So here is the data that we are going to use in this course.
The problem statement here is that you are a manager in a real estate company and you want to find out
the true value of a property that is based on the pricing of past property transactions, you want to
get the price of the property you want to sell.
So price of a property will be the dependent variable in our analysis and other factors which impact
the price, which we identified with primary and secondary research are the independent variables.
This dataset has values for all these variables whenever we collect data.
Different parts of data come from different sources.
And you need to collect this data into a single tabular format such as this.
In this table, the room tells us the variable names.
While keeping variable names, try not to use space, instead use an underscore, for example, in crime
rate, we have used crime underscore rate.
This is because some softwares do not accept spaces and variable names.
Secondly, try to keep names such that you can recognize the actual variable that is a to put names
like X1 Extra X3.
Since post analysis, the results from softwares will also be showing these variable names and then
it will become difficult to make sense of the result.
I will show you in a couple of minutes what each variable stands for, but even by just looking at the
names, you get some idea of what this data is.
So this data is about price of houses and other related variables to each house.
We will look at these variables in detail.
And sometime after this top hit a row, each row contains values pertaining to one observation and we
have a total of 506 observations.
The total number of columns we have is 19.
We may have received different parts of this data from different sources, probably one source gave
us data of home address and its price.
One source gave us the tough crime rates in different neighborhoods in such a case, we need to use
a unique identifier which will help us match these two parts.
So join all these different parts of data and get it into one single dataset with rows and columns,
with columns, contain each variable and contain each observation.
Now, your dataset is ready, but whenever you are going to share your data or your analysis with anyone,
the data should always be accompanied by data dictionary.
A data dictionary contains information about the variables in the dataset.
So comprehensive data dictionary includes the following.
It has definitions of all the variables of your dataset.
Then it should tell about the unique identifier for each observation.
This unique identifier is called the primary key.
To match data from another table, if there is a matching key used, that should also be highlighted.
If you want to learn more about primary and secondary keys, you can look at our video here.
We have shared Ehlinger.
This video is part of our Anadarko's on database management using Escuela.
Lastly, if there is a categorical variable in the data.
All its values should also be explained.
Now, let's define the variables of our host data dataset.
On the left is the variable name, as in the data set on the right is the brief description of what
that variable is.
I'm going to read this one by one, No.
So Price tells us the value of the house crime rate, as does the crime rate in that neighborhood research
area, stands for the proportion of residential area in the town.
Air quality gives us the index of quality of air in that neighborhood.
Room number is the average number of rooms in houses of that locality.
Ages, how old is that house construction in years, how many years ago it was constructed?
This one, this two, the three, just four are four distances of that house from the nearest employment
hub.
Because we hypothesize that.
The price of house will depend on the employment opportunity available Nereid teachers stands for the
number of teachers 1000 population in that town.
Then we have a bigger problem.
This is the proportion of world population in town airport stands for, is there any airport in the
city or not?
So this is a categorical variable.
It has two values.
Yes and no.
These are self-explanatory.
That just means that there is an airport in the city.
No means there is no airport in the city.
And Hotbed is giving us number of hospital beds.
But the population in the town and heart rooms is giving us number of hotel rooms.
But our population in the town waterboardings, another categorical variable.
It has values like Lake River Boat or none, which is giving us whether there is a natural freshwater
source within the city or not.
Rainfall is giving us the average rainfall in centimeters.
Buster is a categorical variable, which is telling us whether there is a bus terminal in the city.
Yes, means there is no means there is no bus terminal in the city.
And Parks Variable is giving us the proportion of land assigned as green land in that town.
So with The Cafferty File and the PDF of the attacks in the resource section of this lecture.
You have with you the data we will be using for our analysis.
Note that this data is not real data, and it's similar to the Boston housing data set commonly used
for analysis.
But do not take the results of our analysis too seriously.
This is not actually the.