The best model making predictions about the data set is a. y = 2.0259x + 0.167763
How to determine the best model making predictions about the data set
From the question, we have the following parameters that can be used in our computation:
variable_1 = ([5.49, 4.25, 3.17, 1.57, 9.58, 11.72, 10.99, 8.41, 2.34, 6.09, 7.62])
variable_2 =([11.12, 8.75, 6.25, 3.21, 20.01, 23.85, 22.32, 17.23, 5.51, 12.42, 15.48])
Let x be variable_1 and y be variable_2
So, we have
x = ([5.49, 4.25, 3.17, 1.57, 9.58, 11.72, 10.99, 8.41, 2.34, 6.09, 7.62])
y = ([11.12, 8.75, 6.25, 3.21, 20.01, 23.85, 22.32, 17.23, 5.51, 12.42, 15.48])
Using a graphing tool, we have
- Sum of X = 71.23
- Sum of Y = 146.15
- Mean X = 6.4755
- Mean Y = 13.2864
- Sum of squares (SSX) = 120.7409
- Sum of products (SP) = 244.6085
The model equation is represented as
y = bx + a
Where
b = SP/SSX = 244.61/120.74 = 2.0259
a = MY - bMX = 13.29 - (2.03*6.48) = 0.167763
So, we have
y = 2.0259x + 0.167763
Hence, the best model is y = 2.0259x + 0.167763
Question
Which model is best for making predictions about this data set
variable_1 = ([5.49, 4.25, 3.17, 1.57, 9.58, 11.72, 10.99, 8.41, 2.34, 6.09, 7.62])
variable_2 =([11.12, 8.75, 6.25, 3.21, 20.01, 23.85, 22.32, 17.23, 5.51, 12.42, 15.48])
y = 2.0259x + 0.167763
y = 0.00319504x² + 1.98351x + 0.273178
y = 0.00329151x³ + 0.0689209x² + 1.60841x + 0.831659
y = 4.74127x + 1 15404