Create real estate property listings data by yourself:

Just want to know the steps to do the question. The settlement of data is not the concern of this question. Randomly create the data by yourself. Use Excel to do the question.

Y = listing price

X1 = interior floor space (m2 )

X2 = land size (m2 ).

X3 = number of bedrooms

X4 = number of bathrooms

X5 = age of building (often listed as Built in date. You will need subtract the given year from the present year)

X6,7,8..= binary variables for Heating Type As Heating type id categorical, you will need to create dummy variables for each heating type found in your selected neighbourhood less one. Examples of heating types are: none, fireplace, gas and electric or baseboard.

Steps 1. The necessary regression analysis assumptions are 1) linearity, 2) independence, 3) normality, and 4) equal variance. To check these assumptions, please create 6 scatter plots: Y versus each Xi for i = 1, 2, 3,4,5 and provide a comment of what characteristics each plot are related to each of the four assumption tests. Are any of your variables potentially problematic from this perspective? (Provide only a brief, point-form answer identifying why). Note: You are not expected to provide formal rigorous tests on the regression assumptions. Carry out the above scatter plots on the non-binary variables only but for the sake of this exercise, no not exclude variables that violate assumptions.

Step 2. Check for potential multicollinearity by creating a correlation matrix. Correlation stronger than 0.6 between any two X-variables indicates that they are somewhat redundant and could potentially cause problems in your analysis. R values stronger than 0.9 indicate a severe problem whereby all but one of the collinear or multicollinear variables must be excluded from further analysis. Do you have any such potential or severe problems? (Answer in three bullet points at most.) If so, proceed to next steps after excluding the redundant variables.