Answer:
Solution-
We know that,
Residual value = Given value - Predicted value
The table for residual values is shown below,
Plotting a graph, by taking the residual values on ordinate and values of given x on abscissa, a random pattern is obtained where the points are evenly distributed about x-axis.
We know that,
If the points in a residual plot are randomly dispersed around the horizontal or x-axis, a linear regression model is appropriate for the data. Otherwise, a non-linear model is more appropriate.
As, in this case the points are distributed randomly around x-axis, so the residual plot show that the line of regression is best fit for the data set.
Hope this helps!
Step-by-step explanation:
Answer:
The y intercept is positive 1 because that's where the graph crosses the y axis.
Answer:
its a
Step-by-step explanation:
Answer:4/15
Step-by-step explanation:
Change the whole number into a fraction and divide both and you get 4/15 or get photomath with math problems works perfectly
Answer:
![x=(243)log_{\frac{1}{81}}[(\frac{1}{81})-1]](https://tex.z-dn.net/?f=x%3D%28243%29log_%7B%5Cfrac%7B1%7D%7B81%7D%7D%5B%28%5Cfrac%7B1%7D%7B81%7D%29-1%5D)
Step-by-step explanation:
you have the following formula:
To solve this equation you use the following properties:
Thne, by using this propwerty in the equation (1) you obtain for x
![log_{(\frac{1}{81})}(\frac{1}{81})^{\frac{x}{243}}=log_{\frac{1}{81}}[(\frac{1}{81})-1]\\\\\frac{x}{243}=log_{\frac{1}{81}}[(\frac{1}{81})-1]\\\\x=(243)log_{\frac{1}{81}}[(\frac{1}{81})-1]](https://tex.z-dn.net/?f=log_%7B%28%5Cfrac%7B1%7D%7B81%7D%29%7D%28%5Cfrac%7B1%7D%7B81%7D%29%5E%7B%5Cfrac%7Bx%7D%7B243%7D%7D%3Dlog_%7B%5Cfrac%7B1%7D%7B81%7D%7D%5B%28%5Cfrac%7B1%7D%7B81%7D%29-1%5D%5C%5C%5C%5C%5Cfrac%7Bx%7D%7B243%7D%3Dlog_%7B%5Cfrac%7B1%7D%7B81%7D%7D%5B%28%5Cfrac%7B1%7D%7B81%7D%29-1%5D%5C%5C%5C%5Cx%3D%28243%29log_%7B%5Cfrac%7B1%7D%7B81%7D%7D%5B%28%5Cfrac%7B1%7D%7B81%7D%29-1%5D)
