Introduction to Statistics and Its Applications
Welcome to the Blog Series on statistics. In this Series, we will learn about statistics and how it can be applied in machine learning and data science. However, statistics is not limited to these fields; it has numerous applications across different domains. In this video, we will discuss what statistics is and its applications.
Statistics is a field that deals with the collection, organization, analysis, interpretation, and presentation of data.
Purpose of Statistics
One very important thing to understand is why we perform these tasks. Why do we collect, organize, analyze, interpret, and present data in the form of graphs to stakeholders? Ultimately, the goal is to make informed decisions. When we have data, it is crucial because it allows us to observe behaviors, such as customer behavior, and identify important factors. Based on this, if we make effective decisions, the business will be profitable. Thus, we use statistical tools to make decisions based on data.
Example: Age Feature in Online Shopping
Consider an age feature representing the ages of customers interested in online shopping. Suppose we have data points like 24, 27, 14, 13, 28, 29, 31, and 32. With this information, we might want to decide to whom we should target promotional offers and how to target them. Using statistics, we can calculate important measures such as the mean and median of the age, and analyze the distribution of ages. Various distributions exist, such as Gaussian, normal, standard normal, and log-normal distributions, which we will explore in further examples.
Statistical Analysis and Visualization
Using statistical techniques, we can analyze the data and create visualizations such as histograms, probability density functions (PDF), and cumulative density functions (CDF). For example, a histogram represents the frequency distribution of data as vertical bars. Smoothing the histogram can produce a PDF, which helps us understand the underlying distribution of the data. These tools help us understand the data better and make informed decisions.
The Goal: Understanding Data to Make Decisions
The final goal of statistics is to understand data and make decisions that help grow your business. By analyzing and presenting data effectively, stakeholders can make informed choices.
Application Example: Banking ATM Location Decision
Let’s consider a practical application of statistics in banking. Suppose there is an ATM at location A. The bank is considering opening another ATM at location B, which is about five kilometers away from location A. The bank wants to determine whether opening an ATM at location B will improve service efficiency.
At location A, many people perform transactions and withdraw money. For the new ATM at location B to be viable, there must be sufficient traffic and demand. The bank will analyze data such as the mean number of transactions per month and operational costs like electricity bills. Based on this data, they will decide whether opening an ATM at location B is beneficial. This decision-making process based on data is an example of statistical decision-making.
Data Analysis for Decision Making
The bank will analyze past transaction data from location A, organize it, and present it to stakeholders using charts and graphs. By reviewing these visualizations, they can decide whether to open the new ATM at location B.
Who Uses Statistics?
Statistics is used by everyone in various domains and everyday life. Some key users include:
- Machine learning practitioners
- Data scientists
- Data analysts
- Business intelligence developers and business analysts
- Risk analysts
- Even individuals in daily life, such as housewives or parents
Statistics is pervasive and applied across many fields and roles.
Example: COVID-19 Vaccination Safety
A relevant example is the COVID-19 vaccination process. To determine if the vaccine is safe, researchers selected samples of people and conducted trials. They performed statistical analysis on the results to conclude whether the vaccination is safe for public use. This is another example of how statistics is extensively used in experiments and decision-making processes.
Conclusion and Next Steps
This was the introductory topic on statistics and its applications. Many terms and concepts were introduced, which will be explored in depth in subsequent lectures. Follow along with the examples and try to apply these concepts in your own domain. In the next session, we will discuss many more topics related to statistics.
Key Takeaways
- Statistics is the field that deals with the collection, organization, analysis, interpretation, and presentation of data.
- The primary goal of statistics is to understand data and make informed decisions that can drive business growth.
- Statistical tools help analyze data features such as mean, median, and distributions, and visualize them through charts like histograms, PDFs, and CDFs.
- Statistics is widely applied across various domains including banking, machine learning, data science, business analytics, and even public health for vaccine safety evaluation.