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ANCOVA and Linear Models: Solution with Interpretation
Use ANCOVA and Linear models to solve example assignment.
Beginner Friendly Introduction to Statistics in R Programming
Example of beginner level university level assignment of R Studio statistics with solution.
Model Prices Using Classificaton Trees and Regression in R
Use R Studio to create decision trees with pruning for building model to predict used car prices.
Compare R Studio ggplot2 Graphs: qplot() vs ggplot()
R Studio ggplot2 library contains functions qplot() and ggplot(). What is the result of these functions? Example solution explains the differences.
Discrete Logistic Model: Maps, Bifurcations, Oscillations, Convergence
Use R studio to analyze a logistic model - bottom up loop development and analysis.
Top Unisex Baby Names of Last Century: R Studio Visualization
In this post, we combine data-wrangling and visualization skills to create a professional-looking data visualization using the ggplot2 package in R. Most popular unisex baby names are presented nicely in an R graph.
Linear Regression in Matrix Form - R Studio
The following exercises aim to compare simple linear regression results computed in matrix form with the built in R function lm().
Linear Regression Analysis of College Tuition in R
The following exercises aim to use linear regression tools in R to investigate whether a linear relationship exists between tuition and other variables.
Dataset "311 Service Requests" Analysis Using tidyverse R Library
It is a popular dataset used to analyze service requests of various cities (Pitsburg, New York, etc.). This time we analyze Roslindale city in R.
Multiple Linear Regression Exercises and Solutions
Solutions to Multiple Linear Regression Exercises in R Studio
Linear Regression Example: Education Expenditure to Predict SAT
Example solution of analysis how SAT scores are dependent on Education expenditure. Model is built using linear regression. It explains 82% of variance in SAT scores.
Data Preprocessing in R Studio: Example Solutions
Data preprocessing is the first step in a data modeling assignment. Learn the best practices following the solution to an example assignment. Topics covered: reading file, dealing with missing values, various plots, analyzing relations and distributions of the data.