Answer:
Part A:
The cofounding variable is a factor that could cause a result on the experiment. This would be the people taking extra food.
Independent variable would be being asked to sign in or not and take free food, because independent variable is a variable that is changed or manipulated and in this case the people were split in half and the hypothesis is that the people who didn't sign in would take more food.
Operational definitions are important when conducting research because it defines all the variables in the experiment, so it can be replicated. The operational def for the dependent variable would be "Doing the right thing even though you aren't being watched"
Part B:
The data does not support the hypothesis because even the people who signed in could've taken extra food when the dean wasn't looking. The findings cannot be generalized to all students because some students could have been taught better and can differentiate between right and wrong. So the people who were not signed in could've just taken one burger and drink, and the people who were signed in might feel obligated to take more than one burger because they had signed in and didn't just come without signing in.
The study is not a naturalistic observation because the observer did not look at the people who took the food and which side took more food.
Beacuse we're suppose to work it out and see if it is right or wrong.
<span>We can use a simple kinematic equation to find the velocity of the ball.
v = v0 + at
v = 58.5 m/s + (-9.80 m/s^2) (5.97 s)
v = -0.006 m/s
The negative sign means that the ball is heading down toward the ground. The small magnitude of the velocity means that the ball has reached the maximum height and is starting to fall back down toward the ground.
The velocity of the ball is -0.006 m/s</span>
Answer:-1.83
Explanation:
1 13
-7.34
<h3><u>subtract-5.51</u></h3><h3> 1.83 </h3>
Answer:
Sorry
Explanation:
잘자, 좋은 꿈 꿔
Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.
