Nature of data je jit - practice
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Scaled data provides exact numeric values, ranked data provides comparative values to show ordering or ranking, and nominal data categorizes items without numeric values. The document discusses these three types of data and provides examples of each. It then presents sample questions to determine whether the data referenced would be considered scaled, ranked, or nominal.
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Nature of the data
The document discusses different types of data:
- Scaled data provides exact amounts like distances and speeds.
- Ordinal or ranked data provides comparative amounts by ranking items.
- Nominal data names or categorizes items without numerical values.
- Nominal proportional data expresses categories as simple percentages.
The document uses examples to illustrate each type and provides practice questions for determining the type of outcome data in different scenarios.
Nature of the data practice
The document discusses different types of data:
- Scaled data provides exact amounts like miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are percentages like 45% Republican, 55% Democrat.
The key is determining what type of data is being influenced or is the "outcome" in questions being analyzed.
Nature of the data practice - Copyright updated
The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
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According to research of giving patterns since 1966 compiled by the Giving USA Foundation, only bequests averaged an increase during recessionary periods. All
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But is your planned giving program getting its fair share of the marketing budget?
We scoured the internet to find the following awesome information. And, although we can’t take credit for the research, we can take credit for the pretty charts!
So feel free to share this report with colleagues and friends (including your board). And when you’re ready to engage in serious marketing to find hidden gifts, generate
leads and cultivate relationships, we hope you’ll reach out to us: http://imarketsmart.com/contact-us
After all, our mission is to help further your mission.
ENJOY THE REPORT!
Data Warehouse Presentation
A data warehouse is a database organized to allow fast queries of information from different systems. It contains numerical "facts" like meals provided or dollars spent, stored at a specific level of detail called the "grain." Dimensions like ethnicity, language, or location are used to group and describe facts in queries. Building a data warehouse involves extracting data from source systems, transforming it by standardizing codes and values, and loading it into the warehouse where it can be analyzed to answer business questions.
Segmenting Major Donors Versus Planned Givers
I offered this talk in October 2009 at the International Catholic Stewardship Conference in Dallas, Texas
Nature of the data (spread)
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances, temperatures, and speeds. Ordinal data provides comparative amounts by ranking items from first to last place without exact measures between ranks. Examples provided determine whether data about driving speeds, state well-being rankings, giraffe heights, and baby weight percentiles are scaled or ordinal.
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Nature of the data
The document discusses different types of data:
- Scaled data provides exact amounts like distances and speeds.
- Ordinal or ranked data provides comparative amounts by ranking items.
- Nominal data names or categorizes items without numerical values.
- Nominal proportional data expresses categories as simple percentages.
The document uses examples to illustrate each type and provides practice questions for determining the type of outcome data in different scenarios.
Nature of the data practice
The document discusses different types of data:
- Scaled data provides exact amounts like miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are percentages like 45% Republican, 55% Democrat.
The key is determining what type of data is being influenced or is the "outcome" in questions being analyzed.
Nature of the data practice - Copyright updated
The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
The Good Spread - velir presentation - Simplifying the Process of Charitable ...
The document discusses The Good Spread, a proposed website that aims to simplify charitable giving. It seeks to address challenges with current charity evaluation/search sites by developing a scoring system for charities, providing personalized recommendations, and creating a positive user experience. The website would score charities based on quantitative metrics and qualitative factors, combining information from various rating organizations. It would also feature interactive tools for donors to explore issues and charities. The goals are to make it easy for donors to choose trustworthy charities and avoid "analysis paralysis." Key challenges involve developing the scoring method, generating tailored recommendations, designing an intuitive user interface, and determining the business and legal structure.
MarketSmart 26 Really Interesting Planned Giving Marketing Stats
According to research of giving patterns since 1966 compiled by the Giving USA Foundation, only bequests averaged an increase during recessionary periods. All
other types of giving either remained static or reported declines.
But is your planned giving program getting its fair share of the marketing budget?
We scoured the internet to find the following awesome information. And, although we can’t take credit for the research, we can take credit for the pretty charts!
So feel free to share this report with colleagues and friends (including your board). And when you’re ready to engage in serious marketing to find hidden gifts, generate
leads and cultivate relationships, we hope you’ll reach out to us: http://imarketsmart.com/contact-us
After all, our mission is to help further your mission.
ENJOY THE REPORT!
Data Warehouse Presentation
A data warehouse is a database organized to allow fast queries of information from different systems. It contains numerical "facts" like meals provided or dollars spent, stored at a specific level of detail called the "grain." Dimensions like ethnicity, language, or location are used to group and describe facts in queries. Building a data warehouse involves extracting data from source systems, transforming it by standardizing codes and values, and loading it into the warehouse where it can be analyzed to answer business questions.
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Nature of the data (spread)
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances, temperatures, and speeds. Ordinal data provides comparative amounts by ranking items from first to last place without exact measures between ranks. Examples provided determine whether data about driving speeds, state well-being rankings, giraffe heights, and baby weight percentiles are scaled or ordinal.
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3) Cultivation of major donors is an ongoing process of engagement to build understanding and commitment over time before making an ask.
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The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
- Relationship questions examine if a change in one variable is associated with a change in another variable.
- Independence questions determine if two variables with multiple levels are independent of each other.
- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
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- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
- The new car data set skewness value of -1.69 divided by the standard error is between -2 and 2, so this distribution is normal
Normal or skewed distributions (descriptive both2) - Copyright updated
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
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The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
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The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updated
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
Dichotomous or scaled
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
Skewed less than 30 (ties)
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Skewed sample size less than 30
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Ordinal (ties)
The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
Ordinal and nominal
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
Relationship covariates
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3) Cultivation of major donors is an ongoing process of engagement to build understanding and commitment over time before making an ask.
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2) The research revealed that people initially avoid thinking about their mortality but may be inspired to consider bequests if their sense of "autobiographical heroism" is engaged. Messaging should focus on symbolic immortality and how donors can be remembered.
3) The document provides recommendations for non-profits to close the "legacy giving gap", including building awareness of planned giving, generating leads, educating donors, cultivating relationships, and asking for legacy gifts.
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The document discusses how location-based social services can drive retail sales through increased relevance and engagement. It provides examples of partnerships between Collective Bias, Whrrl, and brands like Marie Callender's and Murphy USA to create geo-targeted online communities that encourage in-store activations and recommendations. Evaluation of the Marie Callender's Family Meals Society showed a significant increase in social media activity for the brand compared to competitors.
Explain the procedures your department or a department near you woul
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1) The local law enforcement used to be involved in any loss but now the department has investigators and inspectors that respond to any suspicious fires or those with high dollar losses or deaths.
2) The investigators and inspectors will call in the State Fire Marshal to assist with the investigation for deaths or high dollar losses.
3) The State Fire Marshal is a law enforcement officer who can make arrests, issue citations, and help arrest suspects, but having law enforcement certified investigators at the county level would also help with arrests.
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The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
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The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to generalize to the larger population. Examples are provided to illustrate when each type of statistic would be used. Key differences include descriptive statistics examining entire populations while inferential statistics examine samples that aim to infer conclusions about populations.
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The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
- Relationship questions examine if a change in one variable is associated with a change in another variable.
- Independence questions determine if two variables with multiple levels are independent of each other.
- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
Normal or skewed distributions (inferential) - Copyright updated
- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
- The new car data set skewness value of -1.69 divided by the standard error is between -2 and 2, so this distribution is normal
Normal or skewed distributions (descriptive both2) - Copyright updated
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Nature of the data (spread) - Copyright updated
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
Mode practice 1 - Copyright updated
The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updated
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
Dichotomous or scaled
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
Skewed less than 30 (ties)
The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
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The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
Ordinal (ties)
The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
Ordinal and nominal
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
Relationship covariates
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
Relationship nature of data
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
Number of variables (predictive)
The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
Levels of the iv
The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
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The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
Dependent variable je jit - practice
This document discusses dependent and explanatory variables in research questions. It provides examples of questions with one and two dependent variables. A dependent variable is the thing being influenced or measured in a research study. An explanatory variable is what does the influencing. Good research questions will have one or more clearly defined dependent variables and one or more explanatory variables. The document uses examples like shifts' influence on employee satisfaction and training methods' influence on proficiency to illustrate dependent and explanatory variables.
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3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Income Tax exemption for Start up : Section 80 IAC
A presentation on the concept of Exemption of Profits of Start ups from Income Tax
Best Forex Brokers Comparison in INDIA 2024
Navigating the world of forex trading can be challenging, especially for beginners. To help you make an informed decision, we have comprehensively compared the best forex brokers in India for 2024. This article, reviewed by Top Forex Brokers Review, will cover featured award winners, the best forex brokers, featured offers, the best copy trading platforms, the best forex brokers for beginners, the best MetaTrader brokers, and recently updated reviews. We will focus on FP Markets, Black Bull, EightCap, IC Markets, and Octa.
The Evolution and Impact of OTT Platforms: A Deep Dive into the Future of Ent...
This presentation provides a thorough examination of Over-the-Top (OTT) platforms, focusing on their development and substantial influence on the entertainment industry, with a particular emphasis on the Indian market.We begin with an introduction to OTT platforms, defining them as streaming services that deliver content directly over the internet, bypassing traditional broadcast channels. These platforms offer a variety of content, including movies, TV shows, and original productions, allowing users to access content on-demand across multiple devices.The historical context covers the early days of streaming, starting with Netflix's inception in 1997 as a DVD rental service and its transition to streaming in 2007. The presentation also highlights India's television journey, from the launch of Doordarshan in 1959 to the introduction of Direct-to-Home (DTH) satellite television in 2000, which expanded viewing choices and set the stage for the rise of OTT platforms like Big Flix, Ditto TV, Sony LIV, Hotstar, and Netflix. The business models of OTT platforms are explored in detail. Subscription Video on Demand (SVOD) models, exemplified by Netflix and Amazon Prime Video, offer unlimited content access for a monthly fee. Transactional Video on Demand (TVOD) models, like iTunes and Sky Box Office, allow users to pay for individual pieces of content. Advertising-Based Video on Demand (AVOD) models, such as YouTube and Facebook Watch, provide free content supported by advertisements. Hybrid models combine elements of SVOD and AVOD, offering flexibility to cater to diverse audience preferences.
Content acquisition strategies are also discussed, highlighting the dual approach of purchasing broadcasting rights for existing films and TV shows and investing in original content production. This section underscores the importance of a robust content library in attracting and retaining subscribers.The presentation addresses the challenges faced by OTT platforms, including the unpredictability of content acquisition and audience preferences. It emphasizes the difficulty of balancing content investment with returns in a competitive market, the high costs associated with marketing, and the need for continuous innovation and adaptation to stay relevant.
The impact of OTT platforms on the Bollywood film industry is significant. The competition for viewers has led to a decrease in cinema ticket sales, affecting the revenue of Bollywood films that traditionally rely on theatrical releases. Additionally, OTT platforms now pay less for film rights due to the uncertain success of films in cinemas.
Looking ahead, the future of OTT in India appears promising. The market is expected to grow by 20% annually, reaching a value of ₹1200 billion by the end of the decade. The increasing availability of affordable smartphones and internet access will drive this growth, making OTT platforms a primary source of entertainment for many viewers.
Authentically Social by Corey Perlman - EO Puerto Rico
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Social media for business LA HUG - Video Testimonials with Chynna Morgan - June 2024
Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
We will dig deeper into:
1. How to capture video testimonials that convert from your audience 🎥
2. How to leverage your testimonials to boost your sales 💲
3. How you can capture more CRM data to understand your audience better through video testimonials. 📊
Understanding User Needs and Satisfying Them
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
Part 2 Deep Dive: Navigating the 2024 Slowdown
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
Best practices for project execution and delivery
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
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Nature of data je jit - practice
- 1. Scaled data is data that gives you exact amounts. Central Tendency, Spread, or Symmetry? 80 Miles per hour 12.5 feet 60 degrees Ranked data is data that gives you comparative amounts. 1st Place 2nd Place 3rd Place Nominal data is categorical. Republican or DemocratFor or Against Mexican, Canadian, or American
- 3. Determine the type of data in each question that follows:
- 4. Scaled Ranked Which product has the highest profit margin in terms of $$? Nominal
- 5. Scaled Ranked Which product has the highest profit margin in terms of $$? Nominal
- 6. Scaled Ranked Which product has the highest profit margin in terms of $$? Nominal
- 7. Scaled Ranked Which product has the highest profit margin in terms of $$? Nominal Scaled data is data that gives you exact amounts. 80 Miles per hour 12.5 feet 60 degrees
- 9. Scaled Ranked Does where you grow up influence your religious affiliation? Nominal
- 10. Scaled Ranked Does where you grow up influence your religious affiliation? Nominal
- 11. Scaled Ranked Does where you grow up influence your religious affiliation? Nominal
- 12. Scaled Ranked Does where you grow up influence your religious affiliation? Nominal Nominal data is categorical. Republican or DemocratFor or Against Mexican, Canadian, or American
- 13. Final Question
- 14. Scaled Ranked Which school places higher in terms of overall ACT scores? Nominal
- 15. Scaled Ranked Which school places higher in terms of overall ACT scores? Nominal
- 16. Scaled Ranked Which school places higher in terms of overall ACT scores? Nominal
- 17. Scaled Ranked Which school places higher in terms of overall ACT scores? Nominal Ranked data is data that gives you comparative amounts. 1st Place 2nd Place 3rd Place
Editor's Notes
- Does where you grow up influence your religious affiliation?