Answer:
Explanation:
There are 3 main forces at work here, gravity, normal and friction. The gravity pulls the car straight down and is what keeps the car on the ground. Normal force is straight up from the points where the car is touching, so since the wheels are the only parts of the car touching the street, this is where all the normal force is. Friction force opposes any and all motion, the car wants to slide down the hill and would slide down the hill if there was no friction, so the friction force is in the opposite direction of the cars intended motion.
Answer:
good question, did you press any certain buttons?
Explanation:
The energy is involved in ionization, electron affinity etc
The following are the conditions that favor the creation of covalent bonds since they are generated by the mutual sharing of electrons:
- Electron affinity, first. If both atoms have a strong electron affinity, a covalent link is often preferred between them.
- The energy of ionization. The ionization energy of the two atoms participating in the bonding process should be high.
- Atomic Dimension. The atoms that form covalent bonds should have lower atomic sizes. Stronger covalent bonds are created when atoms have smaller atomic radii.
- Electronegativity. Both atoms' electronegativities ought to be high. The two atoms' electronegativities should differ as little as possible.
- In order to establish covalent bonds, the atoms' high ionization energy is involved.
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When conducting a hypothesis test there are two possible decisions that you can make: reject the null hypothesis or fail to reject the null hypothesis.
A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters.
When conducting a hypothesis test there are two possible decisions: reject the null hypothesis or fail to reject the null hypothesis. You should remember though, hypothesis testing uses data from a sample to make an inference about a population. When conducting a hypothesis test we do not know the population parameters. In most cases, we don't know if our inference is correct or incorrect.
When we reject the null hypothesis there are two possibilities. There could really be a difference in the population, in which case we made a correct decision. Or, it is possible that there is not a difference in the population (i.e., is true) but our sample was different from the hypothesized value due to random sampling variation. In that case we made an error. This is known as a Type I error.
When we fail to reject the null hypothesis there are also two possibilities. If the null hypothesis is really true, and there is not a difference in the population, then we made the correct decision. If there is a difference in the population, and we failed to reject it, then we made a Type II error.
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