The role of statistics in business decision-making

Statistics plays a critical role in business decision making, as it provides a powerful tool for analyzing data and drawing meaningful conclusions. Through statistical analysis, companies can better understand patterns, trends, and relationships in data, enabling them to make informed and strategic decisions. In this article, we’ll explore how statistics can aid in business decision making and look at a practical example using Python.

Importance of statistics in business decision making

1. Trend analysis: Statistics allows you to analyze historical data and identify significant trends. For example, a company can use statistical techniques to analyze past sales and predict future trends, which helps planning and decision-making about production and inventory.
2. Risk assessment: Statistics provides tools to assess and manage risks in the business environment. Through data analysis, potential risks can be identified and the probability of occurrence calculated. This helps companies make informed decisions about risk mitigation strategies.
3. Hypothesis testing: Statistics offers methods for testing hypotheses and making decisions based on empirical evidence. For example, a company may use statistical tests to assess the impact of a new marketing strategy or determine whether a variation in the production process has a significant effect on product quality.
4. Optimization of resources: Statistics allows you to maximize efficiency and optimize the allocation of limited resources. For example, through statistical analysis, a company can identify areas of underperformance and make decisions to improve productivity and reduce costs.

Now let’s look at a practical example using Python to analyze business data.

Practical example: Data analysis using Python

In this example, we’ll use the Python Pandas library to analyze a set of business data. Suppose we have a dataset that contains information about a company’s employees, including their department, experience level, age, and salary.

import pandas as pd

# Crear un DataFrame con los datos
data = {
    'Departamento': ['Ventas', 'Marketing', 'Recursos Humanos', 'Finanzas'],
    'Nivel de experiencia': ['Junior', 'Senior', 'Senior', 'Junior'],
    'Edad': [28, 35, 42, 30],
    'Salario': [50000, 75000, 90000, 55000]
}

df = pd.DataFrame(data)

# Mostrar el DataFrame
print(df)

SALIDA:
 Departamento Nivel de experiencia  Edad  Salario
0            Ventas               Junior    28    50000
1         Marketing               Senior    35    75000
2  Recursos Humanos               Senior    42    90000
3          Finanzas               Junior    30    55000

The above code creates a DataFrame with four variables: ‘Department’, ‘Experience Level’, ‘Age’ and ‘Salary’. Now, we can use the functions of Pandas to perform basic statistical calculations. The mean and mean deviation (the deviation of the data around the mean value) are used.

# Calcular la media del salario
media_salario = df['Salario'].mean()

# Calcular la desviación estándar de la edad
desviacion_edad = df['Edad'].std()

# Mostrar los resultados
print("Media del salario: ", media_salario)
print("Desviación estándar de la edad: ", desviacion_edad)

SALIDA:
Media del salario:  67500.0
Desviación estándar de la edad:  6.238322424070967

The above code calculates the mean salary and the standard deviation of age using the Pandas mean()  and std() functions  . These statistical measures provide important information about the data and can aid decision-making.

Creating statistics charts

Here’s an example of how to create a chart using the above data with the matplotlib library in Python. In this case, we will use a bar graph to visualize employee salaries by department. In addition, a variable “colors” is created to color the bars.

import matplotlib.pyplot as plt
import matplotlib.animation as animation

# Crear una figura y un eje
fig, ax = plt.subplots()

# Definir los datos de los departamentos y salarios
departamentos = df['Departamento']
salarios = df['Salario']

# Crear una lista de colores para las barras
colores = ['blue', 'green', 'orange', 'red']

# Crear las barras iniciales
barras = ax.bar(departamentos, salarios, color=colores)

# Función de actualización para la animación
def animate(i):
    # Actualizar las alturas y colores de las barras con los salarios y colores correspondientes
    for j, b in enumerate(barras):
        b.set_height(salarios[j])
        b.set_color(colores[j])

        # Obtener las coordenadas x e y de la barra
        x = b.get_x() + b.get_width() / 2
        y = b.get_height()

        # Mostrar el valor encima de la barra
        ax.text(x, y, salarios[j], ha='center', va='bottom')

# Crear la animación
anim = animation.FuncAnimation(fig, animate, frames=len(df), interval=1000, repeat=True)

# Mostrar la animación
plt.show()

In this code, a block is added at the end to save the generated animation as a PNG file using the animation’s save() method  . The file will be saved with the name “grafico_animado.png”. You can change the name and location of the file according to your needs.

At the end of the code execution, the animation will be displayed and at the same time saved as a PNG file for later download. And you get a graph like the following:

Summary

Therefore, statistics plays a crucial role in business decision making by providing tools to analyze data, identify patterns and trends, assess risks and make evidence-based decisions. With the use of Python and libraries such as Pandas, companies can perform advanced statistical analysis to gain valuable insights and make informed decisions.

Python codes have been generated from ChatGPT which helps in creating documents and polishing codes.