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      • STA215
        • Linear Regression
          • LR-A1: Intro to Linear Regression
          • LR-A2:Predicted Equation
          • LR-A3: Least Squared Errors
          • LR-A4: Interpreting Coefficients
          • LR-A5: Interpreting Significance (and insignificance)
          • LR-A6: Interpreting the Intercept
          • LR-A8: Interpreting Coefficients for Quantitative IVs
          • LR-A9: Interpreting Coefficients for Qualitative IVs
          • LR-A10: Interpreting R2
          • LR-A11: Residual Plots
          • LR-A12: Linear Relationship
          • LR-B1-Residual vs Error
          • LR-B2-Calculating Predicted Values and Residuals
          • LR-B3-Interpreting predictions + residuals
          • LR-B4-Covariance
          • LR-B5-Interpret Units
          • LR-B6-Qualitative IV with more than two categories
          • LR-B7-calculating difference categories not reference
          • LR-B8-Substantive intercepts
          • LR-B9-Significance using confidence intervals
          • LR-B10-Standardized Quantitative Independent Variables
          • LR-B11-Standardizing with Qualitative IV
          • LR-B12-Assumptions and Conditions
          • LR-B13-Relationship as Modeled, Not Observed
          • LR-C1-calculating coefficients
          • LR-C2-Effect of Outliers
          • LR-C3-Root Mean Square Error
          • LR-C4-Squared Error Regression Line
          • LR-C5-Inferences in Regression
          • LR-C6-Calculating T-Statistic
          • LR-C7-Calculating Confidence Intervals of the Slops
          • LR-C8-Fitting non-linear Relationships
          • LR-C9-Interpreting non-linear regressions
          • LR-C10-Polynomials
          • LR-C11-Log-linear Model
          • LR-C12-Using Residual Plots to Assess Assumptions
  • Research
    • Major Projects
    • Peer-Reviewed Publications
  • C.V.
 
  • Home
  • Teaching
    • Core Teaching Commitments
    • Courses
      • STA215
        • Linear Regression
          • LR-A1: Intro to Linear Regression
          • LR-A2:Predicted Equation
          • LR-A3: Least Squared Errors
          • LR-A4: Interpreting Coefficients
          • LR-A5: Interpreting Significance (and insignificance)
          • LR-A6: Interpreting the Intercept
          • LR-A8: Interpreting Coefficients for Quantitative IVs
          • LR-A9: Interpreting Coefficients for Qualitative IVs
          • LR-A10: Interpreting R2
          • LR-A11: Residual Plots
          • LR-A12: Linear Relationship
          • LR-B1-Residual vs Error
          • LR-B2-Calculating Predicted Values and Residuals
          • LR-B3-Interpreting predictions + residuals
          • LR-B4-Covariance
          • LR-B5-Interpret Units
          • LR-B6-Qualitative IV with more than two categories
          • LR-B7-calculating difference categories not reference
          • LR-B8-Substantive intercepts
          • LR-B9-Significance using confidence intervals
          • LR-B10-Standardized Quantitative Independent Variables
          • LR-B11-Standardizing with Qualitative IV
          • LR-B12-Assumptions and Conditions
          • LR-B13-Relationship as Modeled, Not Observed
          • LR-C1-calculating coefficients
          • LR-C2-Effect of Outliers
          • LR-C3-Root Mean Square Error
          • LR-C4-Squared Error Regression Line
          • LR-C5-Inferences in Regression
          • LR-C6-Calculating T-Statistic
          • LR-C7-Calculating Confidence Intervals of the Slops
          • LR-C8-Fitting non-linear Relationships
          • LR-C9-Interpreting non-linear regressions
          • LR-C10-Polynomials
          • LR-C11-Log-linear Model
          • LR-C12-Using Residual Plots to Assess Assumptions
  • Research
    • Major Projects
    • Peer-Reviewed Publications
  • C.V.
  • More
    • Home
    • Teaching
      • Core Teaching Commitments
      • Courses
        • STA215
          • Linear Regression
            • LR-A1: Intro to Linear Regression
            • LR-A2:Predicted Equation
            • LR-A3: Least Squared Errors
            • LR-A4: Interpreting Coefficients
            • LR-A5: Interpreting Significance (and insignificance)
            • LR-A6: Interpreting the Intercept
            • LR-A8: Interpreting Coefficients for Quantitative IVs
            • LR-A9: Interpreting Coefficients for Qualitative IVs
            • LR-A10: Interpreting R2
            • LR-A11: Residual Plots
            • LR-A12: Linear Relationship
            • LR-B1-Residual vs Error
            • LR-B2-Calculating Predicted Values and Residuals
            • LR-B3-Interpreting predictions + residuals
            • LR-B4-Covariance
            • LR-B5-Interpret Units
            • LR-B6-Qualitative IV with more than two categories
            • LR-B7-calculating difference categories not reference
            • LR-B8-Substantive intercepts
            • LR-B9-Significance using confidence intervals
            • LR-B10-Standardized Quantitative Independent Variables
            • LR-B11-Standardizing with Qualitative IV
            • LR-B12-Assumptions and Conditions
            • LR-B13-Relationship as Modeled, Not Observed
            • LR-C1-calculating coefficients
            • LR-C2-Effect of Outliers
            • LR-C3-Root Mean Square Error
            • LR-C4-Squared Error Regression Line
            • LR-C5-Inferences in Regression
            • LR-C6-Calculating T-Statistic
            • LR-C7-Calculating Confidence Intervals of the Slops
            • LR-C8-Fitting non-linear Relationships
            • LR-C9-Interpreting non-linear regressions
            • LR-C10-Polynomials
            • LR-C11-Log-linear Model
            • LR-C12-Using Residual Plots to Assess Assumptions
    • Research
      • Major Projects
      • Peer-Reviewed Publications
    • C.V.

Linear Regression

Introduction to Linear Regression

NEED TO KNOW

Model

Predicted Equation

Least Squared Errors

Interpretations

Interpreting Coefficients

Interpreting significance and insignificance

Interpreting the Intercept

Interpreting Coefficients for Quantitative IVs

Interpreting Coefficients for Qualitative IVs

Interpreting R2

Assumptions

Residual Plots

Linear Relationship

Should Know

Model

Residuals vs Error

Predictions and Residuals in the Social Sciences

Covariance

Interpretations

Interpret Units

Qualitative IVs with more than two categories

Calculating Differences between Categories that Aren't the Reference

Substantively Interpreting Intercepts

Assessing Significance using Confidence Intervals

Standardized Quantitative Variables

Standardized Qualitative Variables

Assumptions

Assumptions and Conditions

Relationship as Modeled, Not Observed!

Could Know

Model

Calculating Coefficients

Effect of Outliers

Root Mean Square Error (RMSQ)

Squared Error Regression Line

Interpretations

Inferences in Regression

Calculating T-Statistic

Calculating Confidence Intervals of the Slopes

Fitting non-linear relationships

Interpreting regressions with non-linear terms

Polynomials

Log-Linear Model

Assumptions

Using Residual Plots to Assess Assumptions

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