i think one which is where x=-6
Answer:
x = 8, and y = 12
Step-by-step explanation:
There are 2 variables, so you need 2 equations to form a system of equations in two variables.
The upper left triangle has all angle measures given: 100, 2x + y, 5x + y. We know that the sum of the measures of the angles of a triangle is 180.
First equation:
100 + 2x + y + 5x + y = 180
Simplify:
7x + 2y = 80 (First equation)
Now we see that the upper and lower sides are parallel, so alternate interior angles are congruent. The angles measuring 2x + y and 5x - y are alternate interior angles and are congruent.
Second equation:
2x + y = 5x - y
Simplify:
3x - 2y = 0 (Second equation)
Now we use the first equation and the second equation as a system of simultaneous equations to solve for x and y.
7x + 2y = 80
3x - 2y = 0
Solve the second equation for 2x.
3x = 2y
Now replace 2y in the first equation with 3x.
7x + 3x = 80
10x = 80
x = 8
Replace x with 8 in the second equation.
3(8) - 2x = 0
24 = 2x
x = 12
Answer: x = 8, and y = 12
Answer:
A. True
Step-by-step explanation:
Linear regression is "an analysis that assesses whether one or more predictor variables explain the dependent (criterion) variable. The regression has five key assumptions:
1) Linear relationship
: We need to check if the dependnet variable present a linear relationshipThe linearity assumption can best be tested with scatter plots in order to check if we have outliers in the data.
2) Multivariate normality
: "The linear regression analysis requires all variables to be multivariate normal". And we can check this with a histogram or a Q-Q-Plot, usually Normality can be checked with a goodness of fit test like the Kolmogorov-Smirnov test or Shapiro Wilks test.
3) No or little multicollinearity
: "Multicollinearity occurs when the independent variables are too highly correlated with each other". And we can check this with a correlation matrix, variance inflation factor (VIF's), correlation index and other statistics.
4) No auto-correlation
: The "Autocorrelation happens when the residuals are not independent from each other in the data". And usually we can test this with the Durbin-Watson test.
5) Homoscedasticity: MEans that we need constant variance for the linear model. The scatter plot is good way to check whether the data are homoscedastic. And we can interpret this condition as "that variance in the response variable is reasonably consistent across the range of an explanatory factor (otherwise known as homoscedasticity)"
So then the statement is TRUE.
Answer:
The answer is 234
Step-by-step explanation:
Answer: D, Y, X
<u>Step-by-step explanation:</u>
The (salad, sandwich) coordinates are as follows:
A: (0, 10)
B: (1, 8)
C: (2, 6)
D: (3, 4) MOST EQUAL AMOUNTS
E: (4, 2)
F: (5, 0)
X: (4, 8) OUTSIDE OF THE PRODUCTION LINE
Y: (1, 3) UNDER THE PRODUCTION LINE
