They all involve descriptive statistics.
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:
B 6,17
Step-by-step explanation: If you plug in the value of x from each of the choices only b gives the correct value of y. If you put 6 in the place of x in y= 3x-1 then y= 18-1 which is 17, and it does say 17 in 6,17.
The vertex form of the function gives the vertex as (-6,48). The vertex of f(x)=x^2 is (0,0) so from this information, the vertex is moved LEFT 6 and UP 48. This cancels out two options. The coefficient -3 tells us that the graph is flipped or reflected over the x-axis (negative sign flips graph) and that all y-values will be 3 times as large. Larger y-values for the same x inputs makes the graph narrower.