In this video, we are going to run a simple linear regression, that is we will use only one predictor
variable to predict the value of price, the predictor variable.
We are going to use is room number, that is number of rooms in the house and using the value of room
number.
We will try to establish a relationship between room number and price.
We did this earlier also where we created this trend line.
And so the equation and the R-squared against this trend line.
Now we will run the proper regression analysis and see its results.
So we will go to data analysis options here.
We will select the regression option and click on talk.
The input lighting is the response variable, which is in the column to select this whole column.
The input X range is our room number column.
Our data has labeled that as the first rule of the column contains the variable name.
Next option is where we want our output.
We want an output and a new sheet.
And we do not need to look at these attitudes for not.
So we'll just click on OK?
And we get the result of linear regression between these two variables and this new shit, you can see
that the Oscar value is coming out two point forty for it.
This is the same as.
The one we got when we plotted this train line.
The values is in this column, so this is Bedazzle against InterCivic, and this particular cell is
containing beta one value, which is the coalition of room no evil.
So a value of nine point zero nine is telling us that if I increase the room number, they will buy
one unit, it will increase the house price by nine point zero nine units.
So using these to be the values we can estimate, the equation between Price and Ruman will understand
the meanings of all these other parameters in the coming reduce.