Data Set Information: I can provide the dataset separately.

The market historical data set of real estate valuation are collected from Sindian Dist., New Taipei City, Taiwan.

The inputs are as follows:

X1=the transaction date (for example, 2013.250=2013 March, 2013.500=2013 June, etc.)

X2=the house age (unit: year)

X3=the distance to the nearest MRT station (unit: meter)

X4=the number of convenience stores in the living circle on foot (integer)

X5=the geographic coordinate, latitude. (unit: degree)

X6=the geographic coordinate, longitude. (unit: degree)

The output is as follows:

Y= house price of unit area (10000 New Taiwan Dollar/Ping, where Ping is a local unit, 1 Ping = 3.3 meter squared)

Assignment Details

Before any advanced analysis can be done however, you must first examine the raw data in detail, thoroughly describe it, find raw data problems and fix them. Only then will it be possible conduct a meaningful analysis. As with any analytic project, the first order of business is to an EDA and then report those findings to the executive team. The stakes are high.

Uuse the Excel LINEST function to build the model.

On a separate tab, answer the following questions:

Q1: How many observations (rows) and how many variables (columns) are there in the raw data?

Q2: Produce a table of variables showing their types.

Q3: For numeric variables, produce a table of statistics including missing values, min, max, median, mean, standard deviation, skewness and kurtosis.

Q4: How many outliers are present in each numeric variable? Show the tallies in a table. Set them to missing.

Q5: Impute the missing values. Be sure to explain how you did that.

Q6 Build a model to predict house price per unit area (Y).

Q7 For which variables can we reject the null hypothesis that their coefficients equal zero?

Q8 Calculate the residuals and show their distribution on a boxplot.

Q9 Using your model, what price per unit area do you predict for the following property:

X1 = 2013

X2 = 17

x3 = 150

X4 = 7

X5 = 24.97320

X6 = 121.52421