesis. P values less than 0.05 are often reported as “statistically significant”, and interpreted as being small enough to justify rejection of the null hypothesis.
Sensitivity
Proportions of those with disease who are postive on the new diagnostic test.
Specificity
Proportions of those with out disease who are negative
Positve predictive value
The propotion of all individuals w/ a postive test who actually have disease.
Negative Predictive Value
The proportion of all individuals w/ a negative test who do not have the disease.
Simple Linear Regression
Y is the dependent variable.
X is the independent variable –>Predictor, Regressor, Covariate. The linear regresssion line minimizes the sum of squares of vertical deviations.
R2
is a measure of association, it represents the percent of the variance in the values of y that can be explained by nowing the value of x. between 0-1
R or the correlation coefficient
is a measure of the degree of linear relationship between two varaibles, usually labeled X & Y.