Implement statistical analysis using quantitative and qualitative variables
Apply statistical techniques to address research problems
Scenario
You are a data analyst working for a real estate company based in Seattle. You have access to a large set of historical data that you can use to analyze patterns between different attributes of a house (such as square footage and number of bathrooms) and the house’s selling price. You have been asked to create different regression models that can be used to predict a house’s selling price based on different factors. These regression models will help your company set better prices when listing a home for a client. You will use the R programming language to perform the statistical analyses and then prepare a report of your findings. Since your report will be read by different stakeholders within your real estate company, you will need to interpret your findings and describe their practical implications.
Note: This data set has been “cleaned” for the purposes of this assignment.
Reference
Harlfoxem. (2016). House Sales in King County, USA [Data file]. Retrieved from https://www.kaggle.com/harlfoxem/housesalesprediction
Directions
R Script: To complete the tasks listed below, open the Project One Jupyter Notebook link in the Assignment Information module. Your project contains the data set and a Jupyter Notebook. The Jupyter Notebook contains instructions and blank code blocks where you will write your R scripts. You will be asked to complete the following regression analyses:
First Order Regression Model with Quantitative and Qualitative Variables
Complete Second Order Multiple Regression Model with Quantitative Variables
Nested Models F-Test