Descriptive statistics for the basic performance measures.

Descriptive statistics for test performance measures in groups with low versus high levels of SCC.

Descriptive statistics of participant performance across all measures.

Descriptive statistics of response styles measures and the overall mean, median, reliability measures alpha and omega and skewness of all response styles.

Descriptive Statistics and Correlations Between the Predictors and Academic Performance Measures.

Goalkeeper and Midfielder Statistics

Leveraging Statistical Data Of Goalkeepers and Midfielders

By [source]

About this dataset

Welcome to Kaggle's dataset, where we provide rich and detailed insights into professional football players. Analyze player performance and team data with over 125 different metrics covering everything from goal involvement to tackles won, errors made and clean sheets kept. With the high levels of granularity included in our analysis, you can identify which players are underperforming or stand out from their peers for areas such as defense, shot stopping and key passes. Discover current trends in the game or uncover players' hidden value with this comprehensive dataset - a must-have resource for any aspiring football analyst!

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How to use the dataset

• Define Performance: The first step of using this dataset is defining what type of performance you are measuring. Are you looking at total goals scored? Assists made? Shots on target? This will allow you to choose which metrics from the dataset best fit your criteria.

• Descriptive Analysis: Once you have chosen your metric(s), it's time for descriptive analysis. This means analyzing the patterns within the data that contribute towards that metric(s). Does one team have more potential assist makers than another? What about shot accuracy or tackles won %? With descriptive analysis, we'll look for general trends across teams or specific players that influence performance in a meaningful way.

• Predictive Analysis: Finally, we can move onto predictive analysis. This type of analysis seeks to answer two questions: what are factors that predict player performance? And which factors are most important when predicting performance? Utilizing various predictive models—ex – Logistic regression or Random forest -we can determine which variables in our dataset best explain a certain metric’s outcome—for example –expected goals per match -and build models that accurately predict future outcomes based on given input values associated with those factors.

By following these steps outlined here, you'll be able to get started in finding relationships between different metrics from this dataset and leveraging these insights into predictions about player performance!

Research Ideas

• Creating an advanced predictive analytics model: By using the data in this dataset, it would be possible to create an advanced predictive analytics model that can analyze player performance and provide more accurate insights on which players are likely to have the most impact during a given season.
• Using Machine Learning algorithms to identify potential transfer targets: By using a variety of metrics included in this dataset, such as shots, shots on target and goals scored, it would be possible to use Machine Learning algorithms to identify potential transfer targets for a team.
• Analyzing positional differences between players: This dataset contains information about each player's position as well as their performance metrics across various aspects of the game (e.g., crosses attempted, defensive clearances). Thus it could be used for analyzing how certain positional groupings perform differently from one another in certain aspects of their play over different stretches of time or within one season or matchday in particular.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: DEF PerApp 2GWs.csv | Column name | Description | |:----------------------------|:------------------------------------------------------------| | Name | Name of the player. (String) | | App. | Number of appearances. (Integer) | | Minutes | Number of minutes played. (Integer) | | Shots | Number of shots taken. (Integer) | | Shots on Target | Number of shots on target. (Integer) ...

Notes. BIS-11: Barratt Impulsiveness Scale; BIS: Behavioural Inhibition System; BAS: Behavioural Approach Systems; SSS-V: Sensation Seeking Scale-v; IGT: Iowa Gambling Task; IGT-v: variant of the Iowa Gambling Task; Net score is computed as difference between advantageous and disadvantageous deck selections; Total Sample: N = 165, Males: N = 80, Females: N = 85; IGT: N = 84, Males = 40, Females: N = 44; IGT-v: N = 81, Males = 40, Females: N = 41; *p

Drug Performance Evaluation

Assessing Quality, Cost, and Effectiveness

By [source]

About this dataset

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How to use the dataset

This dataset contains drug performance metrics for 37 common conditions, so it can be used to compare and analyze the effectiveness, cost, and satisfaction of different drugs.

To get started using this dataset, let’s take a look at the columns. The key fields to consider are Condition, Drug Name, Indication, Type/Form of Drug (such as pill), Reviews/Ratings (for efficacy), Ease-of-Use Ratings (rated by customers), Satisfaction Ratings (also rated by customers) and Average Price. Additionally you should consider the Information field which includes any additional information related to the drug.

Now that we have identified all the important figures we can begin analyzing our data set. A great first step is to make use of descriptive statistics such as mean or median values for each column in order to get an idea on how various drugs perform with regards to effectiveness or cost etcetera. We also might want group our data indexed based on condition – that way comparing drugs would be simpler based on what they treat! Additionally grouping by type and form can also help provide meaningful insights into our data set allowing us to gain greater understanding about various drug responses across different forms/types available for different conditions (e.g capsules vs pills versus injections). Comparing performance metrics amongst groups created this way will enable us draw correlations between cost effectiveness ratios and satisfaction rates towards certain types/forms of drugs compared with others treating similar diseases or health concerns!

Finally “Reviews” are a key component any analysis involving drug performance ratings – while they do not necessarily correspond directly towards efficacy; they play a vital measuring stick when it comes consumer perceptions regarding certain medications – both good and bad alike! Overall understanding how these reviews & ratings interact with all other factors provides us great insight into medication reception thereby aiding potential health news coverage as well making informed decisions related medical purchases & usage across consumer level demographics!

Research Ideas

• Developing smart algorithms to recommend prescription drugs based on a patient's medical condition and other factors.
• Comparing the overall performance of different types of drugs across various conditions.
• Analyzing price points of drugs to improve access to affordable treatments for those with lower incomes it in need of health care services

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Drug.csv | Column name | Description | |:-----------------|:------------------------------------------------------------------------| | Condition | The medical condition associated with the drug. (String) | | Drug | The name of the drug. (String) | | Indication | The purpose of the drug. (String) | | Type | The type of drug (generic or brand). (String) | | Reviews | The number of reviews associated with the drug. (Integer) | | Effective | The effectiveness of the drug based on customer reviews. (Integer) | | EaseOfUse | The ease of use of the drug based on customer reviews. (Integer) | | Satisfaction | The satisfaction level of the drug based on customer reviews. (Integer) | | Information | Other relevant information about the drug. (String) |

File: Drug_clean.csv | Column name | Description | |:-----------------|:------------------------------------------------------------------------| | Condition | The medical condition associated with the drug. (String) | | Drug ...

Despite experiencing a dip during the COVID-19 pandemic, football remains a robust multi-billion-euro industry. The accurate prediction of team values holds immense significance for teams, investors, and other stakeholders. In this research, we delve into the determinants of German Bundesliga team values, encompassing performance-based metrics, macroeconomic indicators, and demographic statistics. Leveraging machine learning and dynamical linear methods, we construct a comprehensive model for Bundesliga team values. Our findings not only illuminate team performance on and off the field but also offer vital insights to guide decisions and navigate the complexities of the fiercely competitive football industry.

General Information - CMS Certification Number (CCN): Unique identifier for the dialysis facility assigned by the Centers for Medicare & Medicaid Services (CMS). - Network: The network or region to which the facility belongs. - Facility Name: Name of the dialysis facility. - Five Star Date: Date range for the facility's star rating assessment. - Five Star: Star rating for the facility's overall quality (scale: 1–5). - Five Star Data Availability Code: Indicates if the five-star data is available.

Location Information - Address Line 1 / Address Line 2: Physical address of the facility. - City/Town: City where the facility is located. - State: U.S. state abbreviation. - ZIP Code: Postal code. - County/Parish: County or parish where the facility resides. - Telephone Number: Facility's contact number.

Facility Ownership and Operations - Profit or Non-Profit: Indicates the ownership type (profit or nonprofit). - Chain Owned: Whether the facility is part of a chain. - Chain Organization: Name of the parent chain, if applicable. - Late Shift: Indicates whether the facility offers late dialysis shifts. - # of Dialysis Stations: Number of available dialysis stations.

Treatment Types - Offers in-center hemodialysis: Whether the facility provides in-center hemodialysis. - Offers peritoneal dialysis: Availability of peritoneal dialysis. - Offers home hemodialysis training: Availability of home hemodialysis training.

Certification and Claims Data - Certification Date: Date when the facility was certified. - Claims Date: Date range of claims data used for assessment. - EQRS Date: Data reporting date for the End Stage Renal Disease Quality Reporting System (EQRS).

Mortality, Hospitalization, and Readmission - SMR Date: Date for Standardized Mortality Ratio (SMR) reporting. - Patient Survival Category Text: Survival category (e.g., "As Expected," "Better than Expected"). - Patient Survival Data Availability Code: Indicates survival data availability. - Number of Patients Included in Survival Summary: Patients included in mortality analysis. - Mortality Rate (Facility): Mortality rate at the facility. - Mortality Rate: Upper/Lower Confidence Limit: Statistical confidence intervals for the mortality rate. - SHR Date: Date for Standardized Hospitalization Ratio (SHR) reporting. - Patient Hospitalization Category Text: Facility’s performance in hospitalizations (e.g., "Worse than Expected"). - Patient Hospitalization Data Availability Code: Indicates hospitalization data availability. - Number of Patients Included in Hospitalization Summary: Patients in hospitalization analysis. - Hospitalization Rate (Facility): Rate of hospitalizations for the facility. - SRR Date: Standardized Readmission Ratio (SRR) reporting date. - Patient Hospital Readmission Category: Performance in readmissions. - Readmission Rate (Facility): Rate of hospital readmissions within 30 days.

Transfusion and Transplant Metrics - STrR Date: Standardized Transfusion Ratio reporting date. - Patient Transfusion Category Text: Facility’s performance in blood transfusion metrics. - Transfusion Rate (Facility): Transfusion rates at the facility. - SWR Date: Standardized Waitlist Ratio reporting date. - Standardized First Kidney Transplant Waitlist Ratio: Ratio of patients on the transplant waitlist compared to expected values. - Percentage of Prevalent Patients Waitlisted: Percentage of patients on a transplant waitlist.

Emergency Department and Infection Metrics - SEDR Date: Emergency Department Encounter reporting date. - Standardized ED Visits Ratio (Facility): ED visits relative to expected values. - ED30 Date: Emergency Department visits within 30 days of hospitalization reporting date. - Standard Infection Ratio (SIR): Infection rates compared to expected values.

Clinical Metrics - Fistula Rate (Facility): Percentage of patients with arteriovenous fistula for dialysis access. - HGB < 10 g/dL / HGB > 12 g/dL: Percentage of patients with specific hemoglobin levels. - Hypercalcemia: Percentage of patients with calcium levels above 10.2 mg/dL. - Serum Phosphorus Levels: Percentage of patients in specific serum phosphorus ranges. - Kt/V Data (HD/PD): Adequacy of dialysis as measured by Kt/V for hemodialysis (HD) and peritoneal dialysis (PD).

Staff and Patient Metrics - Healthcare Worker COVID-19 Vaccination Adherence: Percentage of healthcare workers vaccinated against COVID-19. - Long-Term Catheter Usage: Percentage of patients with long-term catheter usage.

Additional Metrics - SMoSR Date: Standardized Modality Switch Ratio reporting date. - Number of Patients in Modality Summary: Patients analyzed for switching dialysis modalities. - nPCR Data: Nutritional metrics related to dialysis adequacy for pediatric patients.

Descriptive statistics of the dependent variables in each training group and moment measure (students).

Descriptive statistics of participants’ performance of the convergent visual imagination task at pre-measure, half-way measure and post-measure after checking familiarity.

This summary dataset provides the underlying data for the Agency Performance – Child Welfare Dashboard and the Agency Performance – Child Welfare Quality Assurance Dashboards. Data are pre-aggregated and reported at the level of fiscal year and region. The following metrics are also summarized by race/ethnicity on the Agency Performance Child Welfare Dashboard: - Median Length of Stay - Re-Entry The following metrics are summarized by race/ethnicity on the Agency Performance Quality Assurance Dashboard: - Permanency in 12 Months - Kin Placement Rate The following metrics are also summarized by age on the Agency Performance Child Welfare Dashboard: - Out-of-Home Exits - Out-of-Home Entries - Out-of-Home: Single Point in Time CW Agency Performance: Agency Performance CW Dashboard Final_v1 | Tableau Public CW Supplemental Quality Assurance: Supplemental Agency Performance CW Dashboard Final_v1 | Tableau Public

Response variable: overall_score

Scenario It is a difficult task to find an overall measure of quality for higher-education institutions, as there are many areas of work most universities worldwide involve themselves in, such as teaching, research, and knowledge exchange. Nevertheless, to decide on a way to predict the overall quality of an institution would be desirable for those who want to make an informed decision of whether to engage with a specific university, as a student, research collaborator or as industry partner.

This dataset contains records of many universities, offering data on performance metrics, student data and descriptive data. The goal is to forecast the value for the variable overall_score, by employing models suitable for regression problems. Find out what characteristics have the most influence on the general quality of institutions of higher learning.

Columns Description name: Name of the university

scores_teaching: The teaching quality of the university, scored out of 100

scores_research: The research quality of the university, scored out of 100

scores_citations: Citation volume and of academics based at the university, scored out of 100

scores_international_outlok: The university’s level of engagement with international partners, scored out of 100 record_type: Category of the record

member_level: Level of membership

location: Country where the university is located

stats_number_students: The number of students enrolled at the university

stats_student_staff_ratio: Number of staff members per student stats_pc_intl_students: The percentage of enrolled students that are classed as international

stats_female_male_ratio: The ration of female students versus male students

subjects_offered: The range of subject areas that are taught at the university

closed: Whether the university is currently closed to new applicants

unaccredited: Whether the university is currently unaccredited

overall_score: The overall quality of the university, scored out of 100

This page provides data for the 3rd Grade Reading Level Proficiency performance measure.The dataset includes the student performance results on the English/Language Arts section of the AzMERIT from the Fall 2017 and Spring 2018. Data is representive of students in third grade in public elementary schools in Tempe. This includes schools from both Tempe Elementary and Kyrene districts. Results are by school and provide the total number of students tested, total percentage passing and percentage of students scoring at each of the four levels of proficiency. The performance measure dashboard is available at 3.07 3rd Grade Reading Level Proficiency.Additional InformationSource: Arizona Department of EducationContact: Ann Lynn DiDomenicoContact E-Mail: Ann_DiDomenico@tempe.govData Source Type: Excel/ CSVPreparation Method: Filters on original dataset: within "Schools" Tab School District [select Tempe School District and Kyrene School District]; School Name [deselect Kyrene SD not in Tempe city limits]; Content Area [select English Language Arts]; Test Level [select Grade 3]; Subgroup/Ethnicity [select All Students] Remove irrelevant fields; Add Fiscal YearPublish Frequency: Annually as data becomes availablePublish Method: ManualData Dictionary

The impact of a chief executive officer’s (CEO’s) functional experience on firm performance has gained the attention of many scholars. However, the measurement of functional experience is rarely disclosed in the public database. Few studies have been conducted on the comprehensive functional experience of CEOs. This paper used the upper echelons theory and obtained deep-level curricula vitae (CVs) data through the named entity recognition technique. First, we mined 15 consecutive years of CEOs’ CVs from 2006 to 2020 from Chinese listed companies. Second, we extracted information throughout their careers and automatically classified their functional hierarchy. Finally, we constructed breadth (functional breadth: functional experience richness) and depth (functional depth: average tenure and the hierarchy of function) for empirical analysis. We found that a CEO’s breadth is significantly negatively related to firm performance, and the quadratic term is significantly positive. A CEO’s depth is significantly positively related to firm performance, and the quadratic term is significantly negative. The research results indicate a u-shaped relationship between a CEO’s breadth and firm performance and an inverted u-shaped relationship between their depth and firm performance. The study’s findings extend the literature on factors influencing firm performance and CEOs’ functional experience. The study expands from the horizontal macro to the vertical micro level, providing new evidence to support the recruitment and selection of high-level corporate talent.

This dataset comes from the Biennial City of Tempe Employee Survey questions related to employee engagement. Survey respondents are asked to rate their level of agreement on a scale of 5 to 1, where 5 means "Strongly Agree" and 1 means "Strongly Disagree". This dataset includes responses to the following statements: I have received fair consideration for advancement & promotion, when available, within the City of TempeI have been mentored at workThe City's programs related to professional development & career mobility, such as educational partnerships, Tempe Professional Development Network, etc., are useful to meThe following adequately supports my work-related needs: City Manager's OfficeThe following adequately supports my work-related needs: Strategic Management & Diversity OfficeI believe my opinions seem to countConflict in my work area is resolved effectivelyI believe exceptional job performance is recognized appropriately by managers/supervisors in my work unitThe amount that I pay for health care benefits is reasonableI think the amount I am paid is adequate for the work I doCommunication between my work unit/pision & work units/pisions OUTSIDE my department is goodEmployees in my department take personal accountability for their actions and work performance (starting in the 2018 survey) Participation in the survey is voluntary and confidential. This page provides data for the Employee Engagement performance measure. The performance measure dashboard is available at 2.13 Employee Engagement. Additional InformationSource: paper and digital survey submissionsContact: Aaron PetersonContact E-Mail: Aaron_Peterson@tempe.govData Source Type: ExcelPreparation Method: NAPublish Frequency: biennialPublish Method: ManualData Dictionary

Comprehensive YouTube channel statistics for Summary, featuring 2,450,000 subscribers and 1,349,228,735 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the News-&-Politics category and is based in AE. Track 3,779 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Summer Products Sales Performance

E-commerce sales performance and ratings data for summer products

By Jeffrey Mvutu Mabilama [source]

About this dataset

The Summer Products and Sales Performance dataset is a comprehensive collection of product listings, ratings, and sales data from the Wish platform. The dataset aims to provide insights into the trends and patterns in e-commerce during the summer season. It contains valuable information such as product titles, prices, retail prices, currency used for pricing, units sold, whether ad boosts are used for product listings, average ratings for products, total ratings count for products, counts of five-star to one-star ratings for products.

Additionally, the dataset includes data on various aspects related to product quality and shipping options such as badges count (indicating special qualities), local product status (whether the product is sold locally), product quality rating badges (indicating the quality of the product), fast shipping availability badges (indicating whether fast shipping is available), tags associated with products (making them more discoverable), color variations of products available in inventory along with their count. It also provides information on different shipping options including option names and their corresponding prices.

Moreover,the dataset encompasses details about merchants selling these products including merchant title and name as well as information on merchant rating count (total number of ratings received by merchants) ,merchant profile picture availability,and subtitle which gives additional details about merchant's info.

The dataset further includes links to images of individual listed products along with links to respective online shop pages where these are found . In addition,currency buyer specifies currency type used by buyers throughout various transactions.Items flagged under urgency text have an associated urgency text rate indicating how urgently they are desired or needed.

This comprehensive dataset also allows users to analyze units sold per listed item as well as mean units sold per listed item across different categories/theme .Further evaluation can be done using totalunitsold variable which represents total volume sales from all listed items tied together across Wish platform.

Aiding further analysis around elasticity theory users can find marked down rates/percentage tagged describing discounts over retail price,ranging from 0-1 as well as average discount values for individual listed products.Further custom insights such as number of countries items can be delivered to, their origin country, if they possess an urgency banner or fast shipping and if the seller is famous/has a profile picture.

This comprehensive dataset served to build model helping sellers predict how well an item may sell so as to equip businesses with ability to make replenishment decisions guided by this model

How to use the dataset

• Familiarize Yourself with the Columns:

  • Before diving into data analysis, it's important to understand the meaning of each column in the dataset. The columns contain information such as product titles, prices, ratings, inventory details, shipping options, merchant information, and more. Refer to the dataset documentation or use descriptive statistics methods to gain insights into different attributes.

• Explore Product Categories:

  • The dataset includes a column named theme that represents the category or theme of each product listing. By analyzing this column's values and frequency distribution, you can identify top-selling categories during the summer season. This information can be beneficial for businesses looking to optimize their product offerings.

• Analyze Pricing Data:

  • The columns like price, retail_price, and currency_buyer provide insights into pricing strategies employed by sellers on Wish platform.
  • Calculate various statistical measures like mean price using 'meanproductprices', highest priced items using 'price', average discount using averagediscount'
  • Investigate relationships between pricing factors such as discounted prices compared to original retail prices ('discounted price' = 'retail_price' - 'price').

• Examine Ratings Data: 4a) Analyze Product Ratings: To gauge customer satisfaction levels regarding products listed on Wish platform products rating features have been provided. Available columns- -> Number of ratings received per star rating -> Total number of ratings received (rating_count) -> Average rating (rating) Perform analysis to find: - Aver...

Descriptive analysis of players' performance measures according to the number of baskets.