slader The logarithmic equation is a nonlinear regression equation of the form ya. The accompanying data are the shoe sizes and heights​ (in inches) of men. Graphs of the regression line and the logarithmic equation are also provided. Which equation is a better model for the​ data? Explain.

Respuesta :

Answer:

The graphs and the table is missing in the question.

Step-by-step explanation:

The guidelines for interpreting correlation  co-efficient r are :

1. Strong correlation    0.7<|r|≤1

2. Moderate correlation   0.4<|r|<0.7

3. Weak correlation   0.2<|r|<0.4

4. No correlation  0≤|r|<0.2

Logarithmic regression

(i). Mean : [tex]$ {\overset{-}{ln}x} = \frac{\sum ln x_i}{n}, \ \ \ {\overset{-}y} = \frac{\sum y_i}{n} $[/tex]

(ii) Trend line : [tex]$ y = A +B \ln x, \ \ B = \frac{S_{xy}}{S_{xx}}, \ \ A={\overset{-}y-B{\overset{-}{\ln x}}}$[/tex]

(iii). Correlation coefficient : [tex]$ r = \frac {S_{xy}}{\sqrt{S_{xx}} \sqrt{S_{yy}}} $[/tex]

[tex]$ S_{xx} = \sum (\ln x_i - {\overset{-}{\ln x}})^2 = \sum (\ln x_i)^2-n. ({\overset{-}{\ln x}})^2$[/tex]

[tex]$ S_{yy} = \sum(y_i - {\overset{-}y})^2 = \sumy_i^2 - n. {\overset{-}y^2}$[/tex]

[tex]$ S_{xy} = \sum(\ln x_i - {\overset{-}{\ln x}})(y_i-{\overset{-}y}) = \sum \ln x_i y_i - n. {\overset{-}{\ln x}}{\overset{-}y} $[/tex]

Now using the technology we can calculate

The equation of the regression curve is y = A + B(lnx)

we get A = 30.72 , B = 17.19

The equation of regression curve is [tex]$ \hat y$[/tex] = 30.72 + 17.19(lnx)