![]() Using Graphing Calculator to Get Line of Best Fit Applications of Integration: Area and Volume.Exponential and Logarithmic Integration.Riemann Sums and Area by Limit Definition.Differential Equations and Slope Fields.Antiderivatives and Indefinite Integration, including Trig.Derivatives and Integrals of Inverse Trig Functions.Exponential and Logarithmic Differentiation.Differentials, Linear Approximation, Error Propagation.Curve Sketching, Rolle’s Theorem, Mean Value Theorem.Implicit Differentiation and Related Rates.Equation of the Tangent Line, Rates of Change.Differential Calculus Quick Study Guide.Polar Coordinates, Equations, and Graphs.Law of Sines and Cosines, and Areas of Triangles.Linear, Angular Speeds, Area of Sectors, Length of Arcs.Conics: Part 2: Ellipses and Hyperbolas.Graphing and Finding Roots of Polynomial Functions.Graphing Rational Functions, including Asymptotes.Rational Functions, Equations, and Inequalities.Solving Systems using Reduced Row Echelon Form.The Matrix and Solving Systems with Matrices.Advanced Functions: Compositions, Even/Odd, Extrema.Solving Radical Equations and Inequalities.Solving Absolute Value Equations and Inequalities.Imaginary (Non-Real) and Complex Numbers.Solving Quadratics, Factoring, Completing Square.Introduction to Multiplying Polynomials.Scatter Plots, Correlation, and Regression.Algebraic Functions, including Domain and Range.Systems of Linear Equations and Word Problems.Introduction to the Graphing Display Calculator (GDC).Direct, Inverse, Joint and Combined Variation.Coordinate System, Graphing Lines, Inequalities.Types of Numbers and Algebraic Properties.Powers, Exponents, Radicals, Scientific Notation.The correlation simulation uses the rmvnorm function in the mvtnorm package in R. The user can explore how the dispersion of the Yhat values depends on the size of the pearson product-moment correlation.Ī “play” button on the correlation slider permits dynamic visualization of how the characteristics of the system change when Rho is changed.īuilt using Shiny by Rstudio and R, the Statistical Programming Language. csv file.įor both approaches, the scatterplots emphasize examination of the “rug plots” of both the raw Y values and the Yhat values. If the user wants to see the same kind of scatterplot with their own data, the data upload approach permits this with upload of a. The randomly drawn sample results are displayed in the scatterplot along with the sample pearson product-moment correlation. The simulation approach in this application simulates samples drawn from a bivariate normal distribution, where the means, sd's, rho, and n are specified by the user. Tools for Statistics Instruction using R and ShinyĪuthor: Bruce Dudek. Open the csv file in a text editor and it should look like this: The best approach would begin by creating a file in a spreadsheet such as this: csv files that include variable names as a header row. csv file without a header (and indicate that by unchecking the entry box on the sidebar), the variables to choose from will be listed as V1, V2, V3, etc, depending on their position in the. The first row in the csv file should contain the variable names (a "header”). The user can specify which variable to be used as the IV (X) and which is to be used as the DV (Y). The csv file must have only a small number of variables. It will work best if the number of rows (cases, or sample size) is less than 100. The application will only accept a “comma separated text file (.csv). It is very important to follow the instructions here. It needs to have at least two columns that represent the IV and DV, respectively. csv file that contains data to be displayed. This app will permit the user to upload a. Uploading Data for the Bivariate Plotting Application ![]()
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