Respuesta :
Answer:
[tex]m=\frac{S_{xy}}{S_{xx}}[/tex]
Where:
[tex]S_{xy}=\sum_{i=1}^n x_i y_i -\frac{(\sum_{i=1}^n x_i)(\sum_{i=1}^n y_i)}{n}[/tex]
[tex]S_{xx}=\sum_{i=1}^n x^2_i -\frac{(\sum_{i=1}^n x_i)^2}{n}[/tex]
[tex]\bar x= \frac{\sum x_i}{n}[/tex]
[tex]\bar y= \frac{\sum y_i}{n}[/tex]
And we can find the intercept using this:
[tex]b=\bar y -m \bar x[/tex]
On this case the correct answer would be:
E. none of the above
Since the intercept has no association between the increase/decrease of the dependent variable respect to the independent variable
Step-by-step explanation:
Assuming the following options:
A. there is a positive correlation between X and Y
B. there is a negative correlation between X and Y
C. if X is increased, Y must also increase
D. if Y is increased, X must also increase
E. none of the above
If we want a model [tex] y = mx +b[/tex] where m represent the lope and b the intercept
[tex]m=\frac{S_{xy}}{S_{xx}}[/tex]
Where:
[tex]S_{xy}=\sum_{i=1}^n x_i y_i -\frac{(\sum_{i=1}^n x_i)(\sum_{i=1}^n y_i)}{n}[/tex]
[tex]S_{xx}=\sum_{i=1}^n x^2_i -\frac{(\sum_{i=1}^n x_i)^2}{n}[/tex]
[tex]\bar x= \frac{\sum x_i}{n}[/tex]
[tex]\bar y= \frac{\sum y_i}{n}[/tex]
And we can find the intercept using this:
[tex]b=\bar y -m \bar x[/tex]
On this case the correct answer would be:
E. none of the above
Since the intercept has no association between the increase/decrease of the dependent variable respect to the independent variable
