Answer: 2x -5
Step-by-step explanation:
Hi, to answer this question we have to write equations with the information given:
- <em>John has × marbles </em>
John=x
- <em>Max has twice (multiplied by 2) as many.max give gives john 5 of his marbles.
</em>
We have to multiply by 2 the number of marbles that John has (x), and subtract 5.
Max = 2x -5
Feel free to ask for more if needed or if you did not understand something.
This is a binomial experiment and you'll use the binomial probability distribution because:
- There are two choices for each birth. Either you get a girl or you get a boy. So there are two outcomes to each trial. This is where the "bi" comes from in "binomial" (bi means 2).
- Each birth is independent of any other birth. The probability of getting a girl is the same for each trial. In this case, the probability is p = 1/2 = 0.5 = 50%
- There are fixed number of trials. In this case, there are 5 births so n = 5 is the number of trials.
Since all of those conditions above are met, this means we have a binomial experiment.
Some textbooks may split up item #2 into two parts, but I chose to place them together since they are similar ideas.
Answer:
1/4 = 0.25
Step-by-step explanation:
<span>All three sides of triangle X'Y'Z' must be parallel to the corresponding sides in triangle XYZ and the corresponding angles are congruent .
The dilated triangle will be similar to the original triangle, which means all angles will be the same.
</span><span>The sides of the two triangles will also be parallel. You can test this by observing the slopes:
Line XY has a slope of 2/3, moving from -4,2 and passing through -1,4.
Line X'Y' has the same slope, moving from (-6,3) to (-3,5)
You can see that this is the case for all corresponding sides' slopes. </span>
Minimizing the sum of the squared deviations around the line is called Least square estimation.
It is given that the sum of squares is around the line.
Least squares estimations minimize the sum of squared deviations around the estimated regression function. It is between observed data, on the one hand, and their expected values on the other. This is called least squares estimation because it gives the least value for the sum of squared errors. Finding the best estimates of the coefficients is often called “fitting” the model to the data, or sometimes “learning” or “training” the model.
To learn more about regression visit: brainly.com/question/14563186
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