Logistic Regression Made Simple

Learn to run logistic regression in R Studio using basic statistical commands, with the General Social Survey 2016 dataset, and explore familiarization with essential R logistic regression functions.

Explore why R is a powerful tool for statistical analysis, compare it with other platforms, and learn to interpret basic statistical commands in R while understanding timeless concepts behind analysis.

Analyze 2016 survey dataset of nearly 3000 respondents from Association of Religion Data Archives to explore socioeconomic features such as marital status, education, work hours, and employment in logistic regression.

Explore the General Social Survey 2016 codebook, understand data formats (SPSS, Stata, CSV), and interpret key variables like work status codes from full time to part time.

Explore how the dataset is structured in Excel, with year, respondent IDs, and coded survey data. Learn how to decode variables such as working status and gender from the codebook.

Explore data types in the dataset, including quantitative and categorical features with ordinal encoding. Apply regression analysis to general social survey data and interpret outputs.

Learn to load general social survey data in R studio with read Excel and dplyr, then group by gender to summarize age statistics (mean, median, sd, range, iqr) using describe.

Explain how to obtain descriptive statistics in R using stat.dsc and table from the pass tex package for gender, degree, and marital status, including mean, median, and standard deviation.

Demonstrates using R commands to compute descriptive statistics with table and summary, perform chi-square tests, and explore margins and cross tables for understanding dependencies between variables.

Explore generalized linear models in R, handling binary, ordinal, nominal, and count responses with GLM, cumulative link models, and regression using vglm and vgam packages.

Review key logistic regression functions in R and essential statistical commands, and prepare to compare models using goodness of fit statistics.

Explore logistic regression concepts and assess model goodness of fit using R square, AIC, and BIC to compare models and ensure robust performance.

Explore logistic regression challenges by evaluating goodness-of-fit metrics, using the Bernoulli distribution to model binary outcomes, and applying maximum likelihood to estimate parameters.

Explore deviance, log-likelihood ratio, Macfadden's pseudo R-squared, and AIC/BIC to compare logistic regression models—from saturated to full, reduced, and null—and assess fit.

Master macfadden's pseudo r-square and statistics for comparing different logistic regression models. Gain solid understanding and syntax knowledge to implement these logistic regressions, including goodness-of-fit statistics in r studio.

Explore simple binary logistic regression with one predictor and one binary response variable in RStudio, forming a binary logistic equation for outcomes 0 or 1.

Explore the simplest form of binary logistic regression using General Social Survey 2016 data to predict an individual's bachelor's degree from the mother's bachelor's degree and assess potential correlation.

Explore why linear regression fails with binary outcomes and how logistic regression uses logit odds to model a binary response, with an R example.

Implement binary logistic regression in R to predict a child's bachelor's degree from the mother's bachelor's degree, and interpret the logit coefficient as indicating higher odds with significance (p<0.05).

Compute Nagelkerke and Macfadden pseudo R-squared values for a simple logistic model in R, and interpret the log-likelihood ratio test with AIC and Bic for nested and non-nested models.

Conclude the section by summarizing the binary logistic regression implementation in R and previewing multiple predictor logistic regression, where the response variable stays the same as predictors increase.

Explore multiple predictor logistic regression for binary outcomes. Build on simple logistic regression by incorporating binary or continuous predictors and a shared response variable.

Extend binary logistic regression to a multiple predictor model by adding father's education, and interpret the results, noting the mother's education coefficient of about 0.316 raises logit odds.

In R, implement a multiple predictor logistic regression with binary mother and father bachelor’s degree status to predict degree status; interpret coefficients and note positive correlations and improved model fit.

Explore multiple predictor logistic regression, assess goodness of fit with nagelkerke pseudo r-squared, AIC, and BIC; show that including father's education improves model performance over a single predictor.

Explore implementing multiple predictor logistic regression in R and prepare for ordinal logistic regression with ordered predictors and levels, noting real-world complexity beyond simple models.

Explore simple logistic regression for ordinal variables and learn how to handle ordered health-status data from the General Social Survey 2016, using a distinct ordinal logistic regression model.

Explore implementing logistic regression with ordinal data using the adult dataset from the UCI repository, converting ordered categories into codes and interpreting results in R.

Explore how education level predicts income using ordinal logistic regression, converting variables to ordinal data and testing whether higher education relates to higher income.

Apply ordinal logistic regression on adult dataset with a cumulative link model. Interpret education code 0.56232 as increasing log odds of income over 50k.

Model Klemtu uses a binary bachelor’s predictor to model an ordinal income outcome with the CLM function. The bachelor’s degree increases the log odds of earning over 50k, with p<0.05.

examine the third logistic regression model using education years as a continuous predictor for income above 50k, with a positive coefficient and a very small p-value.

Compare three logistic models using pseudo r-square, aic, and bic; the third model with education code, bachelors, and education years shows the best fit.

Predict whether income exceeds 50k using three models with ordinal education code, binary bachelors, and education years, and interpret probabilities with the effects library in R.

Implement ordinal logistic regression with a single predictor and outline plans to add more predictors to improve prediction of the same response variable.

Introduce multiple logistic regression for ordinal variables by adding predictors such as gender to assess effects on family income class (>50k or <50k) and model robustness.

Explore logistic regression with multiple predictors in R, using education code, gender code, and race code to predict income greater than 50k, and interpret logit coefficients.

Introduce a random code to test its impact on family income, alongside education code. The random code shows no effect (p = 0.98), while education remains significant.

Compare Nagelkerke pseudo R square, AIC, and BIC for logistic models predicting family income from education, gender, and race codes versus a random Excel code; the first model performs better.

Use logistic regression in R with the effects package to predict family income from education and gender codes, interpreting predicted probabilities for income brackets across education levels and gender.

Conclude the section on implementing ordinal logistic regression in R and outline upcoming discussions on model comparisons using pseudo r square and goodness of fit statistics.

Compare different types of logistic regression models using AIC, BIC, and log-likelihood, and assess them with ANOVA and p-values to identify models with better goodness of fit.

Compare binary and ordinal logistic regression models in R using parental education to predict education, and assess significance with chi-square via anova; three-model analyses on the UCI adult dataset.

Compare model one and model two for predicting child education in R; model two adds farthest education level and shows lower residual deviance, indicating better fit.

Apply anova to compare three ordinal models predicting family income using income >50k, gender, education, and race codes. Model five fits best (lowest AIC, highest log-likelihood), indicating predictors influence income.

Conclude the model comparison section and preview generalised logistic regression, highlighting upcoming differences from prior material.

Explore generalized ordinal regression, where effects vary by health status levels. Use marital status to illustrate transitions among health levels and model interpretation in R.

Explore how education levels influence family income using the generalized ordinal regression model, contrasting proportional odds with level-dependent coefficients to reveal varying transition effects on income beyond 50k.

Explore how generalized linear models handle the proportional hazard assumption and when to use a partial proportional model by letting only violating variables vary across outcome levels.

Implement generalized logistic regression in R with vg glm to predict health status from age, education, mental status, gender, full-time work, and income, interpreting three binary models and logit differences.

Showcase goodness of fit for logistic regression using Nagelkerke, AIC, and BIC, then compare models and interpret exponentiated coefficients and odds ratios, including marital status (0.564) in health category odds.

Compare model one and model two in R, interpret odds ratios 0.667 and 0.886 for married versus unmarried health status, and assess fit with Macfadden's pseudo R-square, AIC, and BIC.

Explore how to implement logistic regression in R and interpret odds ratios across health status categories, comparing married versus unmarried individuals using logit link for multiple thresholds.

In this R implementation of ordinal regression, log odds for category two or above differ by marital status. Married individuals have 1.77 times higher odds than unmarried, indicating health benefits.

Interpret the 1.772 odds ratio for married versus unmarried status in generalized ordinal regression, and compare models with and without family income using log likelihood and LR test.

Master the generalized logistic regression model and its implementation in R, and preview the partial proportional Ode model that assigns variables across outcome levels.

Identify which predictors violate the proportional assumption and apply the partial proportional model within a generalized ordinal regression framework, then implement and interpret it in R.

Explore partial proportional odds models in logistic regression by identifying predictors that violate the proportional odds assumption and allowing only those to vary across health status levels.

Shows implementing an ordinal logistic regression in R with CLM on the GSS 2016 data, testing proportional odds, and identifying marital status and family income as violating the assumption.

Explore implementing a proportional odds model in R using GLM, and interpret health status with exponentiated coefficients and odds ratios.

Compare goodness-of-fit statistics for two models: an ordinal regression model via CLM function and a partial proportional odds model via GLM function, showing model two has lower AIC and BIC.

Conclude by reviewing the partial proportional model in R and its interpretation, and preview multinomial logistic regression and its R implementation.