diff --git a/R/commonglm.R b/R/commonglm.R
index 642d765..2d89c8e 100644
--- a/R/commonglm.R
+++ b/R/commonglm.R
@@ -867,7 +867,14 @@
 
   modelMatrix <- as.data.frame(model.matrix(model))
   modelMatrix <- modelMatrix[colnames(modelMatrix) != "(Intercept)"]
-  influenceResData[["mahalanobis"]] <- mahalanobis(modelMatrix, center = colMeans(modelMatrix), cov = cov(modelMatrix))
+
+  if (ncol(modelMatrix) > 0) {
+    influenceResData[["mahalanobis"]] <- mahalanobis(modelMatrix, center = colMeans(modelMatrix), cov = cov(modelMatrix))
+  } else if (options[["mahalanobis"]]) {
+    influenceTable$addFootnote(
+      gettext("Mahalanobis distance cannot be computed for the intercept only model.")
+    )
+  }
 
   if (options$residualCasewiseDiagnosticType == "cooksDistance")
     index <- which(abs(influenceResData[["cooksDistance"]]) > options$residualCasewiseDiagnosticCooksDistanceThreshold)
diff --git a/R/generalizedlinearmodel.R b/R/generalizedlinearmodel.R
index b927b82..f29a441 100644
--- a/R/generalizedlinearmodel.R
+++ b/R/generalizedlinearmodel.R
@@ -102,14 +102,14 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,
                limits.min = 0,
                limits.max = Inf,
                exitAnalysisIfErrors = TRUE)
-  
+
   if (length(options$factors) != 0)
     .hasErrors(dataset,
                type = "factorLevels",
                factorLevels.target  = options$factors,
                factorLevels.amount  = '< 2',
                exitAnalysisIfErrors = TRUE)
-  
+
   if (options[["family"]] == "bernoulli") {
 
     if (length(levels(dataset[, options[["dependent"]]])) != 2)
@@ -117,6 +117,9 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,
 
   } else if (options[["family"]] == "binomial") {
 
+    if (any(dataset[, options[["weights"]]] == 0))
+      .quitAnalysis(gettext("The Binomial family requires the weights variable (i.e. total number of trials) to be non-zero."))
+
     if (any(dataset[, options[["dependent"]]] < 0) || any(dataset[, options[["dependent"]]] > 1))
       .quitAnalysis(gettext("The Binomial family requires the dependent variable (i.e. proportion of successes) to be between 0 and 1 (inclusive)."))
 
@@ -414,7 +417,11 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,
   rowNames <- rownames(modelSummary)
 
   if (options[["coefficientCi"]]) {
-    coefCiSummary <- confint(fullModel, level = options[["coefficientCiLevel"]])
+    coefCiSummary <- try(confint(fullModel, level = options[["coefficientCiLevel"]]))
+    if (jaspBase::isTryError(coefCiSummary)) {
+      jaspResults[["estimatesTable"]]$setError("Confidence intervals not available for this model, try other predictors, families, or links.")
+      return()
+    }
     if (length(rowNames) == 1) coefCiSummary <- matrix(coefCiSummary, ncol = 2)
   } else {
     coefCiSummary <- matrix(nrow = length(rowNames),
@@ -477,14 +484,14 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,
   .glmOutlierTable(jaspResults, dataset, options, ready, position = 8, residType = "standardized deviance")
   .glmOutlierTable(jaspResults, dataset, options, ready, position = 8, residType = "studentized deviance")
 
-  .glmInfluenceTable(jaspResults[["diagnosticsContainer"]], 
+  .glmInfluenceTable(jaspResults[["diagnosticsContainer"]],
                      jaspResults[["glmModels"]][["object"]][["fullModel"]],
                      dataset, options, ready, position = 9)
-  
-  .regressionExportResiduals(jaspResults, 
+
+  .regressionExportResiduals(jaspResults,
                              jaspResults[["glmModels"]][["object"]][["fullModel"]],
                              dataset, options, ready)
-  
+
   .glmMulticolliTable(jaspResults, dataset, options, ready, position = 10)
 
   return()
diff --git a/R/regressionlogistic.R b/R/regressionlogistic.R
index 707ed99..9df739d 100644
--- a/R/regressionlogistic.R
+++ b/R/regressionlogistic.R
@@ -361,9 +361,9 @@ RegressionLogisticInternal <- function(jaspResults, dataset = NULL, options, ...
       rows <- vector("list", length(glmObj))
 
       for (midx in seq_along(glmObj)) {
-  
+
         if (options$method == "enter")
-          .linregAddPredictorsInModelFootnote(jaspResults[["modelSummary"]], 
+          .linregAddPredictorsInModelFootnote(jaspResults[["modelSummary"]],
                                               options[["modelTerms"]][[midx]][["components"]], midx)
         mObj <- glmObj[[midx]]
         if (midx > 1) {
@@ -796,6 +796,9 @@ RegressionLogisticInternal <- function(jaspResults, dataset = NULL, options, ...
   mObj   <- glmObj[[length(glmObj)]]
   predictors <- character(0)
 
+  if (is.null(options$modelTerms[[length(glmObj)]][["components"]])) {
+    return()
+  }
   mComponents <- options$modelTerms[[length(glmObj)]][["components"]]
   predictors <- unlist(sapply(mComponents, function(x) if(length(x) == 1) x))
 
@@ -904,7 +907,7 @@ RegressionLogisticInternal <- function(jaspResults, dataset = NULL, options, ...
   container[["subContainer"]] <- createJaspContainer(gettext("Independent - predicted plots"))
   subcontainer <- container[["subContainer"]]
   if(!ready){
-    subcontainer[["placeholder"]] <- createJaspPlot(width = 480, height = 320, dependencies = c("independentVsPredictedPlot", 
+    subcontainer[["placeholder"]] <- createJaspPlot(width = 480, height = 320, dependencies = c("independentVsPredictedPlot",
                                                                                                 "independentVsPredictedPlotIncludeInteractions",
                                                                                                 "independentVsPredictedPlotUseLogit"))
     return()
@@ -914,32 +917,32 @@ RegressionLogisticInternal <- function(jaspResults, dataset = NULL, options, ...
   glmObj <- jaspResults[["glmRes"]][["object"]]
   mObj   <- glmObj[[length(glmObj)]]
   mComponents <- options$modelTerms[[length(glmObj)]][["components"]]
-  
+
   if (options[["independentVsPredictedPlotIncludeInteractions"]]) {
     predictors <- sapply(mComponents, function(x) if(length(x) < 3) x)
   } else {
     predictors <- sapply(mComponents, function(x) if(length(x) == 1) x)
   }
-  
+
   predictions <- predict(mObj, type = "response")
   if(options[["independentVsPredictedPlotUseLogit"]]) {
-    predictions <- log(predictions / (1 - predictions)) 
+    predictions <- log(predictions / (1 - predictions))
     yName <- "Logit Predicted Probability"
   } else {
     yName <- "Predicted Probability"
   }
-  
+
   for (pred in predictors) {
-    
+
     facPredictorIndex <- which(pred %in% options[["factors"]])
-    
+
     for (i in seq_along(pred)) {
-      
+
     predictorLogitPlot <- createJaspPlot(title =  paste(c(pred[i], pred[-i]), collapse = " \u273B "), width = 480, height = 320)
-    predictorLogitPlot$dependOn(c("independentVsPredictedPlot", 
+    predictorLogitPlot$dependOn(c("independentVsPredictedPlot",
                                   "independentVsPredictedPlotIncludeInteractions",
                                   "independentVsPredictedPlotUseLogit"))
-    
+
     binContVar <- FALSE
     if (length(pred) == 1) {
       groupVar <- groupName <- NULL
@@ -967,13 +970,13 @@ RegressionLogisticInternal <- function(jaspResults, dataset = NULL, options, ...
                                          yName = yName,
                                          addSmooth = TRUE,
                                          addSmoothCI = TRUE,
-                                         plotAbove = "none", 
+                                         plotAbove = "none",
                                          plotRight = "none",
                                          showLegend = length(pred) > 1,
                                          legendTitle = if (binContVar) paste0(groupName,"_binned") else groupName,
                                          smoothCIValue = 0.95,
                                          forceLinearSmooth = options[["independentVsPredictedPlotUseLogit"]]))
-    
+
     if(isTryError(p))
       predictorLogitPlot$setError(.extractErrorMessage(p))
     else {
@@ -982,10 +985,10 @@ RegressionLogisticInternal <- function(jaspResults, dataset = NULL, options, ...
       predictorLogitPlot$plotObject <- p
     }
     subcontainer[[paste0(indepVar, groupName)]] <- predictorLogitPlot
-    
+
     }
   }
-  
+
   return()
 }