L21 & L22 Social Science Statistics

 By the end of lesson 21, you should be able to:

  • Create a scatterplot of bivariate data.
  • Interpret the overall pattern in a scatter plot to assess linearity and direction.
  • Calculate the correlation coefficient.
  • Interpret the correlation coefficient, 
    , as a measure of strength and direction of a linear relationship between two variables.

REMEMBER:

> What we want to predict or estimate = (y) response variable (the dependent variable)
> What we use to make that prediction = (x) explanatory variable (the independent variable)

What to look for on a Scatterplot:
  • Form (Linear vs. Nonlinear)
    • Direction (Positive or Negative) When positive, x and y move together, both increasing or both decreasing. When negative, x and y move opposite
  • Strength (Weak, Moderate, Strong)
> Values close to 1 indicate a strong positive association
> Values close to -1 indicate a strong negative association
> Values close to 0 indicate a weak or no linear association

  • Correlation can only be computed for two quanitative variables
  • Use rho (soft curve p) for population correlation and r = sample correlation
  • Correlation is always between -1 and positive 1 ( for samples -1 < r < 1)
  • The sign on correlation only indicates the direction, not the strength
  • Strength is determined by how close the absolute value is to 1
  • Changing the unit of measurment for x or y or both will not change the correlation

By the end of lesson 22, you should be able to:

  • Identify the explanatory and response variable in a study.
  • Calculate the slope and intercept of a regression model.
  • Interpret the slope of the regression model.
  • Make predictions using a regression model

REMEMBER:
>Correlation measures the strength and direction of the linear relationship between x and y

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