Housing Multiple Linear Regression Model Using R Project

CSUGC Housing Multiple Linear Regression Model Using R Project

Build a Housing Multiple Linear Regression Model Using R

The Multiple Linear Regression model (MLR) captures the relationship between multiple attributes of a data set (called predictors) and an attribute of the same data set (called target). Using an MLP model, we can then predict the unknown value of the target attribute given the known values of its predictors.

In this assignment, you will use R Studio and R to build an MLR for a housing data set that is available at the University of California Irvine (UCI) Machine Learning repository.

To prepare for this Critical Thinking Assignment:

1. Review the module’s interactive lecture and its references.
2. Download and install R. Detailed instructions are provided in the document: R-Installation-Instructions Download R-Installation-Instructions. Note that R must be installed to use RStudio.
3. Download and install R Studio. Detailed instructions are provided in the document RStudio-Installation-Instructions Download RStudio-Installation-Instructions. Note that R must be installed to use RStudio.
4. Download the HousingDataset (it’s normally placed in the assignment as a link). The file contains the dataset on one tab and the attributes on another.
5. Convert the “housing.data” into a CSV file called “housing.csv” using these steps:
   • Open the “housing.data” in Microsoft Excel
   • When the Text Import Wizard dialog appears, configure it as follows:
     • select Fixed width for file type, click Next
     • use the default field (column) width suggested by Excel, click Next
     • select General for column data format, click Finish
   • Save the file as CSV file called “housing.csv”

To complete this Critical Thinking Assignment:

Complete these steps:

Step 1:

• Read the “housing.csv” file and assign it to data frame called “housing”. Hint: use read.csv() function with header = FALSE.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show the actions you took and the results you obtained. The screenshot should have the current date and time.

Step 2:

• Display summary statistics for all attributes (columns) of the housing data set.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show the actions you took and the results you obtained. The screenshot should have the current date and time.

Step 3:

• Display the correlation numbers among the attributes of the housing data set at the column positions of 1, 6, 11, and 14.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show the actions you took and the results you obtained. The screenshot should have the current date and time.

Step 4:

• Display a scatter matrix of the attributes of the housing data set at the column positions of 1, 6, 11, and 14.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show the actions you took and the results you obtained. The screenshot should have the current date and time.

Step 5:

• Build an MLR model using the attribute of the housing data set at position 14 as the target attribute and the attributes at positions 1, 6, and 11 as the predictor.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show the actions you took and the results you obtained. The screenshot should have the current date and time.

Step 6:

• Display a summary of the performance of your MLR model.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show the actions you took and the results you obtained. The screenshot should have the current date and time.

Step 7:

Summarize your work on this assignment as follows:

• Explain how you completed this assignment and how you resolved the issues you faced, if any. Use of first-person style is acceptable (200 words). Explanation of your work should be relevant and detailed.
• Interpret the results you obtained from your work. Use of first-person style is acceptable (200 words). Interpretation of results should be relevant and accurate.
• Reflect on your experience with this assignment and the lessons you learned. Use of first-person style is acceptable (200 words). Reflection on experience should be relevant and personalized.