How to find the point estimate using regression model in R?

To find the point estimate using regression model in R, we can follow the below steps −

• First of all, create a data frame.
• Then, create the regression model.
• After that, define the value for which we want to find the point estimate and use predict function to find the estimate.

Create the data frame

Let's create a data frame as shown below −

 Live Demo

x1<-rnorm(20)
y1<-rnorm(20)
df<-data.frame(x1,y1)
df

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

      x1          y1
1 0.53233256 -0.17433578
2 0.53362706 1.73778811
3 1.21038775 -1.02142344
4 -1.50504650 0.01770948
5 -0.55570505 0.91796585
6 1.01597916 0.88380869
7 0.21911440 1.34088517
8 1.21258700 1.14469629
9 -0.98170554 -1.04790911
10 -0.67748759 -1.16909492
11 0.00801995 -0.35320938
12 -1.04972030 1.35817346
13 -1.35385333 0.87222670
14 1.09276537 0.70046753
15 0.10064662 0.27685523
16 0.12231502 -0.26659197
17 0.83791912 -0.80416436
18 1.56681559 0.43084296
19 -1.13942633 1.19649376
20 0.84196501 0.28244014

Create the regression model

Using lm function to create the regression model between x1 and y1 −

 Live Demo

x1<-rnorm(20)
y1<-rnorm(20)
df<-data.frame(x1,y1)
Model<-lm(y1~x1)
Model

Output

Call:
lm(formula = y1 ~ x1)
Coefficients:
(Intercept)    x1
0.317061    -0.008665

Find the point estimate

Using predict function to find the point estimate of y1 when x1 is 1.08 −

 Live Demo

x1<-rnorm(20)
y1<-rnorm(20)
df<-data.frame(x1,y1)
Model<-lm(y1~x1)
new_data<-data.frame(x1=0.08)
predict(Model,new_data)

Output

   1
0.3163682