So in the last lecture, we have performed preprocessing on our data.
We converted these three categorical variables into this four numeric orrible.
Now we can use this numeric variable along with other variables, to create a linear regression model
in exit.
Now, to create the model, you have to go to this Datatel.
Click on this, do the analysis.
And just search for recreation, just to scroll down, you will find recreation over here and click
on OK.
Now, once you click on regulation, you will get this kind of dialog box where you have to provide
your variable X rebels and some other information.
So a variable that is dependent variable is this valuation.
So just select all the records that you have in this column.
So select the top cell and hold your control Chimpsky and click on the downward arrow.
You will be able to select all the values in this food.
And once you do that, you will find a range that you have selected over.
Next, we have to select the input X range.
Now, this all are over ex, and we've already converted this categorical variables to a numerical variable,
so select all the converted rebel and all the candidates rebels that were there.
So select all this records.
And again, select all the records by holding down shift control and don't do it at all.
So you can notice that we have ignored these three categorical variables because we have already converted
them and we have also ignored this valuation column because this is our variable.
So now we have selected our X and Y range that is independent and dependent variables.
There are few other options.
Now, while selecting, you can notice that we have also selected this heteros, and whenever you select
the heteros, you have to click on this labels checkbox.
This means that you have also selected labels of your data.
The rules are also known as labels.
So now the settings are fine.
And you can click on OK to create the model and the summary report.
So this is the result of a linear regression.
So whenever you get the result of linear regression, the first thing you have to check is significant
evalu.
A small, significant F value signifies that the predictors have some kind of relationship with the
response variable.
In other words, you can see that the independent variables that you are using have some significant
relationship with the dependent variable or the target variable.
So you can see that here the value is four point four and two areas to the power, minus 264.
It is labor minus 264 is a very small number, and since the significant effort is very small, you
can see that there is a significant relationship between independent and dependent variable.
Now, once we have checked this significant value, we can look at the accuracy score of our modern.
Now, to look at the accuracy of our model, you can look at this adjusted square value here.
The value is zero point six zero five out of one.
And this is a good enough value.
Ideally, your adjusted artistic value should be more than zero point four or zero point four five.
So since we are getting zero point six.
This means that our independent variables are good enough to predict the dependent variable.
Now, using significant F and artistic value, we have seen that there is a significant relationship
between dependent and independent variables and our independent variables are good enough to predict
the dependent variable.
Now let's look at individual variables and let's find out which all independent variables have more
impact on our dependent variable.
Now, here in the bottom, you will find all the independent variables.
The first item in this list is intercept, this is the beeton not or the concern coefficient, then
we have all the variables that we have in our data.
Here, four variables, you have to look at the P values and similar to significant F value, p value
tells us how significant the relationship is between dependent and that independent variable.
And the smaller the P value, the more significant the relationship is between the dependent and independent
variable.
So we should look for P values less than zero point zero five.
Let's say let all the cells where this p value is less than zero point zero five.
So you can see that here the value is Edessa minus 17.
So obviously this is very less.
And again, we can ignore this value.
And here the value is it to go to minus one nine so we can highlight this?
This also the values are Edessa are minus 20 and it is sort of minus 39.
And this also.
So by looking at the P-value, you can see what all independent variables are important in your analysis
or in other words, have significant impact on the viability.
But you can see that the first variable we have here is the large.
Then location equal to residential city equal to Metro, the investment we are making and the average
household income.
Now, these three are the not important variables, so exercise equal to medium do not have a significant
relationship with the variable or the valuation of the store.
Similarly, the number of competitor stores and the estimated population in the vicinity are also not
that important.
And the valuation of our store.
Now, once you have identified the key independent variables, you can look at their coefficient.
These are the better values for all these horrible.
So the coefficient for a store under Scollard is.
Four thousand six hundred and fifty nine.
This means that.
If the stories large.
The valuation is going to increase by four thousand six hundred and fifty nine dollars.
Similarly, the Corporation for Investment is zero point four five.
This means that if we increase investment amount by one dollar, the valuation is going to increase
by zero point four or five dollars.
And here is the confusion for average household income, which is zero point four three one.
This means that keeping all the variable same except the household income.
If the household income in one locality is 10000 and in another localities 11000, then there will be
a difference of point four, three, one and two thousand that is 431 dollars and the valuation of two
escorts.
So with each increase in the household income, there is around point for three dollar increase and
the valuation amount.
So that's how we read the result of regression analysis, first we look at the significant value you
got, a significant value should be less than zero point zero five.
In our case, this is much smaller than zero point zero five.
So that we can say that there is a significant relationship between dependent and independent variables.
Once you have identified that there is a significant relationship, you can look at the accuracy of
your model by looking at I just artist square.
I just said artist good value should be more than zero point four.
And the work is this is zero point six.
So you can see that our model is going to predict the variables accurately.
Then after looking at these two values, we can look at individual variable level data where we can
identify the important variables by looking at their P values.
P value should be less than zero point zero five for important variables.
So just highlight all the variables with P-value, less than zero point zero five.
Once you have highlighted the variables, you can look at Coefficient to see how those variables are
going to impact your variable positive means, if you are increasing your independent variable, the
dependent variable is also going to increase negative means.
If you are going to increase your independent variable, variable is going to decrease.
So, for example, here you can see that for residential location, the coefficient is negative.
That means that between commercial and residential options.
The valuation of commercial location is more.