The global number of households with a computer in was forecast to continuously increase between 2024 and 2029 by in total 88.6 million households (+8.6 percent). After the fifteenth consecutive increasing year, the computer households is estimated to reach 1.1 billion households and therefore a new peak in 2029. Notably, the number of households with a computer of was continuously increasing over the past years.Computer households are defined as households possessing at least one computer.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of households with a computer in countries like Caribbean and Africa.

This dataset contains 500 synthetic records of technology usage behavior, capturing how different devices are utilized for various purposes across age groups. It aims to provide insights into consumer technology preferences, usage habits, and engagement levels. The data is fully artificial and generated for educational and analytical purposes.

Dataset Features The dataset includes the following columns:

• Date: The date when the usage was recorded, spanning a simulated timeline in 2024.
• Technology Category: The type of device being used (e.g., Smartphones, Laptops, Tablets).
• Usage Purpose: The primary purpose of using the device (e.g., Work, Entertainment, Education, Social Media).
• Brand: The brand of the device being used (e.g., Apple, Samsung, Google, Microsoft).
• Age Group: The age group of the user (e.g., Under 18, 18-24, 25-34, etc.).
• Daily Usage (Hours): The approximate number of hours the device was used per day.

Potential Use Cases 1. Market Analysis: - Understand brand preferences and usage purposes for different devices. - Identify trends in technology adoption by age group. 2. Behavioral Insights: - Explore correlations between daily usage and device categories. - Analyze which purposes dominate technology usage (e.g., work vs. entertainment). 3. Data Visualization Projects: - Create charts or dashboards to visualize technology engagement trends. 4. Machine Learning Models: - Use the data to predict device usage patterns or preferences.

Key Highlights - Includes a diverse range of device types, purposes, and brands. - Simulates realistic daily usage habits across six distinct age groups. - Useful for practicing data cleaning, visualization, and predictive analytics.

Acknowledgments This dataset is fully synthetic and was generated using Python. It does not contain any real-world user data and is intended solely for educational and research purposes.

📊 About the Dataset

Technology adoption has been evolving rapidly, shaping industries and consumer behaviors worldwide. This dataset provides insights into global gadget consumption trends from 2015 to 2025, covering smartphones, laptops, gaming consoles, smartwatches, and 5G penetration rates.

🎯 Use Cases

• 📈 Market research & consumer behavior analysis
• 🔮 Forecasting future tech adoption trends
• ♻️ Studying the impact of e-waste generation
• 🌐 Understanding 5G penetration across different countries

📑 Column Descriptions

129266 Global exporters importers export import shipment records of Used laptops with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

Other-Long-Term-Assets Time Series for Dongguan Aohai Technology Co. Dongguan Aohai Technology Co., Ltd. research, develops, produces, and sells consumer electronics products in China and internationally. The company products operate in smart power charge, power adaptor, GaN black technology, wireless charging, mobile power, smart device, power energy, and network energy. Its products widely used in smartphones, tablets, laptops, smart wearable devices, smart homes, artificial intelligence equipment, power tools, data, independent brands, and other fields. Dongguan Aohai Technology Co., Ltd. was founded in 2004 and is headquartered in Dongguan, China.

Cash-and-Equivalents Time Series for DreamTech Co Ltd. DREAMTECH Co., Ltd. engages in the design, development, and manufacture of modules in South Korea and internationally. It offers various PBA modules for smartphones and wearables; fingerprint sensor modules for biometric, convergence solutions, and medical systems; optical sensors for office appliances; and compact camera modules for IT devices and automotive applications. The company also engages in manufacturing various IT devices and components, such as smartphones, wireless earphones, laptops, etc. Its products are used in smartphones, fingerprint recognition, medical/healthcare devices, and convergence applications. DREAMTECH Co., Ltd. was founded in 1998 and is headquartered in Seongnam-si, South Korea.

Stock Price Time Series for Dongguan Aohai Technology Co. Dongguan Aohai Technology Co., Ltd. research, develops, produces, and sells consumer electronics products in China and internationally. The company products operate in smart power charge, power adaptor, GaN black technology, wireless charging, mobile power, smart device, power energy, and network energy. Its products widely used in smartphones, tablets, laptops, smart wearable devices, smart homes, artificial intelligence equipment, power tools, data, independent brands, and other fields. Dongguan Aohai Technology Co., Ltd. was founded in 2004 and is headquartered in Dongguan, China.

Net-Income-From-Continuing-Operations Time Series for DreamTech Co Ltd. DREAMTECH Co., Ltd. engages in the design, development, and manufacture of modules in South Korea and internationally. It offers various PBA modules for smartphones and wearables; fingerprint sensor modules for biometric, convergence solutions, and medical systems; optical sensors for office appliances; and compact camera modules for IT devices and automotive applications. The company also engages in manufacturing various IT devices and components, such as smartphones, wireless earphones, laptops, etc. Its products are used in smartphones, fingerprint recognition, medical/healthcare devices, and convergence applications. DREAMTECH Co., Ltd. was founded in 1998 and is headquartered in Seongnam-si, South Korea.

Net-Income-Applicable-To-Common-Shares Time Series for DreamTech Co Ltd. DREAMTECH Co., Ltd. engages in the design, development, and manufacture of modules in South Korea and internationally. It offers various PBA modules for smartphones and wearables; fingerprint sensor modules for biometric, convergence solutions, and medical systems; optical sensors for office appliances; and compact camera modules for IT devices and automotive applications. The company also engages in manufacturing various IT devices and components, such as smartphones, wireless earphones, laptops, etc. Its products are used in smartphones, fingerprint recognition, medical/healthcare devices, and convergence applications. DREAMTECH Co., Ltd. was founded in 1998 and is headquartered in Seongnam-si, South Korea.

Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

Cash-and-Short-Term-Investments Time Series for DreamTech Co Ltd. DREAMTECH Co., Ltd. engages in the design, development, and manufacture of modules in South Korea and internationally. It offers various PBA modules for smartphones and wearables; fingerprint sensor modules for biometric, convergence solutions, and medical systems; optical sensors for office appliances; and compact camera modules for IT devices and automotive applications. The company also engages in manufacturing various IT devices and components, such as smartphones, wireless earphones, laptops, etc. Its products are used in smartphones, fingerprint recognition, medical/healthcare devices, and convergence applications. DREAMTECH Co., Ltd. was founded in 1998 and is headquartered in Seongnam-si, South Korea.

Laptop Price Prediction
This project aims to predict the prices of laptops based on their technical specifications, such as processor type, RAM size, storage capacity, brand, and other key features. Using a regression model, the project analyzes the relationship between these specifications and the corresponding laptop prices to provide accurate price predictions.

Dataset

• Rows: 1303 (representing an individual laptop).
• Columns: 11 (features such as Company, Ram, GPU, and more).
• Target Variable: Predict a laptop price.

Key Features of the Project:

• Data Exploration: Comprehensive analysis of the dataset to identify trends and relationships between features.
• Feature Engineering: Handling categorical and numerical variables, encoding, and scaling data.
• Model Development: Implementation of regression algorithms, including Linear Regression and Decision Trees.
• Visualization: Use of plots to present findings and evaluate model performance.

Technologies Used:

• Python, Pandas, Numpy
• Scikit-learn for machine learning models
• Matplotlib and Seaborn for data visualization

This project demonstrates practical skills in data preprocessing, and predictive modeling, making it applicable for pricing predictions in real-world scenarios.

Context

The Dataset includes 1000 Laptop models with their technical specifications scraped from the popular website Laptopmedia.com. The Informations included are very helpful to form a data-driven decision when you are deciding to buy a new laptop.

Dataset Glossary

• laptop_id - Unique identifier for each laptop
• full_name_with_specs - Full name as displayed on the website
• link_profile - Link to the laptop on laptopmedia.com
• full_name - Name of the model
• producer - Producer company of the laptop
• model - Model name
• cpu - Name of the installed processor
• cpu_mark - Benchmark value for the processor
• gpu - Name of the installed graphic unit
• disc - Hard size and types of hard disk
• ram - Size of the installed Rams
• ram_tech - Used Ram technology
• display_size - Size of the installed display
• display_resolution - Pixel count of the installed display
• display_hz - Refresh rate of the display
• display_tech - Installed display technology
• weight - Weight of the laptop
• hdmi_version - Used Hdmi version for the installed interfrace
• wifi_version - Version of the installed wifi chip
• bluetooth_version - Version of the installed bluetooth chip
• webcam_resolution - Resolution of the installed webcam
• microphone_count - Number of installed microphones
• publishing_date - Date on which the model was posted to the website
• price_in_dollar - Price of the laptop on amazon.us
• review_link - Link to a review page for the laptop
• amazon_link - Link to buy the laptop on amazon.us
• dimensions - Dimensions of the laptop
• ethernet - Ethernet technology installed
• fingerprint_reader - Indicates if a fingerprint reader is present
• service_rank - Rank of the customer service provided by the producer
• service_level - Percentage of satisfied customers with customer service

Cover Photo by:freepik

Image: ICT in Education. Class 6th. SCERT Delhi https://www.youtube.com/watch?app=desktop&v=n9sIGl6gBEo

"Students who reported the use of computers in mathematics lessons during the month prior to the PISA test (%)"

"The following findings, based on an analysis of the 2012 Programme for International Student Assessment (PISA) data, tell us that, despite the pervasiveness of information and communication technologies (ICT) in our daily lives, these technologies have not yet been as widely adopted in formal education. But where they are used in the classroom, their impact on student performance is mixed, at best. In fact, PISA results show no appreciable improvements in student achievement in reading, mathematics or science in most countries that had invested heavily in ICT for education."

"The countries with the greatest integration of ICT in schools are Australia, Denmark, the Netherlands and Norway. Rapid increases in the share of students doing schoolwork on computers can often be related to large-scale laptop-acquisition programmes, such as those observed in Australia, Chile, Greece, New Zealand, Sweden and Uruguay."

"On average, seven out of ten students use computers at school – a proportion unchanged since 2009. Among these students, the frequency of computer use increased in most countries during the period. Overall, the relationship between computer use at school and performance is graphically illustrated by a hill shape, which suggests that limited use of computers at school may be better than no use at all, but levels of computer use above the current OECD average are associated with significantly poorer results. On average across TALIS countries, nearly 18% of teachers reported a high level of need to develop their ICT skills for teaching."

https://gpseducation.oecd.org/IndicatorExplorer?query=13&indicators=N050*N052*N055*N053*N054*N051*N056*N057%20

Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Manufacture of Computers and Peripheral Equipment data was reported at 0.000 USD mn in 2023. This stayed constant from the previous number of 0.000 USD mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Manufacture of Computers and Peripheral Equipment data is updated yearly, averaging 0.000 USD mn from Dec 2005 (Median) to 2023, with 16 observations. The data reached an all-time high of 0.000 USD mn in 2023 and a record low of 0.000 USD mn in 2023. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Manufacture of Computers and Peripheral Equipment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 0.022 USD mn in 2023. This records an increase from the previous number of -0.307 USD mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 0.022 USD mn from Dec 2005 (Median) to 2023, with 17 observations. The data reached an all-time high of 1.732 USD mn in 2006 and a record low of -1.181 USD mn in 2013. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

France Foreign Direct Investment Position: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 193.000 EUR mn in 2023. This records a decrease from the previous number of 233.000 EUR mn for 2022. France Foreign Direct Investment Position: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 73.500 EUR mn from Dec 2013 (Median) to 2023, with 10 observations. The data reached an all-time high of 233.000 EUR mn in 2022 and a record low of -37.000 EUR mn in 2018. France Foreign Direct Investment Position: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s France – Table FR.OECD.FDI: Foreign Direct Investment Position: by Industry: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) has never been observed or is very negligible. It would be treated as portfolio investment in theory. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Direct Influence/Indirect Control (DIIC) method. Debt between fellow enterprises are completely covered. Collective investment institutions not covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the resident direct investor. Statistical unit: Enterprise.

Czech Republic CZ: Foreign Direct Investment Income: Inward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 325.766 CZK mn in 2023. This records an increase from the previous number of -3,482.382 CZK mn for 2022. Czech Republic CZ: Foreign Direct Investment Income: Inward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 92.876 CZK mn from Dec 2013 (Median) to 2023, with 9 observations. The data reached an all-time high of 472.688 CZK mn in 2018 and a record low of -3,482.382 CZK mn in 2022. Czech Republic CZ: Foreign Direct Investment Income: Inward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 1.775 USD mn in 2023. This records an increase from the previous number of 1.756 USD mn for 2022. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 0.102 USD mn from Dec 2013 (Median) to 2023, with 10 observations. The data reached an all-time high of 2.949 USD mn in 2020 and a record low of -10.106 USD mn in 2013. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Position: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.