28 Statistics Examples in Real Life

Statistics

Statistics is a subdomain of mathematics that deals with the collection, organisation, description, interpretation, analysis, synthesis, and presentation of data. Statistical analysis can be broadly classified into seven categories, namely descriptive statistics, inferential statistics, predictive analysis, prescriptive analysis, causal analysis, mechanistic analysis, and exploratory data analysis. In descriptive statistics, the data is summarized through given observations, while inferential statistics mainly deals with the interpretation of the conclusions derived through descriptive statistics. The application of statistics can be observed in industries, hospitals, educational institutes, retail enterprises, information technology, artificial intelligence, research and development, transportation, jurisdiction, and various other related fields. The statistics field is closely correlated to probability and logic theory. Statistics help a person analyse, organise, and present data in a properly organised manner. Statistical data can be arranged in tabular, graphical, pictorial, or any other form desired by the user. Also, statistics eases the process of analysation and processing of bulk data.

Examples of Statistics in Real Life

There are a variety of applications used in our daily life that tend to make use of statistics and related theories. Some of them are listed below:

1. Record of Production Goods and Services

Statistics play a prominent role in performing the production analysis at any workplace. Irrespective of the fact that the organisation may be a goods manufacturing firm or services providing company, statistic models can be applied in either case for different applications such as to configure and estimate the performance of the workers, keep a track of the produced goods, evaluate the productivity and efficiency parameters, etc. The factors that are considered in the statistical evaluation of efficiency and productivity at a firm mainly depend on the number of units of goods produced or tasks accomplished by the employees in a particular duration of time, the number of sales attained on an average by each individual working at the organisation, number of gained and retained customers, import and export rate and frequency of goods and services, purchase and utilisation of resources, etc. Maintaining a proper record of production goods and services helps an organisation improve the production quality index, and customer satisfaction ratio, resolve manufacturing problems that arise during or after the application of a particular approach, and manage the available or newly gained assets. Also, performing statistical analysis on the goods and services production data obtained before and after the implementation of a certain strategy helps the investors and research personnel to estimate the risk and success or failure chances of the scheme. This tends to provide the members of the organisation with an idea of whether or not the implementation of the new business technique is a good idea.

Record of Production Goods and Services

2. Stock Market Data Analysis

Stock market analysis is a classic example of statistical analysis in real life. The investor or the consumer willing to invest in the market tends to take all the available data from the market and perform research and analysis on it with the help of various statistical models to determine the performance portfolio of different investments. This helps the user improve his/her chances of making the most appropriate choice of all the available options. To simplify this process, a variety of software, web pages, and mobile applications have been developed and are available over the internet to educate a person about the working of the stock market and to properly guide him/her throughout the process of making an investment.

Stock Market Data Analysis

3. Quality Department of a Company

The quality control and assurance department of an organisation tend to form a classic example of real-life applications where the demonstration of statistical analysis can be easily observed. In simple words, the quality of a product can be defined as its fitness to the purpose for which it was primarily designed and manufactured. The parameters that help judge the fitness of the purpose of a product include relevance, accuracy, accessibility, timeliness, reliability, comparability, coherence, manufacturing and selling cost, security, privacy, safety, flexibility, wear and tear rate, estimated life span, compatibility with other products, etc. There are numerous advantages of using statistical measures in quality checks. For instance, advanced statistical analysis of the products manufactured in an industry helps avoid the production of faulty goods, thereby improving resource utilization. Similarly, the detailed and timely quality analysis of real-time data assures a significantly higher production efficiency rate of the new product at the time of launch and guarantees the user has complete control on the manufacturing process afterwards. Also, broadcasting the statistical quality check report helps gain the consumer’s interest and faith in the product.

Quality Department of a Company

4. Weather Forecasting

Weather Forecasting is yet another example of a real-life application that makes use of statistical analysis. Weather forecasting basically depends on predicting the probability of occurrence of a particular event based on a collection of past or historical data. To perform weather forecasting with utmost efficiency, the historical trends related to the weather and climate conditions such as air temperature, pressure value, magnitude of humidity, air quality index, the appearance of clouds, speed and direction of winds, precipitation levels and frequency, etc. are captured in form of sample datasets. The bunch of raw data is then fed to algorithms that perform the necessary computation and analysis to draw out conclusions. Weather forecasting basically falls under the category of inferential statistics.

Weather Forecasting

5. Medical Records

Hospitals, medical institutes, research and development labs, etc. receive a bulk of data on daily basis and tend to store the received data in a structured, unstructured, or semi-structured manner. The data obtained at healthcare organisations is crucial and should be maintained properly. Generally, the information contained in medical records includes the name, age, and gender of the patient, date of admission, type of disorder, medical history, assigned medical personnel, prescribed medicines and treatment, etc. Using statistics in medical records helps in the proper organisation of the information regarding a patient in a particular format, segregation of the information or data points of one patient’s record from the other, keeps the records up to date and readily accessible at every point of time, reduces chances of fatal errors, eases the billing procedure, etc.

Medical Records

6. Sales Tracking

The sales department serves to be one of the most crucial units of an organisation. The sales unit tends to develop the necessary resources and financial assets that are required to run a business. The process of selling a good or service to a customer typically involves exchanging a commodity for money or any other valuable asset. The process of sales can be broadly classified into a number of categories, namely, inside sales, outside sales, agency sales, B2B sales, B2C sales, etc. The strategies and methodologies for selling goods to the target audience are closely monitored by the analysts and are required to be selected with high accuracy levels and precision. To enhance the process of selling goods and services to the consumers, a sales statistical survey is generally carried out at regular intervals of time in every organisation. The survey may include customer reviews, sales revenue generated by an individual member of a firm, or the monetary value of assets accumulated by an organisation in a given amount of time. Sales tracking typically involves monitoring and analysis of different stages of a sales process and helps locate the points that need to be enhanced so as to improve the selling rate. Sales tracking is generally performed by feeding the sales data to various statistical models, techniques, and algorithms and deriving a conclusive report from the analysis to plan the next step or strategy model to prevent loss and make gains.

Sales Tracking

7. Health Care Departments

Statistical analysis serves to be one of the best tools for health care providers. This is because observing the current statistical trends and trails helps the healthcare providers to get an insight into the regional state of public health in a particular area, allows them to review and perform a comparative study with the state, national, or international trends, and introduce modifications in the current system accordingly. The information required to perform health statistics usually includes the number of people suffering from a particular disease, the average time taken by the patients to recover from a medical condition, the effectiveness and side-effects of a particular treatment, quality of the healthcare facilities in the surroundings, availability and ease of accessibility to various healthcare provisions to people, frequency and persuasiveness of health awareness programs, number of births and deaths in a particular area, etc. This data is generally collected by government officials, private researchers and experts, and non-profit agencies and firms. After collecting the data, a proper record is required to be maintained in an organised manner for current analysis and for future references. The raw data does not convey any useful information until it is fed to a certain statistical model or algorithm. The information contained in the data set is then extracted and is further used to locate the useful patterns and features in the data to design parameters and measures to prevent the spread of disease, personalise patient care and monitoring systems, and accurately calculate the health insurance rates, etc.

Health Care Departments

8. Budgeting and Finance

Budgeting is basically the process of estimating revenue and expenses for a bunch of dedicated tasks or subdomains for a certain amount or period of time. Likewise, finance is the department that deals with the management of money or other related valuable assets. Both the budgeting and finance procedures make use of statistical analysis to accomplish the tasks. There exist a variety of advantages of using statistics in budgeting, finance, and other related fields. For instance, it enables an adequate usage of the available resources, helps manage and control expenses, allows strategical planning of every course of action at a firm, improves and keeps a record of cash flow, etc. Generally, statistical analysis in budgeting tends to use variance analysis to reduce budget variance. Variance analysis further makes use of elementary statistical techniques such as linear regression, the measure of central tendencies, skewness of data, qualitative and quantitative methodologies of data analysis, etc.

Budgeting and Finance

9. Population Record

Census data or population records tend to form yet another example of real-life applications where the implementation of statistics can be observed easily. Census data is basically used to store various details of people such as name, date of birth, address, occupation, relationship status, profile, etc. in an organised format. Statistical analysis applied to census data and population records helps locate the trends and the patterns in the data that can be useful to make appropriate economic, political, financial, and other related schemes and decisions. The advantage of using statistical methodologies and approaches in such applications include improved decision making, estimating the best manner for distribution and usage of the available resources among consumers, performing a comparative study, computing the average productivity of the residents of a particular place, etc. Also, the organised storage of data serves to be useful for various future applications.

Population Record

10. Educational Data

Educational data is a collection of all the information regarding a student studying, a teacher teaching, or any other professional working at an educational institute. The information or data points typically include name, date of birth, date of enrolment, the current status of involvement in different institutional practices, roles and responsibilities, performance parameters, achievements, etc. The storage of such bulk data is a difficult task as the data keeps building up every day. To resolve this difficulty and to minimize the complexity to interpret the data, statistical analysis tools are generally employed. Implementation of statistics in managing educational data helps organise the data in a presentable manner that is easy to be analysed. Also, one can perform group comparisons as well as individual comparisons to evaluate the performance of an individual with the help of statistics. Other advantages of using statistics to analyse educational data include ease of representing the data in pictorial, graphical, or any other format, determining the success or failure of implementation of a certain amendment in the usual working procedure, enhancing professional productivity, analysing problems beforehand, maintaining a permanent record for future references, etc.

Educational Data

11. Natural Disaster Prediction

A natural disaster is a destructive event that occurs due to natural forces, effects, and phenomena. Such events tend to cause harm to biological as well as man-made resources that are present on the earth and may also lead to loss of life. Some examples of natural disasters include earthquakes, hurricanes, cyclones, floods, typhoons, tsunamis, landslides, etc. The occurrence of most natural disasters is unpredictable and cannot be estimated accurately; however, applying statistical models to the analysis of the data set that contains the history and frequency of occurrence of a particular natural disaster in the past may help get an idea of if or when the similar kind of event is expected to occur in the near future. This helps the people be aware of the kind of mishappening that may occur and be prepared with the preventive measures accordingly. Also, using statistics for natural disaster prediction allows the researchers and analysts to configure ways for optimum utilisation of the available resources and to formulate strategies to overcome the possible side-effects of every particular disaster individually.

Natural Disaster Prediction

12. Pandemic Analysis

A pandemic is basically the outbreak and spread of a particular disease on a worldwide level, i.e., to more than one continent. To date, the pandemic of diseases such as cholera, plague, influenza, HIV/AIDS, swine flu, tuberculosis, Russian flu, COVID-19, etc. has been observed. Pandemics generally can be described with the help of waves or phases. The world health organisation or WHO has described six phases of a pandemic alert with each phase having its own significance. Phase 1 is considered to be the lowest level of pandemic alert. It indicates the circulation of a particular disease among animals and implies no or low risk to humans. Phase 2 specifies the instances that depict the transmission of the virus from animals to humans, thereby implying the possibility of the virus triggering a pandemic situation. Likewise, the other phases of the pandemic preparedness plan are defined according to the rate, severity, and magnitude of the spread of a particular disease. Phase 6 is the last phase of the pandemic situation and describes the rapid widespread and sustained transmission of the disease among humans. Keeping a record of the number of infectious cases observed every day in a pandemic situation is a complicated task, which is why for this purpose certain statistical tools and methodologies are implemented. Also, the use of statistical measures in pandemic analysis helps improve the strategies to remain aware and prepared for the current or future problematic conditions, evaluate the availability of resources and configure their optimum utilisation, maintain a detailed report of the number of infected and cured patients for future references, observe the pattern in the data to depict the portion of the population that gets most affected by a particular disease, etc.

Pandemic Analysis

13. Political Campaigns

Statistical measures and methodologies also have political applications. Diplomats, lobbyists, and political scientists tend to make use of political statistics as a prime tool for their research and analysis. Generally, political statistics is used to design political theories, draft policies, and formulate campaign strategies. For such purposes, the most commonly used statistical models and methodologies include bivariate regression, multiple regression, time-series cross-sectional (TSCS) and panel analysis, Chi-square and T-tests, normality testing, etc. To begin the process of statistical analysis for political applications, the researchers, analysts, and volunteers are required to gather as much relevant data as possible. Political data can be acquired by setting up interviews, obtaining verbal or written feedback from the public, and conducting surveys. The data demonstrating the number of historical wins of a particular political party help estimate its chances of losing or winning the current elections. Similarly, the data collected by the officials in charge that contains the information about the expectations of people from the candidate allows them to design their campaigning agendas accordingly. Here, the use of statistical analysis simplifies the process of selecting campaign agenda by skewing the data towards the agendas that the majority of people expect to be fulfilled. In absence of political statistics, identification and location of the prime agendas out of the others becomes relatively difficult. Also, statistics in politics help maintain the expenses and keep a proper record of the cash flow.

Political Campaigns

14. Sports

Statistics is basically a collection of quantitative and qualitative data related to a particular field. Studying the statistical data involves classification, categorisation, analysis, and presentation of the bulk data in a compact manner in either pictorial, graphical, or in any other format desired by the user. The statistical analysis serves to be of great use in various sports such as football, basketball, baseball, cricket, tennis etc. This is because statistics basically helps the players and the coaches improve their decision-making skills by judging and identifying the weak spots and the areas that require the most improvement, thereby enabling them to formulate the most suitable training strategies required to efficiently overcome the shortcomings in the minimum possible time. Some of the other applications of employing statistics in sports include finance and budget analysis, rank prediction, data storage for current and future references, sports betting predictions, revenue generation, ticket price and availability estimation, injury prevention, etc. Also, one can employ statistics in sports to estimate the probability of a team winning or losing a particular tournament. A sportsperson can perform statistical analysis for the purpose of self-improvement by running a comparative analysis with the opponent player and by observing the leads and lags of his/her own performance.

Sports

15. Research and Analysis

Research and analysis departments of almost all fields rely on statistical analysis for their basic operations. The raw data collected by the researchers and analysts is useless until it is fed to an appropriate statistical model to extract the expected or unexpected information. Some of the prominent statistical concepts and theories used in research and analysis include a measure of central tendency, parametric and non-parametric tests, sample size estimation, power analysis, random variables, probability theory, variance, degree of dispersion, descriptive analysis, regression, etc. The outcome or the result of the application of statistical analysis to research data basically depends on the type, nature, and amount of the data compiled for research and the domain of analysis.

Research and Analysis

16. Banking

Banking is a classic example of a field that makes use of statistical analysis for most of its operations. For instance, keeping a track of the interest rates applied to each and every existing bank account, estimating the chances of failure or risk of investment, managing cash and other valuable assets, monitoring the cash flow, implementing new business strategies and policies, improving customer satisfaction ratio, building revenue, predicting transaction outcomes, etc. One of the most important advantages of using statistical analysis in banking is that it ensures security and privacy. Generally, for this purpose, the data related to the banking information of an individual such as the account holder’s name, date of birth, account number, address, nominee, frequent transaction details, date of enrolment, etc. are recorded. The data is then fed to a statistical model or algorithm that tends to locate the most usual pattern in which the owner operates the account. This particular pattern serves to be the basis of analysis and saves the historical transaction details of the account for future use. The dataset containing the information of the bank account and the statistical model keeps on upgrading after every new transaction made by the user.¬† In case an unexpected transaction is made from the user’s account, the statistical model performs the comparative analysis of the current transaction with the historical transactions and alerts the bank about the possibility of a fraudulent transaction or forgery, thereby ensuring safety.

Banking

17. Business Statistics

As the name itself suggests, business statistics make use of data analysis tools, strategies, and techniques of elementary statistics and implement them in various aspects of business such as monitoring the import and export of products and services provided by the firm, formulating and maintaining the employee data, managing financial records, estimating the chances of suffering a loss while making a particular investment, etc. Some of the most common elementary statistics theories and concepts employed by business statistics include the measure of central tendency, dispersion, and association, regression analysis, hypothesis testing, graphical representation of data, probability theory, random variables, multiple regression, time series analysis, different types of probability distributions, parameter estimation, data sampling, correlation, and indexing. Business statistics help in neat documentation and proper maintenance of the records of a particular organisation for legal purposes and for future reference. Also, one can make use of business statistic models to enhance the decision making and tasks prioritization processes, thereby reducing the risk of suffering a loss or a setback.

Business Statistics

18. Data Science

Data science is a cross-disciplinary domain that primarily deals with the study and analysis of raw data to extract meaningful information from it. Usually, the data obtained is available in an unstructured format and contains multiple missing links, error bits, or faulty data points. Such characteristics of raw data make it difficult to process and extract information. To resolve this, a variety of techniques, scientific methodologies, processes, algorithms, and systems can be employed that tend to classify and segregate data into their respective fields. A majority of the techniques applied to structure and analyse raw data are based on statistics, probability, and logic theory. Some of the prominent concepts of statistics used by data science include the measure of central tendency, the relationship between variables, regression, hypothesis testing, etc. The segregated data further can be displayed in a pictorial, graphical, tabular, or any other user-desired format, thereby making the information more clear and understandable.

Data Science

19. Transportation

Transportation is the movement of goods and people from one place to another. Some of the common examples of transportation include cargo, shipping, public transportation, etc. The modes of transportation generally can be classified into five types, namely roadways, waterways, railways, pipelines, and airways. Irrespective of the mode of transportation or the type of entities being transported, all the transportation facilities or transportation providing firms make use of statistical analysis to simplify and improve their basic operation. Statistics in transportation basically help configure the best way possible to allocate funds to different departments of the organisation, keep a track of the arrival time of a particular order at the destination, time taken by the transportation vehicle to make the delivery, fuel consumption of the vehicles, regular maintenance and servicing dates of the vehicles, etc. The data required for the statistical analysis in transportation is generally collected from the global positioning system device placed in the vehicles, the expenditure receipts, verbal information, signed acknowledgement documents from the receiver, etc.

Transportation

20. Artificially Intelligent Devices

Artificial intelligence is basically the ability of a machine or a device to take appropriate decisions with minimum or no human intervention. It tends to form a classic example of the applications in our real life that make use of statistical analysis. A majority of the daily use gadgets that we use in our daily life are based on artificial intelligence. For instance, mobile phones, music applications, shopping websites, speaker systems, watches, automatic vehicles, etc. are embedded with an artificial intelligence program that tends to automate the operation of the device and makes the user interface comparatively simpler. For this purpose, the algorithms on which the operation of the device is based employ certain statistical analysis tools to take the raw data from the surroundings, feed it to the processing unit to classify and categorise the data, pass the neatly arranged and organised data to the statistical models so as to locate a pattern in which the data is present, and finally provide the output in the form of automated decisions. The recommendations for the favourite food we like, the type of clothes we like to wear, or the kind of music we enjoy listening to are all decided by the statistical algorithm that runs in the backend of our devices. The data sets required for such kind of statistical analysis procedures are generally obtained by the average screentime of the user, search history, likes and dislikes on social media accounts, conversations, etc.

Artificially Intelligent Devices

21. Cryptocurrency

A cryptocurrency is a form of digital or virtual currency. It makes use of a decentralized peer to peer system to issue new units of currency to the users and manage the history of all the previous transactions. Currently, there are thousands of cryptocurrencies available in the market. For instance, bitcoin, Ethereum, litecoin, ripple, etc. Cryptocurrency servers usually make use of cryptography techniques to ensure privacy, authentication, and security for each and every transaction that the user makes, hence the name cryptocurrency. A variety of cryptocurrency-related tasks such as the fall or rise of asset rates, the number of crypto-malware, the number of transactions made in a particular amount of time, currency exchanges, percentage of users who have invested in a particular cryptocurrency, the popularity of the cryptocurrencies among users of different regions, etc. make use of deep statistical computing and analysis. The type of data used by the cryptocurrency servers for statistical analysis includes the name of the user, time of initiating and completing the transaction, amount spent or availed, and other related factors.

Cryptocurrency

22. Travel and Tourism

Implementation of statistics in tourism and travel is generally regarded as tourism statistics. Tourism statistics can be defined as the collection and analysis of data related to each and every aspect of tourism and travel. Using statistical analysis in travel and tourism is advantageous as it serves to be a monitoring tool that maintains the record of all the activities taking place at the organisation for current as well as future references. Also, statistics help determine the effectiveness of a new policy before it actually gets implemented in real life. The efficiency and effectiveness of the tourism policies are required to be significantly high as it helps attract a maximum number of tourists to a particular destination, thereby building a strong economy and improving the overall ranking of a nation. Other merits of using statistical analysis in travel and tourism include designing appropriate marketing strategies, running a comparative analysis between different tourist places, evaluating management decisions, estimating the popularity of different destinations, estimating maintenance cost and time, optimizing resource utilization, etc.

Travel and Tourism

23. Information Technology and Computer Science

Computer science is an engineering field that deals with computation, programming, designing, and integration of hardware and software. Similarly, information technology deals with the support, design, and administration of computer systems and other related applications. Data mining serves to be a prominent example of information technology and computer science applications that makes use of statistical tools and methodologies for analysis and information extraction. The process of data mining is basically a four-step process. The first step is data collection, which involves the identification of the authenticated sources of data. The second step is data cleaning, i.e., removing unwanted and noise signals. The third step is to feed the filtered signal to algorithms and perform the necessary data analysis procedures. The final step of data mining is the interpretation of data in the user’s desired format. Here, the ability of statistics to quantify data aids in fulfilling the main aim of the data mining process, i.e., to identify patterns and locate specific structures in data.

Information Technology and Computer Science

24. Robotics

Robotics is the branch of engineering that can be defined as an optimized amalgamation of electronics and mechanical engineering fundamentals, theories, and concepts. Robotics mainly deals with the design, manufacturing, and operation of different robotic gadgets and devices such as robotic arms, robotic vehicles, humanoid robots, etc. Statistical robotics basically employs different statistical models and probability methodologies to store the data collected by the device in a particularly organised manner for current and future references, enhance the decision-making ability of the device, and optimize the working of automatic operations of the system. Also, statistical robotics can be used to keep a track of the number of robots manufactured by an organisation in a particular duration of time, implementation of robots in different sectors of industry, revised maintenance and replacement dates of each robot, etc.

Robotics

25. Machine Learning and Deep Learning

Machine learning is a subset of artificial intelligence that aims at developing systems that are capable of learning, adapting, and making decisions with minimum or no human intervention. In a similar manner, deep learning is a subdomain derived from machine learning that is based on artificial neural networks. Most machine learning and deep learning techniques make use of statistical, probability theory, logic theory, and number theory models and methodologies for their basic operations. For instance, the problem framing, data understanding, data cleaning, data selection, data preparation, model evaluation, compilation, model configuration, model selection, model presentation, and prediction analysis in machine learning applications use statistical techniques for exploratory data analysis, data mining, data summarization, data visualization, hypothesis testing, sampling of data, scaling, encoding, and transforming of data points, outlier detection, error testing, imputation, feature extraction, and data visualisation.

Machine Learning and Deep Learning

26. Aerospace

Aerospace is an engineering domain that primarily deals with the design, manufacturing, development, and testing of aircraft, space crafts, and other flight vehicles. The aerospace industry makes use of in-depth statistical analysis to operate, implement, and optimize decision support systems. This helps increase the reliability of the systems and reduces the risk of system failure. The algorithm used for the statistical analysis of aerospace data also helps identify the chances of hazards in advance, thereby improving the overall performance of the device. Also, statistical analysis in aerospace can be used to keep a track of the machine health and timely conduct maintenance and rupture part replacement sessions.

Aerospace

27. Computing Devices

Computing devices such as microcontrollers, processors, computers, scientific calculators, etc. are yet another example of devices that make use of statistical analysis and algorithms for their basic operations. Such devices tend to store bulk data in the memory units. Accessing a particular piece of information from a bunch of data is a typical task, which is why the internal algorithms of such devices are pre-equipped with appropriate statistical models. Using statistical analysis for this purpose helps organise and maintain the data in an ordered manner that is easy to read and interpret. Also, other operations that involve the execution of arithmetic or logical operations, storage, extraction, display, manipulation, etc. can also be executed with the help of elementary statistics tools and methods.

Computing Devices

28. Automatic Vehicles

Automatic vehicles or self-driving cars tend to form a classic example of applications where the use of statistical tools, methods, and parameters can be observed easily. Self-driving vehicles are generally equipped with three types of operating systems, namely, manual, semi-automatic, and fully automatic. If the vehicle is operated manually, it does not require any sort of data processing and optimization; however, when the vehicle is operated in semi-automatic and a fully automatic mode, a major portion of the vehicle’s operation depends on the processing of the pre-loaded data sets, analysation of the new data points that are gained with the help of various machine learning techniques, and the statistical models embedded within the algorithms used by the artificially intelligent system of the vehicle. The type of data used for the purpose of statistical analysis in automatic vehicles includes coordinate points, images of signboards, object profiles, temperature values, speed ranges, voice signals, etc. In simple words, all the basic operations that take place at the back end of the device and tend to control the movement of the vehicle such as the data collection, recognition, categorization, formulation, enhancement, processing, display, execution, etc. are controlled by embedding the appropriate statistical models and techniques within the device’s algorithms and programs.

Automatic Vehicles

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