Answer:
1) The initial value of the function is the y-value when the x-value is zero. The number of hours is the x-value and the percentage of battery life left is the y-value. When the number of hours, x, is 0, the percentage of remaining battery life, y, is 100 percent. So, the initial value of the function is 100. The initial value is positive because the percentage of remaining battery life cannot be negative.
2) The rate of change is the rate at which the y-value changes with respect to a change in the x-value. When off the charger, the phone loses its battery life at a constant rate of 5 percent per hour. So, the function’s rate of change is -5. The rate of change is negative because the rate indicates that the percentage of remaining battery life decreases as the number of hours increases.
Step-by-step explanation: I just did the tutorial right now i hope this helps
Answer:
1/10 is the correct answer
Step-by-step explanation:
3/5=6/10
7/10-6/10=1/10
Answer:
(-1,-4)
Step-by-step explanation:
Because it is a rotation that turns both the x and y values the two swap places. The x value become negative because before it was in quadrant 4 where there was still a positive chance but rotating the point clockwise puts it into the third quadrant where there are only negatives.
Answer:
A. R2 = 0.6724, meaning 67.24% of the total variation in test scores can be explained by the least‑squares regression line.
Step-by-step explanation:
John is predicting test scores of students on the basis of their home work averages and he get the following regression equation
y=0.2 x +82.
Here, dependent variable y is the test scores and independent variable x is home averages because test scores are predicted on the basis of home work averages.
The coefficient of determination R² indicates the explained variability of dependent variable due to its linear relationship with independent variable.
We are given that correlation coefficient r= 0.82.
coefficient of determination R²=0.82²=0.6724 or 67.24%.
Thus, we can say that 67.24% of total variability in test scores is explained by its linear relationship with homework averages.
Also, we can say that, R2 = 0.6724, meaning 67.24% of the total variation in test scores can be explained by the least‑squares regression line.
