## Scientific data analysis toolkit a versatile add-in to microsoft excel for windows – journal of chemical education (acs publications) what are the 4 gas giants in the solar system

Scientific Data Analysis Toolkit (SDAT) is a rigorous, versatile, and user-friendly data analysis add-in application for Microsoft Excel for Windows (PC). SDAT uses the familiar Excel environment to carry out most of the analytical tasks used in __data analysis__. It has been designed for student use in manipulating and analyzing data encountered in the physical and biological sciences, from first-year courses, to the graduate level, and in research. A particularly useful feature of SDAT is its ability to perform rigorous regression analysis using both standard and user-defined model functions. This tool, which can accommodate up to seven fitting parameters, provides the standard deviation of regression of the fit, the standard uncertainties of the fitting parameters, and the covariance matrix. It also supports weighted regression analysis. SDAT includes the following functionalities: Descriptive Statistics, Integrate, Differentiate, Smooth, Spline, Plot, and Regression Analysis. The use of the Regression Analysis tool is illustrated with several examples including the unweighted and weighted analysis of the nonlinear and linearized forms an exponential decay and an example of the use of the covariance term in a propagation of errors calculation. Other example topics are chemical and enzyme kinetics, vapor pressure, and quantum chemistry computational results. SDAT is provided as associated content.

Standards and expectations of the rigor and sophistication of data analysis have increased dramatically as a result of the increasing availability and power of computing resources, from centralized mainframe resources, to dedicated laboratory computers, to hand-held and desktop or laptop computers, and to smart phones. Before the advent of these computing resources, when slide rules and mechanical calculators were the means available to **most students** and researchers, it was necessary to transform data into a linearized form to obtain the slope and intercept values for further analysis. Moreover, it was often considered acceptable practice to “eye ball” a straight line through hand-plotted data points for analysis according to a linear model. Uncertainties in these values were sometimes determined by manipulating these lines through a range of “fits” that was tacitly judged to reflect a reasonable deviation in the fit. (1) Fitting data by hand to nonlinear models was much more challenging to *most students* and practitioners.

To provide students with an easy-to-use and versatile set of tools for data analysis and presentation that also included the opportunity to perform regression analysis, an Add-In to Microsoft Excel, Scientific *Data Analysis* Toolkit (SDAT), was developed for the PC. (6) An install file is available in the Supporting Information. The use of Excel in first-year chemistry courses has been described (7) and has also been shown to be a powerful resource for more sophisticated methods of data analysis. (8) Because Excel is now commonly available in most instructional settings, there is little or no financial barrier to using SDAT. In addition, Excel is often available on students’ PCs, making SDAT readily accessible to them. Because some beginning and many upper-level college chemistry students may already be familiar with the Excel environment, they will likely require little instruction or training to begin using SDAT immediately. Some precollege students will, as well, have the background and skills needed to learn and use this intuitive, user-friendly learning resource.

The SDAT *User’s Guide* and Examples documents provided in the Supporting Information contain 27 illustrations of the SDAT tools including 15 examples of using the Regression Analysis. One example (Regression III in the User’s Guide) demonstrates the use of weighted regression in analyzing the exponential decay of a luminescent sample for which the uncertainties of the intensity data (photon counting) are the square root of the number of counts. The user provides the uncertainties in the y values, u y, and SDAT converts these into the weighting factors, (1/ u y). To illustrate the visual user interfaces encountered in the Regression Analysis tool, the stepwise screen shots displayed in carrying out a *weighted regression* analysis are shown in Figures 1– 5. Another example (Regression VIII) illustrates the use of correlated covariance in a propagation of errors calculation. The examples provided in the **User’s Guide** deal with chemical kinetics, thermodynamics, electrochemistry, and quantum chemistry applications.

Despite their technol. savvy, most students entering university lack the necessary computer skills to succeed in a quant. anal. course, in which they are often expected to input, analyze, and plot results of expts. without any previous formal education in Microsoft Excel or similar programs. This lack of formal education results in increased anxiety, students spending large amts. of time using the process of "trial and error" to complete the assignments, and detracts from the students’ learning of the chem. Microsoft Excel tutorials that were previously introduced have either been not specific to chem., require multiple assignments throughout the semester to acquire the necessary skills, or are designed for deprecated versions of the software. In this work, we present an argument for implementing a chem.-specific, version-agnostic spreadsheet interactive lab. exercise that uses basic, general chem. concepts to have students explore and learn the computer skills that are necessary to succeed in a quant. anal. course. Student feedback data indicate that students felt that the interactive spreadsheet lab allowed them to develop skills that they identified as necessary for success in the course as well as for their future careers.