All you have to do is substitute the values in for y to see if they are true.
0: 9 ≤ 6 - 0 = 9 ≤ 6 (FALSE)
3: 9 ≤ 6 - 3 = 9 ≤ 3 (FALSE)
-3: 9 ≤ 6 - -3 = 9 ≤ 9 (TRUE)
-1: 9 ≤ 6 - -1 = 9 ≤ 7 (FALSE)
-6: 9 ≤ 6 - -6 = 9 ≤ 12 (TRUE)
6: 9 ≤ 6 - 6 = 9 ≤ 0 (FALSE)
-4: 9 ≤ 6 - -4 = 9 ≤ 10 (TRUE)
So, the values that belong to 9 ≤ 6 - y are -3, -4, and -6.
Answer:
The regression line is not a good model because there is a pattern in the residual plot.
Step-by-step explanation:
Given is a residual plot for a data set
The residual plot shows scatter plot of x and y
The plotting of points show that there is not likely to be a linear trend of relation between the two variables. It is more likely to be parabolic or exponential.
Hence the regression line cannot be a good model as they do not approach 0.
Also there is not a pattern of linear trend.
D) The regression line is not a good model because there is a pattern in the residual plot.
Answer:
1 × 10² J
Step-by-step explanation:
Given data

2 oz × (1 kg/35.274 oz) = 0.06 kg
The work exerted by the player is equal to the kinetic energy (K) adquired by the ball.
K = 1/2 × m × v²
K = 1/2 × 0.06 kg × (57.7 m/s)²
K = 1 × 10² J
Answer:
b. strong; negative
Step-by-step explanation:
When a correlation has a value greater than 0.74 or lesser than -0.70, it is classified as a strong correlation.
A negative value for a correlation means a negative (downhill) linear correlation.
Therefore, since the correlation coefficient found by Frank Fitness is -0.74, there is a strong and negative relationship between hours of strenuous exercise and body mass.
Converting all values to decimal, we have
1/5 = 0.2
19% - 19/100 = 0.19
Therefore the snack that contains 19% has the least amount of calories from fat.