Boxplots are a great way to visualize the distribution (min, max, quartiles, and median) of a dataset. However, they do not display the mean and standard error of the dataset by default.
In R, you can use the ggplot2 package to add mean and standard error on the boxplot.
The following example explains how to add mean and standard error on a boxplot using the ggplot2 package in R.
We will use the built-in mtcars data.

Boxplots are a great way to visualize the distribution (min, max, quartiles, and median) of a dataset. However, they do not display the mean of the dataset by default.
In Python matplotlib, you can use the showmeans parameter from the boxplot function to show the mean on the boxplot.
The following example explains how to plot a boxplot and show the mean on it using the matplotlib Python package.
Let’s create a random dataset for three groups using the rand function from NumPy,

Boxplots are a great way to visualize the distribution (min, max, quartiles, and median) of a dataset.
In Python, you can generate a boxplot using the boxplot function from matplotlib. By default, the boxplot displays the median line on the boxplot.
However, in some cases, we do not want to display the median line or want to add another metric line (e.g. mean) on the boxplot.
In matplotlib, you can remove the median line by passing the medianprops parameter with linewidth=0 value to the boxplot function.

The shading of confidence intervals on a line plot is useful to understand the range within which the true unknown parameter value lies with a certain level of confidence (e.g. 95% confidence interval).
In R, you can various functions such as ggplot2 and polygon to shade the confidence intervals with specific values given in the table.
The following example explains how to shade the confidence intervals using ggplot2 and polygon functions in R.

The calculation of confidence interval is useful to understand the range within which the true parameter value lies with a certain level of confidence (e.g. 95% confidence interval).
In this article, we will discuss how to calculate the 95% confidence intervals for grouped data using the dplyr package in R.
Example 1 Load the built-in mtcars data. This dataset contains the 11 variables for various observations of car models.
data('mtcars') # view data frame head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.

The binomial distribution is commonly used in statistics for modeling binary outcomes, such as success/failure, yes/no, etc.
The binomial distribution is a discrete probability distribution and based on two main parameters: the number of trials (n) and the probability of success (p).
Binomial confidence intervals estimate the range of values where certain outcome proportion such as success rate (true population probability of successes) will likely fall.
In Python, binomial confidence intervals can be calculated using the proportion_confint() function from the statsmodels package.