What is Wellesley's QR requirement and how do students satisfy it?
Basic Skills Component
The basic skills component is satisfied either by passing the QR Assessment given during the summer and Orientation or by passing the QR basic skills course (QR 140). The assessment and the basic skills course emphasize the practical use of algebra, geometry, basic probability and statistics, graph theory, and estimation. On the assessment and in the course, students are expected to apply these important skills in solving real-world problems. Students must satisfy this component in their first year at the College so that they are prepared to take courses for which solid quantitative skills are essential. Fulfillment of the QR basic skills requirement is a prerequisite for many Wellesley courses, including all QR overlay courses.
Learning Goals for
the QR Basic Skills Course: QR 140
Students will learn to utilize logic, mathematics, and statistics to make decisions as they encounter real world problems in science and economic courses, in their future employment, and in their everyday lives as consumers and citizens. By the end of the semester, students will be able to:
- Set up and solve real-world problems that require multi-step calculations using unit conversions with both familiar and unfamiliar units, scaling, and proportions.
- Calculate with and describe percentages in two-way tables.
- Identify, set up, and solve real-world problems involving linear and exponential growth, using logarithms where appropriate.
- Interpret and perform calculations with numbers in scientific notation.
- Design and carry out multi-step "back-of-the envelope estimations," incorporating geometric formulas for area, volume, and surface area where appropriate.
- Calculate and interpret the mean, median, and standard deviation, and associate these quantities with histograms and written descriptions of data.
- Create spreadsheets to model real-world scenarios and interpret real-world data, incorporating skills from the above learning goals.
Overlay Course Component
The overlay component is designed to engage students in statistical analysis and in the interpretation of data in a specific discipline. In a QR overlay course, students study the framework for data analysis, examine various methods of data collection and measurement, and learn how to represent and summarize data using various statistical distributions. They also study probability in order to understand sampling and inferential statistics. Advanced topics in QR overlay courses include analysis of variance and multiple regression analysis.
Currently QR overlay courses are offered in economics, political science, sociology, education, psychology, astronomy, biology, chemistry, geosciences, physics, and mathematics. Students must satisfy the overlay component before graduation. It is recommended that students take their QR overlay course after they have decided on their major, as some majors require a specific overlay course. For example, psychology majors need to take PSYC 205, the psychology statistics class; economics majors need to take ECON 103/SOC 190, introduction to social science data analysis.
Learning Goals for
the QR Overlay Courses
Students will learn to interpret and to create descriptive statistics of real world data and also to interpret and analyze inferential statistics. More specifically, by the end of the course, students will be able to:
- Explain how empirical questions or hypotheses can be raised, how relevant data can be collected and analyzed to address these questions, and discuss reasonable conclusions and the limits to the analysis.
- Collect relevant data; address possible biases in the data collection; appropriately present the data in tables, in graphs, and in writing.
- Accurately summarize data with measures of central tendency, measures of dispersion, measures of skewness, appropriate distributions and graphs, and measures of correlation.
- Explain the Central Limit Theorem and why the normal distribution is so important in statistics.
- Make and justify inferences from empirical data, using confidence intervals, hypothesis tests, regression analyses, or analysis of variance, as appropriate.
- Transfer the skills in data analysis from one setting (e.g., chemistry laboratories) to another (e.g., economics.)