This course is perfect for anyone looking to learn the basics of data science and linear regression. With no prerequisites, you will learn how to install R and RStudio, create vectors and data frames in R, plot points and lines with ggplot, access vectors from data frames, group with ggplot, plot residual lines with ggplot, fit a least squares line to a data set, and use a least squares line for prediction. In two weeks, you will have the skills to start your journey in data science.
Linear Regression Analysis and Forecasting
1.5
Swayam48 learners
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This course will teach you the steps and checks required to obtain a good model for forecasting using linear regression analysis. Learn how to use the tools of linear regression analysis to find the statistical model between input variables and output variable, and how to use this model to forecast the output. Understand the accuracy of forecasting and how to improve it. Get the skills to make informed decisions and predictions for your experiments.
General Linear Models: Background Material
2.0
Youtube0 learners
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This course covers the background material for General Linear Models. Topics include random vectors and random matrices, statistical distributions such as central and noncentral t and chi square df=1 distributions, deriving the F distribution, noncentral F distribution, idempotent matrices, independence of quadratic forms, distribution of quadratic forms, (1-a)% confidence region for a multivariate mean vector, derivative of a quadratic form with respect to a vector, projection matrices, mean, variance, and covariance of quadratic forms, a square-root matrix, inverse of a partitioned matrix, the spectral decomposition, Woodbury matrix identity and Sherman-Morrison formula, generalized inverse matrix, generalized inverse for a symmetric matrix, Gram-Schmidt orthonormalization process, and sum of perpendicular projection matrices.
Building Regression Models with Linear Algebra
3.0
Coursera0 learners
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This course teaches students how to use linear algebra to build regression models. Through hands-on practice with Python, students will learn to distinguish between different types of regression models and apply the Method of Least Squares to datasets. They will also gain the skills to identify scenarios using linear regression models.
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