We assume data and prediction as question is incomplete
Answer and Step-by-step explanation:
Least squares regression line equations are used to model the relationship that exists between two variables, dependent and independent variables. The equation has the form y=a+bx where y is the dependent variable and x is independent variable, a is a constant and is the y intercept and b is the slope of the line. This relationship is then used to predict future outcomes.
Given that data for 2004-2005 for the basketball players are :
James- 20 points
John- 30 points
Chris- 50 points
Dave-15 points
Donaldson- 32 points
Richard -40 points
We predict the scores/points for James (for example) for the following year using the equation of the regression line y=0.79x+1544
We substitute his points x=20 I'm the equation:
Y=0.79*20+1544
=1599.8
The predicted value is 1599.8
The mean, since it's the average of whatever your data is.
The ratio of red to green is 5:6 which means that for every 5 red cars, there are 6 green cars
The ratio of green to blue is 3:10 telling us that for every 3 green cars, there are 10 blue cars.
The ratio 3:10 is equivalent to 6:20 after we multiply both parts by 2. This now says that for every 6 green cars, there are 20 blue cars.
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Let's say we had 5 red cars, 6 green cars and 20 blue cars
Based on that info, we know that the ratio of red to green is 5:6
And the ratio of green to blue is 6:20 which reduces to 3:10
We don't reduce 6:20 to 3:10 however, since that would change the green count from 6 to 3. We want to keep the green count at 6.
So because there are 5 red cars, 6 green cars, and 20 blue cars in this example, and this example points to the proper ratios mentioned earlier, this means that the final answer is 5:6:20. This ratio cannot be reduced or simplified as there are no common factors (other than 1) for 5, 6, and 20.
She doesn't have 2 numbers she only has one
Answer:501
Step-by-step explanation: So basically you take 419-2 now thats 417+72=489+12=501
