10 Quantitative Skills and How to Develop Them

quantitative skills

  • Updated December 25, 2023
  • Published August 8, 2023

Are you looking to learn more about Quantitative skills? In this article, we discuss Quantitative skills in more detail and give you tips about how you can develop and improve them.

What are Quantitative skills?

Quantitative skills refer to the ability to work with numerical data, perform mathematical calculations, and analyze information using quantitative methods. These skills are crucial in various fields, including but not limited to science, engineering, finance, economics, data science, and social sciences. Here are some key aspects of quantitative skills:


Data analysis.

  • Critical Thinking

Modeling and Simulation

Problem solving, computer programming, financial analysis, economics and econometrics, research and surveys, data visualization.

Developing and honing quantitative skills can greatly enhance your problem-solving abilities and increase marketability across various industries and academic disciplines.

Top 10 Quantitative Skills

Below we discuss the top 10 Quantitative skills. Each skill is discussed in more detail, and we will also give you tips on improving them.

Mathematics is a fundamental quantitative skill that forms the bedrock of various disciplines and problem-solving processes. It encompasses various mathematical concepts, including arithmetic, algebra, calculus, geometry, and statistics. With a solid understanding of mathematics, you can work with numerical data, make accurate calculations, and analyze complex patterns and relationships.

How to Improve Mathematics

Improving your mathematical skills involves consistent practice and a growth mindset. Revisiting basic concepts, such as arithmetic operations and algebraic equations, to build a strong foundation. As you progress, delve into more advanced topics like calculus and statistics to understand quantitative analysis better. Embrace challenges and problem-solving exercises to enhance your critical thinking abilities, and seek out resources like textbooks, online courses, or tutorials to reinforce your knowledge.

Practical application is essential for strengthening your mathematical skills. Engage in real-world problems and projects that require quantitative analysis. Work with data sets, interpret graphs, and tackle mathematical modeling tasks. Collaborate with others and seek feedback to gain different perspectives and learn new approaches to problem-solving. The more you immerse yourself in mathematical applications, the more proficient and confident you will become in handling quantitative challenges across various fields. Remember, with determination and perseverance. You can continually improve your mathematical skills and unlock a world of opportunities in the data-driven landscape.

Data analysis is a vital quantitative skill that involves collecting, cleaning, organizing, and interpreting data to extract valuable insights and make informed decisions. It encompasses various techniques, including statistical methods, data visualization, and data mining. Mastering data analysis empowers you to uncover patterns, trends, and correlations within datasets, enabling you to draw meaningful conclusions and address complex problems.

How to Improve Data Analysis

To improve your data analysis skills, familiarize yourself with various data manipulation and cleaning techniques. Learn how to handle missing data, remove outliers, and transform data into a usable format. Next, dive into statistical concepts such as hypothesis testing, regression analysis, and descriptive statistics. Understanding these methods will help you draw accurate conclusions from data and support your decision-making process.

Practice is key to honing your data analysis skills. Seek out real-world datasets and work on projects that require data analysis. Engage in data-driven research, participate in data analysis competitions, or collaborate on data projects with others. Leveraging data analysis tools and software like Python, R, or Excel will also aid in gaining hands-on experience. Continuously challenge yourself to tackle increasingly complex datasets and problems, and seek feedback from peers or mentors to refine your analytical techniques. By combining theoretical knowledge with practical experience, you will become a proficient data analyst, capable of extracting valuable insights from data and driving evidence-based decision-making in diverse domains.

Critical thinking is a foundational quantitative skill that involves analyzing, evaluating, and synthesizing information objectively to make reasoned decisions and solve problems effectively. It encompasses logical reasoning, questioning assumptions, and considering different perspectives. Mastering critical thinking empowers you to approach complex issues with a clear and open mind, make well-informed choices, and overcome challenges more efficiently.

How to Improve Critical Thinking

To improve your critical thinking skills, start by practicing active reading and engaging with diverse sources of information. Question the author’s arguments, identify biases, and assess the validity of the evidence presented. Cultivate a habit of seeking alternative viewpoints to broaden your understanding of complex topics and strengthen your ability to evaluate arguments objectively.

Engaging in thought-provoking discussions and debates can also sharpen your critical thinking skills. Participate in group discussions or join forums where ideas are exchanged and challenged. Defend your viewpoints logically and be receptive to constructive criticism. Through this process, you’ll develop the ability to analyze different perspectives and refine your own arguments.

Additionally, solve puzzles, riddles, and brain-teasers regularly to enhance problem-solving abilities. These activities stimulate your mind and encourage creative thinking, essential in critical thinking. Embrace intellectual curiosity, be open to learning from various disciplines, and continuously question assumptions and conclusions. By consistently practicing critical thinking, you’ll become more adept at making informed decisions, solving complex problems, and navigating the challenges of a data-rich world.

Related :  Quantitative Analyst vs. Data Scientist – What’s The Difference?

Modeling and simulation is a powerful quantitative skill that involves creating mathematical or computational models to represent real-world systems and processes. These models help you understand and analyze complex phenomena, make predictions, and simulate different scenarios to gain insights into how the system behaves under various conditions. Mastering modeling and simulation empower you to solve complex problems, optimize processes, and make data-driven decisions in diverse fields.

How to Improve Modeling and Simulation

To improve your modeling and simulation skills, start by gaining a strong foundation in mathematics, especially in calculus, differential equations, and linear algebra. These mathematical concepts are the building blocks of many modeling techniques. Familiarize yourself with relevant software and programming languages like Python, MATLAB, or simulation-specific tools. Practice implementing models and simulations with real data to understand how they apply to specific situations and improve your technical proficiency.

Study and analyze existing models and simulations in your area of interest. By examining how experts have approached similar problems, you can learn valuable insights and adapt their approaches to your own work. Engage in projects that require creating models and simulations and challenge yourself to develop innovative ways to represent complex systems. Collaborate with professionals in your field or join simulation-focused communities to share knowledge and receive feedback on your work. With dedication and continuous learning, you can enhance your modeling and simulation skills and contribute to cutting-edge research and problem-solving in various domains.

Problem-solving is a fundamental quantitative skill that involves the ability to approach challenges methodically, analyze them, and devise effective solutions. It encompasses critical thinking, data analysis, and decision-making to tackle complex issues across various domains. Mastering problem-solving empowers you to identify problems, break them down into manageable parts, and apply quantitative and qualitative methods to reach well-reasoned conclusions.

How to Improve Problem-Solving

To improve your problem-solving skills, embrace a growth mindset and view challenges as opportunities to learn and grow. Analyze problems systematically by breaking them into smaller components and understanding the relationships between them. Practice active brainstorming to generate multiple solutions and evaluate each option’s feasibility and potential outcomes.

Foster collaboration and seek diverse perspectives by discussing problems with colleagues or mentors. Working in teams can provide valuable insights and different problem-solving approaches. Continuously seek opportunities to apply your problem-solving skills in academic studies, professional work, or personal projects. Embrace failures as learning experiences and use feedback to refine your problem-solving strategies. As you encounter new problems, keep track of your approach, document the steps you take, and reflect on the effectiveness of your solutions. Over time, your problem-solving skills will strengthen, and you will become a resourceful and confident solver of complex quantitative challenges.

Related :  Problem-Solving Interview Questions & Answers

Computer programming is a crucial quantitative skill that involves writing instructions in programming languages to create software, applications, and algorithms. It allows you to automate tasks, manipulate data, and implement complex quantitative models. Mastering computer programming empowers you to turn ideas into reality and leverage the power of technology to solve a wide range of quantitative problems.

How to Improve Computer Programming

To improve your computer programming skills, select a programming language that aligns with your goals and interests. Popular languages like Python, R, or Java offer robust capabilities for quantitative tasks. Begin with the basics, such as learning syntax, variables, and control structures. As you gain confidence, progress to more advanced topics like functions, object-oriented programming, and data structures.

Engage in hands-on projects to apply your programming skills. Work on real-world problems, tackle coding challenges and develop small applications or scripts. Collaborate with others in coding communities or join open-source projects to gain exposure to different coding styles and problem-solving approaches. Seek feedback from peers or mentors to improve your code quality and efficiency. Embrace continuous learning by exploring online tutorials, coding boot camps, or advanced courses in your chosen programming language. As you persistently practice and refine your programming abilities, you’ll become adept at using this quantitative skill to create innovative solutions and contribute to various quantitative domains.

Financial analysis is a vital quantitative skill that involves examining financial data, statements, and economic trends to evaluate the financial health and performance of individuals, companies, or organizations. It encompasses skills like ratio analysis, cash flow analysis, and risk assessment. Mastering financial analysis empowers you to make informed investment decisions, assess business profitability, and manage financial risks effectively.

How to Improve Financial Analysis

To improve your financial analysis skills, familiarize yourself with financial statements like balance sheets, income statements, and cash flow statements. Learn how to interpret these documents and extract meaningful information about a company’s financial position and performance. Practice calculating and interpreting financial ratios to assess a business’s liquidity, profitability, and leverage.

Stay updated on economic and financial market trends to understand their impact on financial analysis. Follow news and market reports and analyze how economic indicators influence financial data. Engage in case studies and financial modeling exercises to simulate real-world scenarios and strengthen your analytical abilities. Seek internships or work opportunities in finance-related roles to gain practical experience and exposure to financial analysis in a professional setting. Seek feedback from experienced financial analysts and mentors to refine your skills and build confidence in your financial analysis capabilities. With dedication and continuous learning, you can become a proficient financial analyst capable of providing valuable insights and recommendations in the dynamic world of finance.

Economics and econometrics are valuable quantitative skills that study economic systems, behavior, and trends. Furthermore, Economics deals with understanding how individuals, businesses, and governments make choices to allocate resources to satisfy their needs and wants. Econometrics involves applying statistical and mathematical methods to economic data to develop and test economic models. Mastering economics and econometrics empower you to analyze economic phenomena, forecast trends, and evaluate policy impacts.

How to Improve Economics and Econometrics

To improve your skills in economics and econometrics, start by building a strong foundation in economic principles, theories, and concepts. Understand the fundamental factors influencing supply and demand, market structures, and economic growth. As you progress, familiarize yourself with statistical techniques commonly used in econometrics, such as regression analysis, time-series analysis, and hypothesis testing.

Engage in economic research and data analysis projects to gain hands-on experience. Utilize economic databases, access publicly available economic data, and practice applying econometric methods to analyze the data. Consider taking specialized courses or pursuing advanced degrees in economics or econometrics to deepen your knowledge and expertise. Collaborate with professors, researchers, or peers to receive feedback on your work and exchange ideas. Embrace interdisciplinary approaches by integrating knowledge from related fields such as finance, international relations, or environmental studies. By continuously challenging yourself to apply economic principles and econometric methods to real-world problems, you’ll become a skilled economist capable of contributing valuable to economic research and policy analysis.

These are essential quantitative skills for gathering and analyzing academic, business, or social data. Research involves designing studies, formulating hypotheses, and collecting data through various methods such as surveys, experiments, or observations. Surveys are specific data collection tools that involve asking a targeted group of individuals questions to gather information about their opinions, behaviors, or preferences. Mastering research and surveys empower you to obtain valuable insights, draw meaningful conclusions, and contribute to evidence-based decision-making.

How to Improve Research and Surveys

To improve your skills in research and surveys, start by learning about research methodologies and survey design. Understand the different types of research approaches, sampling techniques, and data collection methods. Practice creating survey questionnaires that are clear, unbiased, and effectively capture the information you need. Consider using online survey platforms to distribute surveys and analyze the responses efficiently.

Emphasize the importance of ethics in research and surveys. Familiarize yourself with ethical guidelines for conducting research involving human subjects, ensuring confidentiality, and obtaining informed consent. Participate in research projects or volunteer to assist with surveys to gain practical experience. Collaborate with experienced researchers or survey specialists to learn from their expertise and receive feedback on your own work. Continuously review and improve your research and survey techniques based on feedback and evolving best practices. By refining your skills and adhering to rigorous research standards, you’ll become a proficient researcher capable of conducting insightful studies and providing valuable contributions to your field of interest.

Data visualization is a crucial quantitative skill that involves presenting data in graphical or visual formats to convey complex information in a clear and intuitive manner. It encompasses various visualization techniques such as charts, graphs, maps, and infographics. Mastering data visualization empowers you to communicate data-driven insights effectively, enabling others to understand trends, patterns, and relationships within datasets more easily.

How to Improve Data Visualization

To improve your data visualization skills, start by understanding the principles of effective data visualization. Learn about different types of charts and graphs and when to use each to best represent your data. Practice using data visualization tools like Tableau, Excel, or Python libraries (e.g., Matplotlib, Seaborn) to create compelling visualizations. Experiment with different color schemes, fonts, and design elements to enhance the visual appeal and clarity of your visualizations.

Seek inspiration from existing data visualization examples and data-driven stories. Analyze how other professionals and data journalists present complex information visually and learn from their techniques. Participate in data visualization challenges or competitions to challenge yourself and receive feedback from a broader audience. Collaborate with peers or mentors in data-related fields to exchange ideas and insights. By continuously practicing data visualization and incorporating feedback into your work, you’ll develop the skills to create impactful visualizations that effectively communicate data insights and aid decision-making in diverse domains.

Quantitative Skills Conclusion

In conclusion, developing quantitative skills is paramount in today’s data-driven world. Whether you are a student, a professional, or an aspiring researcher, honing these skills can significantly enhance your problem-solving abilities and boost your career prospects. Working with numbers, analyzing data, and making informed decisions based on quantitative evidence is highly valued across various fields and industries.

Improving these skills requires dedication, practice, and a growth mindset. Embrace challenges and seek opportunities to apply quantitative techniques in your academic or professional projects. Use online courses, tutorials, and resources to reinforce your knowledge and learn new methodologies. Collaborate with others to gain different perspectives and approaches to problem-solving. Seek feedback from mentors or experts to refine your techniques and continue to grow.

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1.4: Quantitative Analysis

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Quantitative Analysis: An Illustrative Example

Quantitative Analysis is the practice of analyzing the quantities in a situation. It is an important part of solving application problems because one cannot truly understand a problem that involves quantities (something one should do before trying to solve a problem) without understanding what each quantity represents and how it relates to other quantities.

Let's look at Exercise 58 from Section 3.1 of the OpenStax textbook Elementary Algebra, Second Edition .

Travis bought a pair of boots on sale for $25 off the original price. He paid $60 for the boots. What was the original price of the boots?

To analyze the quantities, we must be able to identify them.

Example \(\PageIndex{1}\)

Identify the quantities in this exercise. Determine if each quantity is relevant to the problem we are trying to solve. If the value of the quantity is known, state the value.

  • Travis bought a pair of boots. So, there are two boots. This is not relevant to the problem we are trying to solve, however, because everything is about the pair, not each boot individually.
  • The boots had an original price. This is relevant to the problem since that is what we are asked to find. We do not know this value.
  • The boots had a sale price. This is relevant to the problem because it is related to the original price. The sale price is $60.
  • The boots were discounted. This is relevant to the problem since the discount tells us how the original price and the sale price are related. The discount is $25.

Sometimes people like to draw pictures to show relationships between quantities. This is not necessary to every use of Quantitative Analysis, but it is an option. Someone might draw something somewhat literal, in this case maybe stacks of money or even individual bills. Someone might instead draw something more abstract. Let's see an example of that.

A rectangular box labeled "Sale Price = $60" next to a black rectangle. Below is a rectangular box the length of the rectangular box on top and the black rectangle put together. This is labeled "Original Price = $__"

In the image above, we see a representation of the sale price, the original price, and the fact that the sale price is less than the original price. The amount by which the sale price is less than the original price is represented by the black rectangle.

With or without the picture, we want to ask ourselves, how the quantities are related. In our identification of the quantities, we noted that the discount tells us how the other two important quantities (the original price and the sale price) are related. Since the boot are "on sale for $25 off the original price", this means that the sale price is the result of taking the discount away from the original price. Let's describe that mathematically.

Write a mathematical equation representing the fact that the sale price (which is $60) is the result of taking the discount (which is $25) away from the original price (which is unknown).

$60 = Original Price – $25

Now that we have represented the relationship between the quantities and we know which quantity we are trying to find, we can find that quantity using one of many strategies. In this course, you will learn about many strategies from the perspective of a teacher. But you have already learned this, so use whichever strategy you like to find the original price. Then check your answer below.

Solve the exercise.

Since $60 = Original Price – $25, we know $60 +$25 = Original Price. 60+25 = 85, so the original price of the boots is $85.

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  • Understanding QA
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Example of Quantitative Analysis in Finance

Drawbacks and limitations of quantitative analaysis, using quantitative finance outside of finance, the bottom line.

  • Quantitative Analysis

Quantitative Analysis (QA): What It Is and How It's Used in Finance

quantitative analysis problem solving

Ariel Courage is an experienced editor, researcher, and former fact-checker. She has performed editing and fact-checking work for several leading finance publications, including The Motley Fool and Passport to Wall Street.

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Quantitative analysis (QA) refers to methods used to understand the behavior of financial markets and make more informed investment or trading decisions. It involves the use of mathematical and statistical techniques to analyze financial data. For instance, by examining past stock prices, earnings reports, and other information, quantitative analysts, often called “ quants ,” aim to forecast where the market is headed.

Unlike fundamental analysis that might focus on a company's management team or industry conditions, quantitative analysis relies chiefly on crunching numbers and complex computations to derive actionable insights.

Quantitative analysis can be a powerful tool, especially in modern markets where data is abundant and computational tools are advanced, enabling a more precise examination of the financial landscape. However, many also believe that the raw numbers produced by quantitative analysis should be combined with the more in-depth understanding and nuance afforded by qualitative analysis .

Key Takeaways

  • Quantitative analysis (QA) is a set of techniques that use mathematical and statistical modeling, measurement, and research to understand behavior.
  • Quantitative analysis presents financial information in terms of a numerical value.
  • It's used for the evaluation of financial instruments and for predicting real-world events such as changes in GDP.
  • While powerful, quantitative analysis has some drawbacks that can be supplemented with qualitative analysis.

Understanding Quantitative Analysis

Quantitative analysis (QA) in finance refers to the use of mathematical and statistical techniques to analyze financial & economic data and make trading, investing, and risk management decisions.

QA starts with data collection, where quants gather a vast amount of financial data that might affect the market. This data can include anything from stock prices and company earnings to economic indicators like inflation or unemployment rates. They then use various mathematical models and statistical techniques to analyze this data, looking for trends, patterns, and potential investment opportunities. The outcome of this analysis can help investors decide where to allocate their resources to maximize returns or minimize risks.

Some key aspects of quantitative analysis in finance include:

  • Statistical analysis - this aspect of quantitative analysis involves examining data to identify trends and relationships, build predictive models, and make forecasts. Techniques used can include regression analysis , which helps in understanding relationships between variables; time series analysis , which looks at data points collected or recorded at a specific time; and Monte Carlo simulations , a mathematical technique that allows you to account for uncertainty in your analyses and forecasts. Through statistical analysis, quants can uncover insights that may not be immediately apparent, helping investors and financial analysts make more informed decisions.
  • Algorithmic trading - this entails using computer algorithms to automate the trading process. Algorithms can be programmed to carry out trades based on a variety of factors such as timing, price movements, liquidity changes, and other market signals. High-frequency trading (HFT), a type of algorithmic trading, involves making a large number of trades within fractions of a second to capitalize on small price movements. This automated approach to trading can lead to more efficient and often profitable trading strategies.
  • Risk modeling - risk is an inherent part of financial markets. Risk modeling involves creating mathematical models to measure and quantify various risk exposures within a portfolio. Methods used in risk modeling include Value-at-Risk (VaR) models, scenario analysis , and stress testing . These tools help in understanding the potential downside and uncertainties associated with different investment scenarios, aiding in better risk management and mitigation strategies.
  • Derivatives pricing - derivatives are financial contracts whose value is derived from other underlying assets like stocks or bonds. Derivatives pricing involves creating mathematical models to evaluate these contracts and determine their fair prices and risk profiles. A well-known model used in this domain is the Black-Scholes model , which helps in pricing options contracts . Accurate derivatives pricing is crucial for investors and traders to make sound financial decisions regarding buying, selling, or hedging with derivatives.
  • Portfolio optimization - This is about constructing a portfolio in such a way that it yields the highest possible expected return for a given level of risk. Techniques like Modern Portfolio Theory (MPT) are employed to find the optimal allocation of assets within a portfolio. By analyzing various asset classes and their expected returns, risks, and correlations, quants can suggest the best mix of investments to achieve specific financial goals while minimizing risk.

The overall goal is to use data, math, statistics, and software to make more informed financial decisions, automate processes, and ultimately generate greater risk-adjusted returns.

Quantitative analysis is widely used in central banking, algorithmic trading, hedge fund management, and investment banking activities. Quantitative analysts, employ advanced skills in programming, statistics, calculus, linear algebra etc. to execute quantitative analysis.

Quantitative Analysis vs. Qualitative Analysis

Quantitative analysis relies heavily on numerical data and mathematical models to make decisions regarding investments and financial strategies. It focuses on the measurable, objective data that can be gathered about a company or a financial instrument.

But analysts also evaluate information that is not easily quantifiable or reduced to numeric values to get a better picture of a company's performance. This important qualitative data can include reputation, regulatory insights, or employee morale. Qualitative analysis thus focuses more on understanding the underlying qualities of a company or a financial instrument, which may not be immediately quantifiable.

Quantitative isn't the opposite of qualitative analysis. They're different and often complementary philosophies. They each provide useful information for informed decisions. When used together. better decisions can be made than using either one in isolation.

Some common uses of qualitative analysis include:

  • Management Evaluation: Qualitative analysis is often better at evaluating a company's management team, their experience, and their ability to lead the company toward growth. While quantifiable metrics are useful, they often cannot capture the full picture of management's ability and potential. For example, the leadership skills, vision, and corporate culture instilled by management are intangible factors that can significantly impact a company's success, yet are difficult to measure with numbers alone.
  • Industry Analysis: It also includes an analysis of the industry in which the company operates, the competition, and market conditions. For instance, it can explore how changes in technology or societal behaviors could impact the industry. Qualitative approaches can also better identify barriers to entry or exit, which can affect the level of competition and profitability within the industry.
  • Brand Value and Company Reputation: The reputation of a company, its brand value, and customer loyalty are also significant factors considered in qualitative analysis. Understanding how consumers perceive the brand, their level of trust, and satisfaction can provide insights into customer loyalty and the potential for sustained revenue. This can be done through focus groups, surveys, or interviews.
  • Regulatory Environment: The regulatory environment, potential legal issues, and other external factors that could impact a company are also analyzed qualitatively. Evaluating a company's compliance with relevant laws, regulations, and industry standards to ascertain its legal standing and the potential risk of legal issues. In addition, understanding a company's ethical practices and social responsibility initiatives, that can influence its relationship with stakeholders and the community at large.

Suppose you are interested in investing in a particular company, XYZ Inc. One way to evaluate its potential as an investment is by analyzing its past financial performance using quantitative analysis. Let's say, over the past five years, XYZ Inc. has been growing its revenue at an average rate of 8% per year. You decide to use regression analysis to forecast its future revenue growth. Regression analysis is a statistical method used to examine the relationship between variables.

After collecting the necessary data, you run a simple linear regression with the year as the independent variable and the revenue as the dependent variable. The output gives you a regression equation, let's say, R e v e n u e = 100 + 8 ( Y e a r ) Revenue=100+8(Year) R e v e n u e = 100 + 8 ( Y e a r ) . This equation suggests that for every year, the revenue of XYZ Inc. increases by $8 million, starting from a base of $100 million. This quantitative insight could be instrumental in helping you decide whether XYZ Inc. represents a good investment opportunity based on its historical revenue growth trend.

However, while you can quantify revenue growth for the firm and make predictions, the reasons for why may not be apparent from quantitative number crunching.

Augmenting with Qualitative Analysis

Qualitative analysis can provide a more nuanced understanding of XYZ Inc.'s potential. You decide to delve into the company's management and industry reputation. Through interviews, reviews, and industry reports, you find that the management team at XYZ Inc. is highly regarded with a track record of successful ventures. Moreover, the company has a strong brand value and a loyal customer base.

Additionally, you assess the industry in which XYZ Inc. operates and find it to be stable with a steady demand for the products that XYZ Inc. offers. The regulatory environment is also favorable, and the company has a good relationship with the local communities in which it operates.

By analyzing these qualitative factors, you obtain a more comprehensive understanding of the company's operational environment, the competence of its management team, and its reputation in the market. This qualitative insight complements the quantitative analysis, providing you with a well-rounded view of XYZ Inc.'s investment potential.

Combining both quantitative and qualitative analyses could therefore lead to a more informed investment decision regarding XYZ Inc.

Quantitative analysis, while powerful, comes with certain limitations:

  • Data Dependency: Quantitative analysis is heavily dependent on the quality and availability of numerical data. If the data is inaccurate, outdated, or incomplete, the analysis and the subsequent conclusions drawn will be flawed. As they say, 'garbage-in, garbage-out'.
  • Complexity: The methods and models used in quantitative analysis can be very complex, requiring a high level of expertise to develop, interpret, and act upon. This complexity can also make it difficult to communicate findings to individuals who lack a quantitative background.
  • Lack of Subjectivity: Quantitative analysis often overlooks qualitative factors like management quality, brand reputation, and other subjective factors that can significantly affect a company's performance or a financial instrument's value. In other words, you may have the 'what' without the 'why' or 'how.' Qualitative analysis can augment this blind spot.
  • Assumption-based Modeling: Many quantitative models are built on assumptions that may not hold true in real-world situations. For example, assumptions about normal distribution of returns or constant volatility may not reflect actual market conditions.
  • Over-reliance on Historical Data: Quantitative analysis often relies heavily on historical data to make predictions about the future. However, past performance is not always indicative of future results, especially in rapidly changing markets or unforeseen situations like economic crises.
  • Inability to Capture Human Emotion and Behavior: Markets are often influenced by human emotions and behaviors which can be erratic and hard to predict. Quantitative analysis, being number-driven, struggles to properly account for these human factors.
  • Cost and Time Intensive: Developing accurate and reliable quantitative models can be time-consuming and expensive. It requires skilled personnel, sophisticated software tools, and often, extensive computational resources.
  • Overfitting: There's a risk of overfitting , where a model might perform exceedingly well on past data but fails to predict future outcomes accurately because it's too tailored to past events.
  • Lack of Flexibility: Quantitative models may lack the flexibility to adapt to new information or changing market conditions quickly, which can lead to outdated or incorrect analysis.
  • Model Risk: There's inherent model risk involved where the model itself may have flaws or errors that can lead to incorrect analysis and potentially significant financial losses.

Understanding these drawbacks is crucial for analysts and decision-makers to interpret quantitative analysis results accurately and to balance them with qualitative insights for more holistic decision-making.

Quantitative analysis is a versatile tool that extends beyond the realm of finance into a variety of fields. In the domain of social sciences, for instance, it's used to analyze behavioral patterns, social trends, and the impact of policies on different demographics. Researchers employ statistical models to examine large datasets, enabling them to identify correlations, causations, and trends that can provide a deeper understanding of human behaviors and societal dynamics. Similarly, in the field of public policy, quantitative analysis plays a crucial role in evaluating the effectiveness of different policies, analyzing economic indicators, and forecasting the potential impacts of policy changes. By providing a method to measure and analyze data, it aids policymakers in making informed decisions based on empirical evidence.

In the arena of healthcare, quantitative analysis is employed for clinical trials, genetic research, and epidemiological studies to name a few areas. It assists in analyzing patient data, evaluating treatment outcomes, and understanding disease spread and its determinants. Meanwhile, in engineering and manufacturing, it's used to optimize processes, improve quality control, and enhance operational efficiency. By analyzing data related to production processes, material properties, and operational performance, engineers can identify bottlenecks, optimize workflows, and ensure the reliability and quality of products. Additionally, in the field of marketing, quantitative analysis is fundamental for market segmentation, advertising effectiveness, and consumer satisfaction studies. It helps marketers understand consumer preferences, the impact of advertising campaigns, and the market potential for new products. Through these diverse applications, quantitative analysis serves as a bedrock for data-driven decision-making, enabling professionals across different fields to derive actionable insights from complex data.

What Is Quantitative Analysis Used for in Finance?

Quantitative analysis is used by governments, investors, and businesses (in areas such as finance, project management, production planning, and marketing) to study a certain situation or event, measure it, predict outcomes, and thus help in decision-making. In finance, it's widely used for assessing investment opportunities and risks. For instance, before venturing into investments, analysts rely on quantitative analysis to understand the performance metrics of different financial instruments such as stocks, bonds, and derivatives. By delving into historical data and employing mathematical and statistical models, they can forecast potential future performance and evaluate the underlying risks. This practice isn't just confined to individual assets; it's also essential for portfolio management. By examining the relationships between different assets and assessing their risk and return profiles, investors can construct portfolios that are optimized for the highest possible returns for a given level of risk.

What Kind of Education Do You Need to Be a Quant?

Individuals pursuing a career in quantitative analysis usually have a strong educational background in quantitative fields like mathematics, statistics, computer science, finance, economics, or engineering. Advanced degrees (Master’s or Ph.D.) in quantitative disciplines are often preferred, and additional coursework or certifications in finance and programming can also be beneficial.

What Is the Difference Between Quantitative Analysis and Fundamental Analysis?

While both rely on the use of math and numbers, fundamental analysis takes a broader approach by examining the intrinsic value of a security. It dives into a company's financial statements, industry position, the competence of the management team, and the economic environment in which it operates. By evaluating factors like earnings, dividends, and the financial health of a company, fundamental analysts aim to ascertain the true value of a security and whether it is undervalued or overvalued in the market. This form of analysis is more holistic and requires a deep understanding of the company and the industry in which it operates.

How Does Artificial Intelligence (AI) Influence Quantitative Analysis?

Quantitative analysis often intersects with machine learning (ML) and other forms of artificial intelligence (AI). ML and AI can be employed to develop predictive models and algorithms based on the quantitative data. These technologies can automate the analysis process, handle large datasets, and uncover complex patterns or trends that might be difficult to detect through traditional quantitative methods.

Quantitative analysis is a mathematical approach that collects and evaluates measurable and verifiable data in order to evaluate performance, make better decisions, and predict trends. Unlike qualitative analysis, quantitative analysis uses numerical data to provide an explanation of "what" happened, but not "why" those events occurred.

DeFusco, R. A., McLeavey, D. W., Pinto, J. E., Runkle, D. E., & Anson, M. J. (2015). Quantitative investment analysis . John Wiley & Sons.

University of Sydney. " On Becoming a Quant ," Page 1

Linsmeier, Thomas J., and Neil D. Pearson. " Value at risk ." Financial analysts journal 56, no. 2 (2000): 47-67.

Fischer, Black, and Myron Scholes, " The Pricing of Options and Corporate Liabilities ." Journal of Political Economy, vol. 81, no. 3, 1974, pp. 637-654.

Francis, J. C., & Kim, D. (2013). Modern portfolio theory: Foundations, analysis, and new developments . John Wiley & Sons.

Kaczynski, D., Salmona, M., & Smith, T. (2014). " Qualitative research in finance ." Australian Journal of Management , 39 (1), 127-135.

quantitative analysis problem solving

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Kaizen is about changing the way things are. If you assume that things are all right the way they are, you can’t do kaizen. So change something! —Taiichi Ohno

Inspect and Adapt

Inspect & adapt: overview.

quantitative analysis problem solving

The Inspect and Adapt (I&A) is a significant event held at the end of each PI, where the current state of the Solution is demonstrated and evaluated. Teams then reflect and identify improvement backlog items via a structured problem-solving workshop.

The Agile Manifesto emphasizes the importance of continuous improvement through the following principle: “At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.”

In addition, SAFe includes ‘relentless improvement’ as one of the four SAFe Core Values as well as a dimension of the Continuous Learning Culture core competency. While opportunities to improve can and should occur continuously throughout the PI (e.g., Iteration Retrospectives ), applying some structure, cadence, and synchronization helps ensure that there is also time set aside to identify improvements across multiple teams and Agile Release Trains .

All ART stakeholders participate along with the Agile Teams in the I&A event. The result is a set of improvement backlog items that go into the ART Backlog for the next PI Planning event. In this way, every ART improves every PI. A similar I&A event is held by Solution Trains .

The I&A event consists of three parts:

PI System Demo

  • Quantitative and qualitative measurement
  • Retrospective and problem-solving workshop

Participants in the I&A should be, wherever possible, all the people involved in building the solution. For an ART, this includes:

  • The Agile teams
  • Release Train Engineer (RTE)
  • System and Solution Architects
  • Product Management ,  Business Owners , and other stakeholders

Additionally, Solution Train stakeholders may also attend this event.

The PI System Demo is the first part of the I&A, and it’s a little different from the regular system demos after every iteration. This demo shows all the Features the ART has developed during the PI. Typically the audience is broader; for example, Customers or Portfolio representatives are more likely to attend this demo. Therefore, the PI system demo tends to be a little more formal, and extra preparation and setup are usually required. But like any other system demo, it should be timeboxed to an hour or less, with the level of abstraction high enough to keep stakeholders actively engaged and providing feedback.

Before or as part of the PI system demo, Business Owners collaborate with each Agile Team to score the actual business value achieved for each of their Team PI Objectives , as illustrated in Figure 1.

The achievement score is calculated by separately totaling the business value for the plan and actual columns. The uncommitted objectives are not included in the total plan. However, they are part of the total actual. Then divide the actual total by the planned total to calculate the achievement score illustrated in Figure 1.

Quantitative and Qualitative Measurement

In the second part of the I&A event, teams collectively review any quantitative and qualitative metrics they have agreed to collect, then discuss the data and trends. In preparation for this, the RTE and the Solution Train Engineer are often responsible for gathering the information, analyzing it to identify potential issues, and facilitating the presentation of the findings to the ART.

Each team’s planned vs. actual business value is rolled up to create the ART predictability measure, as shown in Figure 2.

Reliable trains should operate in the 80–100 percent range; this allows the business and its external stakeholders to plan effectively. (Note: Uncommitted objectives are excluded from the planned commitment. However, they are included in the actual business value achievement, as can also be seen in Figure 1.)


The teams then run a brief (30 minutes or less) retrospective to identify a few significant issues they would like to address during the problem-solving workshop . There is no one way to do this; several different Agile retrospective formats can be used [3].

Based on the retrospective and the nature of the problems identified, the facilitator helps the group decide which issues they want to tackle. Each team may work on a problem, or, more typically, new groups are formed from individuals across different teams who wish to work on the same issue. This self-selection helps provide cross-functional and differing views of the problem and brings together those impacted and those best motivated to address the issue.

Key ART stakeholders—including Business Owners, customers, and management—join the retrospective and problem-solving workshop teams. The Business Owners can often unblock the impediments outside the team’s control.

Problem-Solving Workshop

The ART holds a structured, root-cause problem-solving workshop to address systemic problems. Root cause analysis provides a set of problem-solving tools used to identify the actual causes of a problem rather than just fixing the symptoms. The RTE typically facilitates the session in a timebox of two hours or less.

Figure 3 illustrates the steps in the problem-solving workshop.

The following sections describe each step of the process.

Agree on the Problem(s) to Solve

American inventor Charles Kettering is credited with saying that “a problem well stated is a problem half solved.” At this point, the teams have self-selected the problem they want to address. But do they agree on the details of the problem, or is it more likely that they have differing perspectives? To this end, the teams should spend a few minutes clearly stating the problem, highlighting the ‘what,’ ‘where,’ ‘when,’ and ‘impact’ as concisely as possible. Figure 4 illustrates a well-written problem statement.

Perform Root Cause Analysis

Effective problem-solving tools include the fishbone diagram and the ‘5 Whys.’ Also known as an Ishikawa Diagram , a fishbone diagram is a visual tool to explore the causes of specific events or sources of variation in a process. Figure 5 illustrates the fishbone diagram with a summary of the previous problem statement written at the head of the ‘fish.’

For our problem-solving workshop, the main bones often start with the default categories of people, processes, tools, program, and environment. However, these categories should be adapted as appropriate.

Team members then brainstorm causes that they think contribute to solving the problem and group them into these categories. Once a potential cause is identified, its root cause is explored with the 5 Whys technique. By asking ‘why’ five times, the cause of the previous cause is uncovered and added to the diagram. The process stops once a suitable root cause has been identified, and the same process is then applied to the next cause.

Identify the Biggest Root Cause

Pareto Analysis, also known as the 80/20 rule, is used to narrow down the number of actions that produce the most significant overall effect. It uses the principle that 20 percent of the causes are responsible for 80 percent of the problem. It’s beneficial when many possible courses of action compete for attention, which is almost always the case with complex, systemic issues.

Once all the possible causes-of-causes are identified, team members then cumulatively vote on the item they think is the most significant factor contributing to the original problem. They can do this by dot voting. For example, each person gets five votes to choose one or more causes they think are most problematic. The team then summarizes the votes in a Pareto chart, such as the example in Figure 6, which illustrates their collective consensus on the most significant root cause.

Restate the New Problem

The next step is to pick the cause with the most votes and restate it clearly as a problem. Restating it should take only a few minutes, as the teams clearly understand the root cause.

Brainstorm Solutions

At this point, the restated problem will start to imply some potential solutions. The team brainstorms as many possible corrective actions as possible within a fixed timebox (about 15–30 minutes). The rules of brainstorming apply here:

  • Generate as many ideas as possible
  • Do not allow criticism or debate
  • Let the imagination soar
  • Explore and combine ideas

Create Improvement Backlog Items

The team then cumulatively votes on up to three most viable solutions. These potential solutions are written as improvement stories and features, planned in the following PI Planning event. During that event, the RTE helps ensure that the relevant work needed to deliver the identified improvements is planned. This approach closes the loop, thus ensuring that action will be taken and that people and resources are dedicated as necessary to improve the current state.

Following this practice, problem-solving becomes routine and systematic, and team members and ART stakeholders can ensure that the train is solidly on its journey of relentless improvement.

Inspect and Adapt for Solution Trains

The above describes a rigorous approach to problem-solving in the context of a single ART. If the ART is part of a Solution Train, the I&A event will often include key stakeholders from the Solution Train. In larger value streams, however, an additional Solution Train I&A event may be required, following the same format.

Due to the number of people in a Solution Train, attendees at the large solution I&A event cannot include everyone, so stakeholders are selected that are best suited to address the problems. This subset of people consists of the Solution Train’s primary stakeholders and representatives from the various ARTs and Suppliers .

Last update: 22 January 2023

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Problem Sets: Quantitative Analysis

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Analytical chemistry spans nearly all areas of chemistry but involves the development of tools and methods to measure physical properties of substances and apply those techniques to the identification of their presence (qualitative analysis) and quantify the amount present (quantitative analysis) of species in a wide variety of settings. This is the homework exercise section for Harvey's "Analytical Chemistry 2.0" TextMap .

  • 1.E: Introduction to Analytical Chemistry (Exercises) These are homework exercises and select solutions to "Chapter 1: Introduction to Analytical Chemistry" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 2.E: Basic Tools of Analytical Chemistry (Exercises) These are homework exercises and select solutions to "Chapter 2: Basic Tools of Analytical Chemistry" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 3.E: The Vocabulary of Analytical Chemistry (Exercises) These are homework exercises and select solutions to "Chapter 3: The Vocabulary of Analytical Chemistry" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 4.E: Evaluating Analytical Data (Exercises) These are homework exercises and select solutions to "Chapter 4: Evaluating Analytical Data" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 5.E: Standardizing Analytical Methods (Exercises) These are homework exercises and select solutions to "Chapter 5: Standardizing Analytical Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 6.E: Equilibrium Chemistry (Exercises) These are homework exercises and select solutions to "Chapter 6: Equilibrium Chemistry" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 7.E: Collecting and Preparing Samples (Exercises) These are homework exercises and select solutions to "Chapter 7: Collecting and Preparing Samples" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 8.E: Gravimetric Methods (Exercises) These are homework exercises and select solutions to "Chapter 8: Gravimetric Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 9.E: Titrimetric Methods (Exercises) These are homework exercises and select solutions to "Chapter 9: Titrimetric Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 10.E: Spectroscopic Methods (Exercises) These are homework exercises and select solutions to "Chapter 10: Spectroscopic Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 11.E: Electrochemical Methods (Exercises) These are homework exercises and select solutions to "Chapter 11: Electrochemical Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 12.E: Chromatographic and Electrophoretic Methods (Exercises) These are homework exercises and select solutions to "Chapter 12: Chromatographic and Electrophoretic Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 13.E: Kinetic Methods (Exercises) These are homework exercises and select solutions to "Chapter 13: Kinetic Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 14.E: Developing a Standard Method (Exercises) These are homework exercises and select solutions to "Chapter 14: Developing a Standard Method" from Harvey's "Analytical Chemistry 2.0" Textmap.
  • 15.E: Quality Assurance (Exercises) These are homework exercises and select solutions to "Chapter 15: Quality Assurance" from Harvey's "Analytical Chemistry 2.0" Textmap.

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quantitative analysis problem solving

Guide to SAT Math Problem Solving and Data Analysis + Practice Questions

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What’s Covered:

Overview of sat math problem solving and data analysis, how will the sat impact my college chances.

  • Strategies for Problem Solving and Data Analysis Questions
  • Practice Questions for Problem Solving and Data Analysis

Final Tips and Strategies

Problem Solving and Data Analysis questions appear on the Calculator section of the SAT Math test and involve applying mathematical knowledge to real-world contexts. These problems can be tough, so if you want to improve your math score, here are some strategies and practice problems to help you out.

The SAT Math section contributes to half of the total SAT score. This section is scored out of 800 and includes three main categories, which each have a subscore out of 15.

Here is the breakdown of each category:

  • Heart of Algebra: 33%
  • Problem Solving and Data Analysis: 29%
  • Passport to Advanced Math: 28%

Additional Topics in Math covers the remaining 10% and consists of a variety of different mathematical topics.

The Problem Solving and Data Analysis section tests students’ ability to solve real-world problems using mathematical understanding and skills. This includes quantitative reasoning, interpreting and synthesizing data, and creating representations. These questions never appear on the SAT No Calculator section, so you’ll always be allowed a calculator for them.

Problem Solving and Data Analysis questions ask students to:

  • Use ratios, rates, proportional relationships, and scale drawings to solve single- and multistep problems.
  • Solve single- and multi-step problems involving percentages.
  • Solve single- and multi-step problems involving measurement quantities, units, and unit conversion. 
  • Given a scatterplot, use linear, quadratic, or exponential models to describe how the variables are related.
  • Use the relationship between two variables to investigate key features of the graph.
  • Compare linear growth with exponential growth. 
  • Use two-way tables to summarize categorical data and relative frequencies, and calculate conditional probability. 
  • Make inferences about population parameters based on sample data. 
  • Use statistics to investigate measures of center of data and analyze shape, center, and spread. 
  • Evaluate reports to make inferences, justify conclusions, and determine appropriateness of data collection methods.

Many selective colleges use a metric called the Academic Index (AI) to assess an application’s strength. The AI is calculated based on GPA and SAT/ACT scores, so you should make sure your scores are competitive to increase your chances of admission. Some colleges even automatically reject applicants with AIs that are too low.

To see how your SAT score compares, use CollegeVine’s free Admissions Chances Calculator . This tool will let you know the impact of your SAT score on your chances and will even offer advice to improve other aspects of your profile.

quantitative analysis problem solving

Strategies to Solve Problem Solving and Data Analysis Problems

Problem Solving and Data Analysis problems often involve graphs or data tables, so it’s important to pay attention to titles and labels to make sure you don’t misinterpret the information. 

As you read the question, underline or circle any important numeric information. Also, pay close attention to what exactly the question is asking for.

Because Problem Solving and Data Analysis problems vary, there is no concrete algorithm to approach them. These questions are typically more conceptual than calculation-based, so though a calculator is allowed, you probably won’t need it aside from simple arithmetic. Therefore, the key to these problems is reading carefully and knowing concepts like proportions, median, mean, percent increase, etc.

10 Difficult Problem Solving and Data Analysis Questions

Here are some sample difficult Problem Solving and Data Analysis questions and explanations of how to solve them. Remember, these questions only appear on the Calculator section of the exam, so you will have access to a calculator for all of them.

1. Measures of Central Tendency (Mean/Median/Mode)

quantitative analysis problem solving

Correct Answer: B

This problem involves computing the median. If you have a graphing calculator, this could be done via lists, but since there are only 7 data values, it might be faster to just write this one out. The median is the measure of the middle of the data set, so start by ordering the values from smallest to largest. This results in the following list: 19.5%, 21.9%, 25.9%, 27.9%, 30.1%, 35.5%, 36.4%. From here, we can clearly see that the middle value is 27.9%, so that is our median. 

However, we’re not done here. We now have to compute the difference between the median we just calculated and the median for all 50 states, 26.95%. Subtracting these two values yields 0.95%, which corresponds to answer choice B. 

2. Percent Increase

quantitative analysis problem solving

Percent increase can be a tricky concept if you don’t remember this rule of thumb: “new minus old over old.” In this case, for the percent increase from 2012 to 2013, we take the “new” value, 5,880, and subtract the “old” value, 5,600. This is 280, which we then divide by the “old” to get .05, which is 5%. 

Since the percent increase from 2012 to 2013 was 5%, and this is double the predicted increase from 2013 to 2014, we know that the percent increase from 2013 to 2014 will be half of 5%, or 2.5%.

Then, to calculate the number of subscriptions sold in 2014, we multiply the value in 2013, 5,880, by 2.5%. This yields 147, which means that in 2014, 147 additional subscriptions were sold. So, the total amount of subscriptions sold in 2014 is 5,880 + 147 = 6,027.

3. Analyzing Graphical Data

quantitative analysis problem solving

Since we are presented with a graph, let’s make note of what this graph is showing us. On the y-axis we have speed, and on the x-axis we have time. So, this graph is showing us how Theresa’s speed varies with time. 

When the graph is flat, the speed is unchanging and is therefore constant. When the graph has a positive slope, the speed is increasing, and when the graph has a negative slope, the speed is decreasing. The rates at which it increases and decreases will be constant since the graph is composed of straight lines (and a line has a constant slope, which means it changes at a constant rate).

For questions asking which statement is not true, it’s crucial to take the time to read through each answer choice. First, choice A states that Theresa ran at a constant speed for five minutes. We can see that this is true, since from 5 to 10 minutes, the graph is flat. Next, choice C says that the speed decreased at a constant rate during the last five minutes. This is also true because from 25 to 30 minutes, the graph is a line with negative slope, which indicates decreasing speed. Finally, choice D claims the maximum speed occurs during the last 10 minutes. We can see that the maximum speed (the highest point on the graph) occurs at 25 minutes, which is within the last 10 minutes, so choice D is also true.

By process of elimination, choice B should be correct, but let’s verify. Choice B states that Theresa’s speed was increasing for a longer time than it was decreasing. Speed was increasing from 0 to 5 and 20 to 25 minutes, for a total of 10 minutes. Speed was decreasing from 10 to 20 and 25 to 30 minutes, for a total of 15 minutes. So, the speed decreased for a longer time than it increased, and choice B is false, making it the correct answer.

4. Inference

quantitative analysis problem solving

Correct Answer: D

For questions involving surveys, always remember that generalizations can only be made to the specific population studied. For example, if a study is given to a select group of 5th grade math students, when analyzing the results, you can only generalize to 5th grade math students, not all math students or all 5th graders. 

In this case, the group surveyed was people who liked the book. From these people, 95% disliked the movie. So, from this survey, most people who like the book will then dislike the movie, which corresponds to choice D.

Choices A and C are incorrect since they generalize to people who see movies and people who dislike the book, which doesn’t apply to the population studied. Choice B is incorrect since it falsely generalizes to all people who read books.

5. Proportions

quantitative analysis problem solving

Proportion problems are usually fairly quick, but easy to mess up on if not read carefully. For proportions, the denominator is the total number of things in the group we’re looking at, and the numerator is the specific characteristic we want. 

This question asks for the fraction of the dogs that are fed only dry food. So, the group we’re looking at is “dogs” and the characteristic we want is that they “are fed only dry food.” 

From the table, the total number of dogs is 25. This means the denominator will be 25. Next, we must find the number of dogs which are also only fed dry food, which is 2, according to the table. So, our numerator is 2, and the answer is 2/25.

quantitative analysis problem solving

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6. Scale Factors

quantitative analysis problem solving

For this question, let’s start with what we know: the column is 8 inches tall. We know that 50 coins creates a \(3\frac{7}{8}\) inch column, which is approximately 4 inches. Since this question asks for an approximation, we know that 8 is slightly more than double \(3\frac{7}{8}\), so we’ll need slightly more than \(50\:\cdot\: 2\) pennies to create an 8-inch column. Answer B is the closest number to our approximate value.

If you wanted to be precise, you could set up an equation:

\(8 in\:\cdot\:\frac{50 coins}{3\frac{7}{8} in}\)

Because 50 coins corresponds to a column which is \(3\frac{7}{8}\) inches tall, we set those two values up in a fraction. We decide which value goes in the numerator and which in the denominator based on the units: since we started with 8 inches, we need the \(3\frac{7}{8}\) inches to be in the denominator so that the inches cancel. Then, we are left with the unit in the numerator, which is coins. The question asks for the number of coins, so this is exactly what we want.

At this point, you would use your calculator to solve the expression, and get about 103 coins. This value is closest to answer choice B.

7. Line of Best Fit/Scatterplots

quantitative analysis problem solving

Correct Answer: A

Once again, since we have a graph, let’s take a moment to read the labels. The y-axis shows density and the x-axis shows distance from the sun.

We also see that the line of best fit is sloping downwards. As the distance increases, the density seems to decrease. So, choice A is correct in that larger distances correspond to lesser densities.

Though it wasn’t explicit in this question, an important thing to note about scatterplots is that these relationships show correlation, not causation. Choice C is incorrect because it falsely implies that changes in distance cause changes in density. Choice D is incorrect since though there is no cause and effect relationship, there is a correlation between these two variables.

8. Geometric Applications of Proportions

quantitative analysis problem solving

Correct Answer: 5/18, .277, .278

This problem could be confusing in that so little information is given. However, this problem requires that you recall that the proportion of degrees is equivalent to the proportion of area. So, for this problem, all you have to do is divide 100 by 360, which is the total number of degrees in a circle.

Then, the answer is 100/360, or 5/18. If you’re faced with a similar problem on the test, where there is little to no numeric information, try to work with the numbers you do have and find helpful relationships.

9. Unit Conversions

quantitative analysis problem solving

Correct Answer: 195

Unit conversions are fairly simple once you set up the expression correctly. Start with the information given.

For this problem, the price is $62,400, so we will start with this value. Next, we will multiply this value by fractions. Each fraction will consist of a numerator and denominator which are equivalent, so multiplying by these fractions is the same as multiplying by 1. Here is what the expression would look like:

\(\$62,400\:\cdot\:\frac{1 ounce}{\$20}\:\cdot\:\frac{1 pound}{16 ounces}\)

We decide which value to put in the denominator based on the units. In this case, the dollars and the ounces cancel, leaving us with pounds, which is what the question asked for. Solving this expression results in 195 pounds, the answer to the question. 

10. Probability

quantitative analysis problem solving

Correct Answer: 5/7, .714

Probability questions are similar to proportion questions in that the denominator should be the group we’re looking at and the numerator should be the characteristic we want.

In this case, it is given that we’re looking at contestants who received a score of 5 on one of the three days, and there is a total of 7 such contestants. The characteristic we want is that the contestant received a score of 5 on Day 2 or Day 3. The number of contestants who fit this description is 2 + 3 = 5, so the probability is 5/7.

For Problem Solving and Data Analysis problems, make sure that your answer addresses what the question asked for. Wrong answer choices on the SAT often reflect common student mistakes, so take the time to read Problem Solving and Data Analysis questions carefully.

When studying for the SAT Math section, try to do plenty of practice problems. The best way to get better at math is to do more math.

Here are some other articles that will help you prepare for the SAT Math section:

  • 15 Hardest SAT Math Questions
  • 30 SAT Math Formulas You Need to Know
  • Guide to SAT Math Heart of Algebra + Practice Questions
  • 5 Common SAT Math Mistakes to Avoid
  • 5 Tips to Boost Your Math SAT Score

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Math-minded students thrive in the quantitative methods concentration, which provides quantitative business methods and analysis tools that can be widely applied in a variety of fields.

This concentration focuses on applied problem-solving methodologies where quantitative models are built and used for quantitative decision making. In addition, quantitative analysis courses in this concentration are designed to offer a fine balance between depth and breadth, relevance and rigor, and critical and analytical thinking. 

Where the Quantitative Methods Concentration Will Take You

Understanding statistical forecasting and quantitative business analysis, including how to decipher and use data strategically, is a skill entrepreneurial leaders use to turn problems into opportunities. Being able to predict outcomes using time series forecasting mehtods, and other strategies means taking risks doesn’t scare you and you can pivot when challenges arise.  

The quantitative methods for business concentration provides tools and techniques such as corporate management, investment banking, consulting, information technology, finance, economics, ecommerce, and marketing.  

Babson students who take quantitative analyst courses have gone on to work as consultants in sports and entertainment, cyber security experts and analysts, and leaders in the operation divisions of companies, including their own.  

What You Will Study in Your Quantitative Methods Courses  

To complete this concentration, you will need to complete four advanced quantitative business method s courses . Your options include: 

A Babson course always finds practical applications for the theoretical. This time - series - forecasting methods course is no different, as you analyze time series data in the context of various real-life forecasting situations such as banking, healthcare, sports, and global warming. You gain practical experience with time series data and get comfortable predicting future outcomes, comparing alternative models, and communicating your results and suggestions clearly.  

The ease of data collection coupled with plummeting data storage costs over the last decades have resulted in massive amounts of data that many business organizations have at their fingertips. Effective analysis of those data followed by sound decision-making is what makes a company an analytical competitor. In this quantitative analyst course , you will learn how to apply advanced quantitative tools for solving complex machine learning problems.

Mathematicians and statisticians are playing an increasing role in shaping how athletic contests are played and how they are judged. This course examines some of the underlying quantitative principles that are routinely used. You will apply some statistical techniques (expectations, probability and risk/reward judgments) and some that are deterministic (optimization, ranking and validation.) A variety of software packages will be used to demonstrate the many ways that a mathematical point of view can inform athletes, trainers, administrators, and fans.

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You Will Learn From the Best

At Babson, our faculty are experts, innovators, and forward thinkers in their chosen fields. Here are just some professors sharing their expertise and support with our students in the quantitative methods program.

Richard Cleary,

Richard Cleary

Rick Cleary is a statistician and mathematician with research and consulting interests in a variety of fields including sports and education. Prior to coming to Babson College in 2013, he taught at St. Michael’s College in Vermont, Cornell University, Bentley University, and Harvard University. He has held leadership positions in the Mathematical Association of America, including six years on the Executive Committee as Associate Treasurer. He currently co-chairs the Upper Division Pathways initiative for TPSE Math.

Michelle Li, Assistant Professor, Mathematics, Analytics, Science, and Technology Division

Michelle Li

Michelle Li’s research and teaching interests lie in the domains of operations research, game theory, business analytics, supply chain network management, humanitarian logistics, and sustainable systems. She studies the economics, equilibrium, and dynamics of game theoretic supply chain networks, with applications to sustainability, corporate social responsibility, outsourcing, information asymmetry, and humanitarian blood banking.

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quantitative analysis problem solving

Quantitative Problem Solving Methods in the Airline Industry

A Modeling Methodology Handbook

  • © 2012
  • Cynthia Barnhart 0 ,
  • Barry Smith 1

Dept. Civil & Environmental Engineering, Massachusetts Institute of Technology, Cambridge, USA

You can also search for this editor in PubMed   Google Scholar

Dallas, USA

  • The Editor team for the handbook is first-rate. Cindy Barnhart holds several positions at MIT. She is Chancellor of MIT, Co-Director of MIT's Center for Transportation & Logistics, Co-Director of MIT's Operations Research Center and she is a full professor in the Civil and Environmental Engineering Department at MIT
  • Barry Smith is Chief Scientist for Sabre Inc. and a leading practitioner developing new mathematical and computational solutions to problems in the Airline Industry. He has been called on to develop new solutions for a wide range of airline problems
  • The airline industry is an especially heavy user of Operations Research throughout its operations. Many hundreds of OR professionals new work in this area, either as employees of airline companies or as members of consulting firms that focus on the problems of companies involved in transportation, including especially airlines. A substantial number of academics also do research and consult in this area. Therefore, this handbook should attract a great deal of interest
  • Includes supplementary material: sn.pub/extras

Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 169)

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Table of contents (7 chapters)

Front matter.

  • Customer Modeling
  • Laurie Garrow

Airline Planning and Schedule Development

  • Timothy L. Jacobs, Laurie A. Garrow, Manoj Lohatepanont, Frank S. Koppelman, Gregory M. Coldren, Hadi Purnomo
  • Revenue Management
  • Darius Walczak, E. Andrew Boyd, Roxy Cramer
  • Airline Distribution
  • Dirk Gunther, Richard Ratliff, Abdoul Sylla

Crew Management Information Systems

  • Diego Klabjan, Yu-Ching Lee, Goran Stojković
  • Stefan E. Karisch, Stephen S. Altus, Goran Stojković, Mirela Stojković
  • Air Traffic Flow Management
  • Thomas W. M. Vossen, Robert Hoffman, Avijit Mukherjee

Back Matter

  • Airline Scheduling
  • Crew Management
  • Operations Research
  • Engineering Economics

About this book

This book reviews Operations Research theory, applications and practice in seven major areas of airline planning and operations.  In each area, a team of academic and industry experts provides an overview of the business and technical landscape, a view of current best practices, a summary of open research questions and suggestions for relevant future research.  There are several common themes in current airline Operations Research efforts.  First is a growing focus on the customer in terms of: 1) what they want; 2) what they are willing to pay for services; and 3) how they are impacted by planning, marketing and operational decisions.  Second, as algorithms improve and computing power increases, the scope of modeling applications expands, often re-integrating processes that had been broken into smaller parts in order to solve them in the past.  Finally, there is a growing awareness of the uncertainty in many airline planning and operational processes and decisions.  Airlines now recognize the need to develop ‘robust’ solutions that effectively cover many possible outcomes, not just the best case, “blue sky” scenario. 

Individual chapters cover:

Customer Modeling methodologies, including current and emerging applications.

Airline Planning and Schedule Development, with a look at  many remaining open research questions.

Revenue Management, including a view of current business and technical landscapes, as well as suggested areas for future research.

Airline Distribution -- a comprehensive overview of this newly emerging area.

Crew Management Information Systems, including a review of recent algorithmic advances, as well as the development of information systems that facilitate the integration of crew management modeling with airline planning and operations. Airline Operations, with consideration of recent advances and successes in solving the airline operations problem. Air Traffic Flow Management, including the modeling environment and opportunities for both Air Traffic Flow Management and the airlines.

Editors and Affiliations

Dept. civil & environmental engineering, massachusetts institute of technology, cambridge, usa.

Cynthia Barnhart

Barry Smith

Bibliographic Information

Book Title : Quantitative Problem Solving Methods in the Airline Industry

Book Subtitle : A Modeling Methodology Handbook

Editors : Cynthia Barnhart, Barry Smith

Series Title : International Series in Operations Research & Management Science

DOI : https://doi.org/10.1007/978-1-4614-1608-1

Publisher : Springer New York, NY

eBook Packages : Business and Economics , Business and Management (R0)

Copyright Information : Springer Science+Business Media, LLC 2012

Hardcover ISBN : 978-1-4614-1607-4 Published: 21 December 2011

Softcover ISBN : 978-1-4899-8856-0 Published: 03 March 2014

eBook ISBN : 978-1-4614-1608-1 Published: 22 December 2011

Series ISSN : 0884-8289

Series E-ISSN : 2214-7934

Edition Number : 1

Number of Pages : X, 462

Topics : Operations Research/Decision Theory , Engineering Economics, Organization, Logistics, Marketing , Industrial Organization , Regional/Spatial Science , Civil Engineering , Organization

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  18. Inspect and Adapt

    Quantitative and qualitative measurement; Retrospective and problem-solving workshop; Participants in the I&A should be, ... Root cause analysis provides a set of problem-solving tools used to identify the actual causes of a problem rather than just fixing the symptoms. The RTE typically facilitates the session in a timebox of two hours or less.

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  20. Problem Sets: Quantitative Analysis

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    This book reviews Operations Research theory, applications and practice in seven major areas of airline planning and operations. In each area, a team of academic and industry experts provides an overview of the business and technical landscape, a view of current best practices, a summary of open research questions and suggestions for relevant future research.