Answer:
A table that has (0,0), (-1,1), (-4, 2) and undefined for any positive x value
Step-by-step explanation:
Reflecting across the y axis just changes the x values, it makes them negative. so 
has points (0,0), (1,1), (4, 2) and so on. reflecting over the y axis makes them(0,0), (-1,1), (-4, 2) and again so on.
Also good to mention in negative x values are undefined, so flipped over the y axis positive x values are undefined.
As for the answer it doesn't look like any of those shown. The first one is close, but the x values would need to swap their signs.
Answer:
n = 4
Step-by-step explanation:
3 = 2 + n/4
1 = n/4
4 = n
Answer:
1) 
, 2) Seis pares de zapatos (Six pairs of shoes), 3) 
Step-by-step explanation:
1) La cantidad de arroz no utilizada por Juanita es (The quantity of rice that was not used by Juanita is):
2) La cantidad de docenas sin vender es (The quantity of dozens of shoes that are not sold is):
Hay seis pares de zapatos (There are six pairs of shoes).
3) El peso total de las frutas son (The total weight of fruits are):
}
Answer:
d
Step-by-step explanation:
Answer:
a. eliminating variables for a multiple regression model
Step-by-step explanation:
Variance inflation factor (VIF) could be described as a measure to the amount of multi-collinearity in a set of multiple regression variables. In mathematics, the variance inflation factor for a regression model variable will be equal to the ratio of the overall model variance to the variance of a model that includes only that single independent variable. Accordingly, this ratio is evaluated for every independent variable and the high factor shows the associated independent variable which is highly collinear to the other variables which can be included in the model.
A multiple regression can be utilized in the case of when there is a need to test the effect of multiple variables on a particular result. As a result, the dependent variable is the outcome which is being acted upon by the independent variables, that are the inputs into the model. Multi-collinearity appears in the case of when either there is a linear relationship, or correlation, between one or more of the independent variables or inputs and it makes the establishment of a problem in the multiple regression due to since the inputs are all influencing each other, they are not actually independent.
