PURCHASE A DIGITAL COPY
PURCHASE A HARD COPY
|Lesson 1||Introduction to Statistical Research Methods|
|Lesson 2||Visualizing Data|
|Lesson 3||Central Tendency|
|Lesson 6||Normal Distribution|
|Lesson 7||Sampling Distributions|
|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 15||Linear Regression|
|Lesson 16||Chi-Squared Tests|
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.
Let’s analyze some real data so you can get a good sense of how we can use correlations to draw conclusions. The data below lists the first few rows of the following variables: [Link to full data]
- population US state population in millions
- pop_density Population density per sq. km.
- tax_rate State tax rate
- spending State and local spending as percent of gross state product
- debt State and local debt as percent of gross state product
- rsg Real state growth in percent