Explore Interactive Data Visualization with R Regression Techniques

DAT 640 Practical R Activity Four Interactive Data Overview: For this activity, you will be exploring visualizations and interacting with data through regression algorithms. First, you will explore potential visualization packages and identify one to apply to a data set. You can use any data set for this, such as the many built into R or those found online. Next, we will work through the three uCertify labs to build a linear regression model and plot the output as visuals. Instructions: Complete the lab activities below. Provide responses to the questions and screenshots when prompted. Please note: This assignment will be submitted and graded in Brightspace. Part 1 : Using the uCertify lab environment and RStudio, choose a visualization package of your choice, explore a data set of your choice, and produce at least two interactive plots, such as marginal, splom, and parallel coordinate plots. Provide screenshots of your results and a summary describing the details of the visual and benefits to view and analyze the underlying data. Ggvis is one data visualization package for R that lets you declaratively describe data graphics and leverages standard web browsers to publish rich interactive graphics. A good overview and tutorial examples can be found at Gg v is 0 .4 O v er v i ew . In addition to ggvis, there are several alternative visualization packages: ggplot2, lattice, and ggcorplot, to name a few (for information on these, review F i v e Wa y s to Vis u ali z e Y o u r P ai r w i s e C o mp ari s on s ). Within uCertify, choose one of these packages to complete the two interactive plots.

Part 2 : Complete the uCertify Lab 5.1.1 Plotting Data with a Regression Line , Lab 5.3.1 Measuring the Goodness of Fit of the Regression , and Lab 5.9.1 Verifying the Regression Assumptions . Take screenshots of each illustrating successful execution of the R commands.

Part 3: In 2 to 3 paragraphs, in your own words, describe the benefit of linear regression to predictive analytics, when linear regression should be used, and what assumptions must be met. Linear Regression is used in predictive analytics. Predictive analytics are used in various industries to manage business problems. Linear regression is useful as it allows analysts to model the relationship between independent input variables and dependent output variables.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

The goal is to find the best-fit line between the two variables. This assists in finding predictions more easily on how trends will move in the future. Linear regression is crucial for analytics to understand to best understand the future of the data they are looking at. Such as if something would be financially responsible to invest in or not. Linear regression can be learned quickly and come up with outcomes quickly with even small data, although large amounts of data lowers the error rate. What is linear regression? . IBM. (n.d.). https://www.ibm.com/topics/linear-regression

DAT 640 Practical R Activity Four

Related Documents