Create statistical visualization for regression analysis results. Help me: 1) Generate scatter plot with regression line and confidence interval, 2) Create residual diagnostic plots (normality, homoscedasticity), 3) Visualize model comparisons (AIC/BIC), 4) Design coefficient plot with error bars, 5) Create correlation matrix heatmap, 6) Format for APA-style publication, 7) Use R (ggplot2) or Python (seaborn)

Prompt

ChatGPTOpenAI

Certainly! Below is a guide and example code for generating various statistical visualizations for regression analysis results using either R (with ggplot2) or Python (with seaborn). I'll provide code snippets for each item you mentioned.

Pre-requisites

For Python, ensure you have the following libraries installed:

pip install pandas numpy seaborn matplotlib statsmodels

For R, make sure you have:

install.packages("ggplot2")
  install.packages("dplyr")
  install.packages("broom")
  install.packages("gridExtra")

1. Scatter Plot with Regression Line and Confidence Interval

Python (Seaborn)

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from statsmodels.regression.linear_model import OLS
import statsmodels.api as sm
# Example Data
np.random.seed(0)
n = 100
x = np.random.normal(size=n)
y = 2 * x + np.random.normal(size=n)
df = pd.DataFrame({'x': x, 'y': y})
# Fit the regression model
model = OLS(df['y'], sm.add_constant(df['x'])).fit()
df['predicted'] = model.predict(sm.add_constant(df['x']))# Plot
sns.regplot(x='x', y='y', data=df, ci=95, line_kws={'color': 'red'})
plt.title('Regression Analysis with Confidence Interval')
plt.xlabel('Independent Variable (X)')
plt.ylabel('Dependent Variable (Y)')
plt.show()

R (ggplot2)

library(ggplot2)
# Example Data
set.seed(0)
n <- 100
x <- rnorm(n)
y <- 2 * x + rnorm(n)
df <- data.frame(x = x, y = y)# Plot
ggplot(df, aes(x = x, y = y)) +
  geom_point() +
  geom_smooth(method = "lm", level = 0.95, color = "red") +
  labs(title = "Regression Analysis with Confidence Interval",
       x = "Independent Variable (X)",
       y = "Dependent Variable (Y)")

2. Residual Diagnostic Plots

Python

import statsmodels.api as sm
# Residuals
residuals = df['y'] - df['predicted']
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
# Normality
sns.histplot(residuals, kde=True, ax=axes[0])
axes[0].set_title('Residuals Distribution')
# Homoscedasticity
sns.scatterplot(x=df['predicted'], y=residuals, ax=axes[1])
axes[1].axhline(0, ls='--', color='red')
axes[1].set_title('Residuals vs Predicted')plt.tight_layout()
plt.show()

R

# Fit the model
model <- lm(y ~ x, data = df)# Residuals
par(mfrow = c(1, 2))
plot(model, which = 1:2) # This includes both Residuals vs Fitted and Normal Q-Q plots

3. Visualize Model Comparisons (AIC/BIC)

Python

from statsmodels.regression.linear_model import OLS
# Fit models
model1 = OLS(df['y'], sm.add_constant(df['x'])).fit()
# Example of a second model
model2 = OLS(df['y'], sm.add_constant(df[['x', 'other_var']])).fit() # assuming 'other_var' exists
# Compare AIC/BIC
aic_values = [model1.aic, model2.aic]
bic_values = [model1.bic, model2.bic]
models = ['Model 1', 'Model 2']fig, ax = plt.subplots()
ax.bar(models, aic_values, alpha=0.6, label='AIC')
ax.bar(models, bic_values, alpha=0.6, label='BIC')
ax.set_title('Model Comparison')
ax.set_ylabel('Information Criterion')
ax.legend()
plt.show()

R

# Fit models
model1 <- lm(y ~ x, data = df)
model2 <- lm(y ~ x + other_var, data = df) # assuming 'other_var' exists
# Collect AIC/BIC
model_comparison <- data.frame(
  models = c("Model 1", "Model 2"),
  AIC = c(AIC(model1), AIC(model2)),
  BIC = c(BIC(model1), BIC(model2))
)
model_comparison_long <- reshape2::melt(model_comparison, id.vars="models")# Plot
ggplot(model_comparison_long, aes(x=models, y=value, fill=variable)) +
  geom_bar(stat='identity', position='dodge') +
  labs(title='Model Comparison', y='Information Criterion')

4. Coefficient Plot with Error Bars

Python

import statsmodels.api as sm
# Get coefficients and conf intervals
params = model.params
conf = model.conf_int()
conf['mean'] = params
# Make DataFrame
coef_df = conf.reset_index()
coef_df.columns = ['Variable', 'lower', 'upper', 'estimate']# Plot
plt.errorbar(coef_df['Variable'], coef_df['estimate'], 
             yerr=[coef_df['estimate'] - coef_df['lower'], 
                    coef_df['upper'] - coef_df['estimate']], 
             fmt='o', capsize=5)
plt.title('Coefficient Plot with Error Bars')
plt.xticks(rotation=45)
plt.ylabel('Estimate')
plt.show()

R

# Get coefficients and confidence intervals
coef_summary <- summary(model)$coefficients
coef_df <- data.frame(Term = rownames(coef_summary),
                      Estimate = coef_summary[,1],
                      CI_lower = coef_summary[,1] - 1.96 * coef_summary[,2],
                      CI_upper = coef_summary[,1] + 1.96 * coef_summary[,2])# Plot
ggplot(coef_df, aes(x=Term, y=Estimate)) +
  geom_point() +
  geom_errorbar(aes(ymin=CI_lower, ymax=CI_upper), width=.2) +
  labs(title='Coefficient Plot with Error Bars', y='Estimate') +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

5. Correlation Matrix Heatmap

Python

# Sample Data
data = df.corr()# Heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(data, annot=True, cmap='coolwarm', square=True)
plt.title('Correlation Matrix Heatmap')
plt.show()

R

# Sample Data
library(reshape2)
correlation_matrix <- cor(df)
melted_cormatrix <- melt(correlation_matrix)# Heatmap
ggplot(melted_cormatrix, aes(Var1, Var2, fill = value)) +
  geom_tile() +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white",
                        midpoint = 0, limit = c(-1,1), space = "Lab",
                        name="Correlation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Correlation Matrix Heatmap")

6. Formatting for APA-Style Publication

In APA style, make sure to use clear titles, refrain from using excessive decoration. Fonts like Arial or Times New Roman, size 12, are often encouraged. When you save your plots:

Save in Python

plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Save in R

ggsave("plot.png", dpi=300, width=6, height=4)

7. Additional Notes

You can run the provided code snippets in your preferred environment to generate the needed visuals. Ensure that your data fits the assumptions and structure of regression analysis. This guide can be modified to suit your dataset or specific analyses as necessary.

{DateTime.UtcNow:HH:mm}

GeminiGoogle

This is a comprehensive request! I'll provide a solution using R with ggplot2 and associated packages (dplyr, broom, tidyr, forcats) to create all the requested visualizations with APA-style formatting.

First, let's make sure you have the necessary packages installed:

# Install packages if you haven't already
install.packages(c("ggplot2", "dplyr", "broom", "tidyr", "forcats", "reshape2"))

Now, let's load them and create some sample data for a multiple regression analysis.

# Load libraries
library(ggplot2)
library(dplyr)    # For data manipulation
library(broom)    # For tidy model output
library(tidyr)    # For data reshaping (e.g., pivot_longer)
library(forcats)  # For factor manipulation (e.g., fct_reorder)
library(reshape2) # For melting correlation matrix
# Set a seed for reproducibility
set.seed(123)
# Generate sample data for multiple regression
n <- 100 # Number of observations
x1 <- rnorm(n, 50, 10) # Continuous predictor 1
x2 <- runif(n, 0, 100) # Continuous predictor 2
x3 <- rbinom(n, 1, 0.5) # Binary categorical predictor
x4 <- rpois(n, 3) # Count predictor (for correlation)
error <- rnorm(n, 0, 15) # Residual error
# Dependent variable (y) based on a linear model
y <- 10 + 0.8 * x1 - 0.3 * x2 + 5 * x3 + 2 * x4 + error
# Create a data frame
df <- data.frame(
  y = y,
  x1 = x1,
  x2 = x2,
  x3 = factor(x3, labels = c("Group A", "Group B")), # Convert x3 to factor
  x4 = x4
)# Fit two regression models for comparison
model_full <- lm(y ~ x1 + x2 + x3 + x4, data = df)
model_reduced <- lm(y ~ x1 + x3, data = df) # A simpler model for comparison

1. Scatter Plot with Regression Line and Confidence Interval

This plot visualizes the relationship between a dependent variable and one of its continuous predictors, along with the fitted regression line and its confidence interval.

# Create the scatter plot
plot_scatter <- ggplot(df, aes(x = x1, y = y)) +
  geom_point(alpha = 0.6, color = "darkgrey") + # Scatter points
  geom_smooth(method = "lm", color = "blue", fill = "skyblue", alpha = 0.4) + # Regression line with CI
  labs(
    title = "Figure 1. Scatter Plot of Y vs. X1 with Regression Line",
    x = "Predictor X1",
    y = "Dependent Variable Y"
  ) +
  theme_minimal() + # Minimal theme for clean look
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.title = element_text(face = "bold", size = 12),
    axis.text = element_text(size = 10),
    panel.grid.major = element_line(linetype = "dotted", color = "grey"),
    panel.grid.minor = element_blank()
  )
print(plot_scatter)# Save the plot (APA style recommends high-resolution images)
ggsave("figure1_scatter_plot.png", plot_scatter, width = 7, height = 5, dpi = 300)

What to look for: This plot helps assess the linear relationship between y and x1. The blue line is the best-fit line, and the shaded area is the 95% confidence interval for this line. If the CI is narrow, it suggests a more precise estimate of the relationship.

2. Residual Diagnostic Plots

These plots are crucial for checking the assumptions of linear regression:

Residuals vs. Fitted Values: Checks for homoscedasticity (constant variance of residuals) and linearity.

Normal Q-Q Plot: Checks for the normality of residuals.

# Extract residuals and fitted values from the full model
model_df <- data.frame(
  fitted = fitted(model_full),
  residuals = residuals(model_full),
  std_residuals = rstandard(model_full) # Standardized residuals for Q-Q plot
)
# a) Residuals vs. Fitted Values Plot (for homoscedasticity)
plot_residuals_fitted <- ggplot(model_df, aes(x = fitted, y = residuals)) +
  geom_point(alpha = 0.6, color = "darkgrey") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") + # Reference line at 0
  labs(
    title = "Figure 2a. Residuals vs. Fitted Values Plot",
    x = "Fitted Values",
    y = "Residuals"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.title = element_text(face = "bold", size = 12),
    axis.text = element_text(size = 10),
    panel.grid.major = element_line(linetype = "dotted", color = "grey"),
    panel.grid.minor = element_blank()
  )
print(plot_residuals_fitted)
ggsave("figure2a_residuals_fitted.png", plot_residuals_fitted, width = 7, height = 5, dpi = 300)
# b) Normal Q-Q Plot of Standardized Residuals (for normality)
plot_qq <- ggplot(model_df, aes(sample = std_residuals)) +
  stat_qq(color = "blue", alpha = 0.6) +
  stat_qq_line(color = "red", linetype = "dashed") + # Reference line
  labs(
    title = "Figure 2b. Normal Q-Q Plot of Standardized Residuals",
    x = "Theoretical Quantiles",
    y = "Standardized Residuals"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.title = element_text(face = "bold", size = 12),
    axis.text = element_text(size = 10),
    panel.grid.major = element_line(linetype = "dotted", color = "grey"),
    panel.grid.minor = element_blank()
  )print(plot_qq)
ggsave("figure2b_qq_plot.png", plot_qq, width = 7, height = 5, dpi = 300)

What to look for:

Residuals vs. Fitted: A good plot shows residuals randomly scattered around zero, with no discernible pattern (e.g., no funnel shape, no U-shape). This indicates homoscedasticity and linearity.

Normal Q-Q Plot: Points should generally fall along the dashed red line. Deviations from the line, especially at the tails, suggest non-normality.

3. Visualize Model Comparisons (AIC/BIC)

This plot helps compare different models based on their AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) values. Lower values generally indicate a better-fitting model, penalizing for complexity.

# Calculate AIC and BIC for both models
model_names <- c("Full Model", "Reduced Model")
aic_values <- c(AIC(model_full), AIC(model_reduced))
bic_values <- c(BIC(model_full), BIC(model_reduced))
# Create a data frame for plotting
model_comp_df <- data.frame(
  Model = factor(model_names, levels = model_names), # Ensure order
  AIC = aic_values,
  BIC = bic_values
)
# Reshape data to long format for ggplot (easier for grouped bar charts)
model_comp_long <- pivot_longer(model_comp_df, cols = c("AIC", "BIC"),
                                names_to = "Criterion", values_to = "Value")
plot_model_comp <- ggplot(model_comp_long, aes(x = Model, y = Value, fill = Criterion)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.7) +
  geom_text(aes(label = round(Value, 2)),
            position = position_dodge(width = 0.8), # Match dodge width
            vjust = -0.5, size = 3.5, color = "black") + # Add value labels
  labs(
    title = "Figure 3. Model Comparison (AIC and BIC)",
    x = "Model",
    y = "Criterion Value"
  ) +
  scale_fill_manual(values = c("AIC" = "lightblue", "BIC" = "steelblue")) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.title = element_text(face = "bold", size = 12),
    axis.text = element_text(size = 10),
    legend.title = element_blank(), # Remove legend title
    legend.position = "top",
    panel.grid.major.x = element_blank(), # Remove vertical grid lines
    panel.grid.major.y = element_line(linetype = "dotted", color = "grey"),
    panel.grid.minor = element_blank()
  )print(plot_model_comp)
ggsave("figure3_model_comparison.png", plot_model_comp, width = 8, height = 6, dpi = 300)

What to look for: Lower AIC/BIC values suggest a better model. This plot visually highlights which model is preferred based on these criteria.

4. Coefficient Plot with Error Bars

This plot displays the estimated coefficients of your regression model along with their confidence intervals, making it easy to see the magnitude and statistical significance of each predictor.

# Use broom::tidy to get a clean data frame of coefficients
coef_df <- tidy(model_full, conf.int = TRUE) %>%
  filter(term != "(Intercept)") %>% # Exclude the intercept for clarity
  # Reorder terms by estimate for better readability
  mutate(term = fct_reorder(term, estimate))
plot_coefficients <- ggplot(coef_df, aes(x = estimate, y = term)) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "grey") + # Reference line at 0
  geom_pointrange(aes(xmin = conf.low, xmax = conf.high),
                  color = "blue", size = 0.8, linewidth = 1) + # Confidence intervals
  geom_point(color = "darkblue", size = 3) + # Coefficient estimates
  labs(
    title = "Figure 4. Regression Coefficients with 95% Confidence Intervals",
    x = "Coefficient Estimate",
    y = "Predictor Variable"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.title = element_text(face = "bold", size = 12),
    axis.text = element_text(size = 10),
    panel.grid.major.x = element_line(linetype = "dotted", color = "grey"),
    panel.grid.major.y = element_blank(), # No horizontal grid lines
    panel.grid.minor = element_blank()
  )print(plot_coefficients)
ggsave("figure4_coefficient_plot.png", plot_coefficients, width = 8, height = 6, dpi = 300)

What to look for:

Coefficients whose confidence intervals do not cross the zero line are statistically significant (at the chosen confidence level, usually 95%).

The length of the error bar indicates the precision of the estimate. Shorter bars mean more precise estimates.

The position of the point shows the direction and magnitude of the effect.

5. Correlation Matrix Heatmap

This visualization shows the pairwise correlations between all numeric variables in your dataset, offering a quick overview of relationships.

# Select only numeric columns for correlation calculation
numeric_df <- df %>% select_if(is.numeric) # Selects y, x1, x2, x4
# Calculate the correlation matrix
corr_matrix <- cor(numeric_df)
# Melt the correlation matrix into a long format suitable for ggplot2
# Use reshape2::melt for this
melted_corr <- melt(corr_matrix, varnames = c("Var1", "Var2"))
plot_corr_heatmap <- ggplot(melted_corr, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile(color = "white", linewidth = 0.5) + # Tiles with white borders
  scale_fill_gradient2(low = "steelblue", high = "firebrick", mid = "white",
                       midpoint = 0, limit = c(-1,1), space = "Lab",
                       name = "Correlation\nCoefficient") + # Color gradient
  geom_text(aes(label = round(value, 2)), color = "black", size = 3.5) + # Correlation values
  labs(
    title = "Figure 5. Correlation Matrix Heatmap"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.title = element_blank(), # Remove axis titles
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 10, face = "bold"),
    axis.text.y = element_text(size = 10, face = "bold"),
    panel.grid.major = element_blank(), # Remove grid lines
    panel.border = element_blank(),
    panel.background = element_blank(),
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9)
  ) +
  coord_fixed() # Ensure square tiles for a true heatmapprint(plot_corr_heatmap)
ggsave("figure5_correlation_heatmap.png", plot_corr_heatmap, width = 7, height = 6, dpi = 300)

What to look for:

Colors represent the strength and direction of the correlation (e.g., red for strong positive, blue for strong negative, white for no correlation).

Values on the tiles provide the exact correlation coefficient.

This helps identify multicollinearity issues (highly correlated predictors) or strong relationships between the dependent variable and predictors.

6. Formatting for APA-Style Publication

Throughout the code, I've applied APA-style formatting principles:

Figure Titles: Centered, bold, and prefixed with "Figure X." followed by a descriptive title (e.g., title = "Figure 1. Scatter Plot of Y vs. X1 with Regression Line"). In a full APA paper, the figure title would typically appear below the figure, but for standalone plots, including it in the plot title is common for clarity.

Axis Labels: Bold, clear, and descriptive (e.g., x = "Predictor X1").

Font: ggplot2 defaults to a sans-serif font, which is generally acceptable for APA.

Minimalist Themes: theme_minimal() is used as a base to avoid distracting backgrounds or heavy borders.

Grids: Grid lines are typically light and subtle, or removed if they clutter the plot. I've used linetype = "dotted" and color = "grey" for major grids and removed minor grids.

Legends: Clear and concise, often without a title (legend.title = element_blank()) or with a descriptive, bold title.

Colors: Generally, use clear, distinct colors. Avoid overly bright or clashing colors.

High Resolution: ggsave() calls include dpi = 300 (dots per inch) for high-quality image output suitable for publication. width and height are set to reasonable sizes in inches.

Remember that for final publication, you would insert these generated .png files into your document and provide detailed captions below each figure in your manuscript, adhering strictly to APA guidelines (e.g., "Figure 1. Scatter plot depicting the relationship between Y and X1. The solid blue line represents the linear regression fit, and the shaded area indicates the 95% confidence interval.").

This comprehensive set of visualizations should effectively summarize your regression analysis results for an APA-style publication!

{DateTime.UtcNow:HH:mm}

Each model brings unique strengths to this type of query
Response styles vary significantly between models
Consider your specific use case when choosing between these models

Try This Comparison Yourself

Want to test these models with your own prompts? Visit SNEOS.com to compare AI responses side-by-side in real-time.

This comparison was generated using the SNEOS AI Comparison ToolPublished: October 15, 2025 | Models: ChatGPT, Gemini

Pre-requisites

1. Scatter Plot with Regression Line and Confidence Interval

Python (Seaborn)

R (ggplot2)

2. Residual Diagnostic Plots

Python

R

3. Visualize Model Comparisons (AIC/BIC)

Python

R

4. Coefficient Plot with Error Bars

Python

R

5. Correlation Matrix Heatmap

Python

R

6. Formatting for APA-Style Publication

Save in Python

Save in R

7. Additional Notes

1. Scatter Plot with Regression Line and Confidence Interval

2. Residual Diagnostic Plots

3. Visualize Model Comparisons (AIC/BIC)

4. Coefficient Plot with Error Bars

5. Correlation Matrix Heatmap

6. Formatting for APA-Style Publication

Analysis

Response Characteristics

Key Takeaways

Try This Comparison Yourself