Linear Regression using Stata

Explore how multiple linear regression uses attendance and study to predict GPA, interpret coefficients, and compare effects, showing attendance has a larger impact than study.

Explore how p-values indicate statistical significance in a multiple linear regression, with each coefficient's p-value below 0.05 signaling significance and justifying including attendance and study in the model.

Assess fit and residuals in a regression predicting GPA from attendance and study in Stata, noting r-squared and p-values; interpret residuals as observed minus predicted and aim for small residuals.

Learn how to encode a binary variable, like gender, as 0/1 for linear regression, interpret coefficients, and compare reference categories in a multiple regression.

Learn how to code categorical variables in linear regression using dummy variables, with a four-group major example, where coefficients compare each group to the reference (business).

After fitting the linear regression model, assess fit and assumptions by comparing predicted to observed GPA and visualize fitted versus observed values to see alignment near the y=x line.

Examine the normality of residuals in linear regression by plotting a histogram and overlaying a normal curve, and assess independence and constant variance to validate model assumptions.

Test independence by plotting residuals against fitted values or independent variables to check for patterns. A random scatter indicates independence; a pattern such as a U shape signals dependence.

Identify and diagnose outliers in linear regression using diagnostics to assess model fit, understand influential observations, and compare regression lines with and without outliers.

Explore how to apply linear regression with Stata, read output, and perform diagnostics and assumption checks using intuitive commands.

Explore simple linear regression with Stata by loading a dataset, plotting a scatter plot of GPA against attendance, and interpreting the regression output: GPA equals 1.22 times attendance minus 13.2.

Assess model fit by comparing simple and multiple regression using adjusted r-squared and p-values, then predict GPA and examine residuals with histograms and q-q plots for normality.

Explore how a quadratic term for English improves GPA regression in Stata, using either English squared or an English-by-English interaction, indicating a nonlinear relationship with significant results (p<0.05).

Learn how to assess model fit and assumptions in a multiple linear regression predicting GPA from attendance and gender, using predicted vs observed plots, adjusted r-squared, and p-values.

Assess independence of residuals and constant variance in linear regression by plotting residuals against fitted values, using a zero line, and applying Stata's robust standard errors when needed.

Run a regression in Stata, then use the vif command to compute VIF values for each independent variable. All VIFs are below 10, indicating no multicollinearity.

Identify and diagnose outliers in simple and multiple linear regression using diagnostic plots, av plots and leverage-residuals plots, and assess their influence on model results.

Identify and assess influential observations using Cook's distance and dfits in Stata; create new influence variables, predict fits, and visualize influential points to decide whether to exclude problematic cases.

Explore selection algorithms in Stata: forward selection and backward elimination identify best predictors for GPA. Stepwise with p<0.05 selects English and attendance as the final model, aligning with dataset insights.

Learn to visualize regression results in Stata using margins plot and margins after analysis, and customize graphs that clearly summarize each independent variable's effect on the dependent variable.

Explore how to visualize effects in multiple linear regression using Stata by applying the margins and margins plot commands to predict GPA across attendance values 40 to 100.

Explore a multiple linear regression with attendance and a gender indicator, using margins and margin plots to compare GPA by gender, showing females score higher than males across attendance levels.

Explore a dataset with more than 800 observations to apply multiple linear regression in Stata, including scatter plots, predicting gpa with residuals, model comparison by adjusted r-squared, and testing assumptions.

Explore a large Stata dataset to model GPA with linear regression, examining variables like college, credits, gender, attendance, English, siblings, income, and work, and test model fit and assumptions.

Review the core theory, learn to analyze data sets and statistics, and apply a solo project in linear regression using Stata.