So far, we’ve been working with one variable. We used z-tests to see if a value of a certain variable (e.g., a value of 6’1” from the variable “height”) differed significantly from the mean value for that variable. We used t-tests to do the same thing, but using samples to approximate populations. And we used ANOVA to look at values of the same variable, but in different groups.
In this lesson, we’ll analyze the relationship between two variables from the same sample (e.g., height and weight, which come from the same people; population and amount of pollution, which come from the same cities; number of students and number of teachers, which come from the same schools). In statistics, this relationship is called the correlation.
Mathematician Hans Rosling has a great video where he visualizes relationships between variables in cool new ways.
Continue to Lesson 15, or select a lesson below.
Lesson 1: Introduction to Statistical Research Methods
Lesson 2: Visualizing Data
Lesson 3: Central Tendency
Lesson 4: Variability
Lesson 5: Standardizing
Lesson 6: Normal Distribution
Lesson 7: Sampling Distributions
Lesson 8: Estimation
Lesson 9: Hypothesis Testing
Lesson 10: t-Tests for Dependent Samples
Lesson 11: t-Tests for Independent Samples
Lesson 12: Intro to One-Way ANOVA
Lesson 13: One-Way ANOVA: Test significance of differences
Lesson 14: Correlation
Lesson 15: Linear Regression
Lesson 16: Chi-Squared Tests