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Meet my friends……. Chester the T TEST. Comparing means of 2 variables Exposure-Categorical w/ up to 2 categories Outcome- Continuous. Rova the ANOVA. Comparing means of 2 variables Exposure- Categorical w/ 3 or more categories Outcome- Continuous. Tory the Correlation.
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Chester the T TEST • Comparing means of 2 variables • Exposure-Categorical w/ up to 2 categories • Outcome- Continuous
Rova the ANOVA • Comparing means of 2 variables • Exposure- Categorical w/ 3 or more categories • Outcome- Continuous
Tory the Correlation • Quantifying association of 2 variables • Exposure & Outcome- Continuous • Positive or Negative? Strong or Weak?
Phineas the Linear Regression • Making predictions about 2 variables • Exposure- categorical or continuous • Outcome- continuous
Muppy the Multiple Linear Regression • Making predictions about 2 variables while controlling for confounding variables • Exposure- Categorical or Continuous • Outcome- Continuous
Chi Square Test • Used to test for significant differences when the data is qualitative and dichotomous AKA 2 CATEGORICAL VARIABLES Exposure- Categorical w/ @ least 3 categories Outcome- Category w/ 2 categories
So….. • Since I use CATEGORICAL variables.. Descriptive statistic tools used are: Frequency and Percentages
Thus I am used: • To see if there are differences in proportions • Not to determine or quantify the differences but just TO SEE IF THERE IS A DIFFERENCE • To look at the frequency of a disease outcome based on a categorical exposure • Variables are independent and mutually exclusive/exhaustive • Random Sample
Example • Exposure? • Categories • Outcome?
Example Research Hypothesis: The proportions of OUTCOME among EXPOSURE CATEGORIESare significantly different Null Hypothesis: The proportions of OUTCOME among EXPOSURE CATEGORIES are equal.