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So here is the data that we are going to use in this course.

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The problem statement here is that you are a manager in a real estate company and you want to find out

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the true value of a property that is based on the pricing of past property transactions, you want to

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get the price of the property you want to sell.

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So price of a property will be the dependent variable in our analysis and other factors which impact

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the price, which we identified with primary and secondary research are the independent variables.

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This dataset has values for all these variables whenever we collect data.

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Different parts of data come from different sources.

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And you need to collect this data into a single tabular format such as this.

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In this table, the room tells us the variable names.

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While keeping variable names, try not to use space, instead use an underscore, for example, in crime

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rate, we have used crime underscore rate.

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This is because some softwares do not accept spaces and variable names.

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Secondly, try to keep names such that you can recognize the actual variable that is a to put names

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like X1 Extra X3.

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Since post analysis, the results from softwares will also be showing these variable names and then

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it will become difficult to make sense of the result.

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I will show you in a couple of minutes what each variable stands for, but even by just looking at the

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names, you get some idea of what this data is.

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So this data is about price of houses and other related variables to each house.

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We will look at these variables in detail.

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And sometime after this top hit a row, each row contains values pertaining to one observation and we

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have a total of 506 observations.

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The total number of columns we have is 19.

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We may have received different parts of this data from different sources, probably one source gave

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us data of home address and its price.

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One source gave us the tough crime rates in different neighborhoods in such a case, we need to use

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a unique identifier which will help us match these two parts.

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So join all these different parts of data and get it into one single dataset with rows and columns,

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with columns, contain each variable and contain each observation.

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Now, your dataset is ready, but whenever you are going to share your data or your analysis with anyone,

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the data should always be accompanied by data dictionary.

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A data dictionary contains information about the variables in the dataset.

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So comprehensive data dictionary includes the following.

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It has definitions of all the variables of your dataset.

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Then it should tell about the unique identifier for each observation.

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This unique identifier is called the primary key.

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To match data from another table, if there is a matching key used, that should also be highlighted.

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If you want to learn more about primary and secondary keys, you can look at our video here.

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We have shared Ehlinger.

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This video is part of our Anadarko's on database management using Escuela.

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Lastly, if there is a categorical variable in the data.

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All its values should also be explained.

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Now, let's define the variables of our host data dataset.

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On the left is the variable name, as in the data set on the right is the brief description of what

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that variable is.

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I'm going to read this one by one, No.

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So Price tells us the value of the house crime rate, as does the crime rate in that neighborhood research

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area, stands for the proportion of residential area in the town.

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Air quality gives us the index of quality of air in that neighborhood.

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Room number is the average number of rooms in houses of that locality.

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Ages, how old is that house construction in years, how many years ago it was constructed?

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This one, this two, the three, just four are four distances of that house from the nearest employment

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hub.

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Because we hypothesize that.

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The price of house will depend on the employment opportunity available Nereid teachers stands for the

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number of teachers 1000 population in that town.

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Then we have a bigger problem.

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This is the proportion of world population in town airport stands for, is there any airport in the

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city or not?

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So this is a categorical variable.

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It has two values.

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Yes and no.

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These are self-explanatory.

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That just means that there is an airport in the city.

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No means there is no airport in the city.

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And Hotbed is giving us number of hospital beds.

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But the population in the town and heart rooms is giving us number of hotel rooms.

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But our population in the town waterboardings, another categorical variable.

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It has values like Lake River Boat or none, which is giving us whether there is a natural freshwater

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source within the city or not.

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Rainfall is giving us the average rainfall in centimeters.

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Buster is a categorical variable, which is telling us whether there is a bus terminal in the city.

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Yes, means there is no means there is no bus terminal in the city.

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And Parks Variable is giving us the proportion of land assigned as green land in that town.

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So with The Cafferty File and the PDF of the attacks in the resource section of this lecture.

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You have with you the data we will be using for our analysis.

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Note that this data is not real data, and it's similar to the Boston housing data set commonly used

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for analysis.

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But do not take the results of our analysis too seriously.

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This is not actually the.