Answer:
Increase in the concentration of the reactants (vinegar and baking soda) leads to an increase in the rate of reaction (more volume of CO2 is evolved within a shorter time)
Explanation:
The chemical reaction between baking soda and vinegar in water is shown in the chemical reaction equation below;
NaHCO3(aq) + CH3COOH(aq) ----->CO2(g) + H2O(l) + CH3COONa(aq)
The chemical name of baking soda is sodium bicarbonate (NaHCO3) while vineager is a dilute acetic acid (CH3COOH) solution. This reaction provides a very easy set up in which we can study the effect of concentration on the rate of chemical reaction.
We must have it behind our minds that increase in the concentration of reactant species increases the rate of chemical reaction. Secondly, the rate of the reaction between baking soda and vinegar can be monitored by observing the volume of CO2 evolved and how quickly it evolves from the reaction mixture.
We can now postulate a hypothesis which states that; 'increase in the concentration of the reactants (vinegar and baking soda) leads to an increase in the rate of reaction (more volume of CO2 is evolved within a shorter time).'
If we go ahead to subject this hypothesis to experimental test, it will be confirmed to be true because a greater volume of CO2 will be evolved within a shorter time as the concentration of the reactants increases.
More the number of turns, more will be the magnetic field produced.
Hence wire A will have magnetic field greater than wire B.
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The correct option is C. The amount of MgCl2. we know this because <span>no matter how much you increase KOH, if you dont increase Mgcl2, the amount of Mg(OH)2 remains the same. Hope this works for you</span>
Correct option:
Entropy is used to calculate information gain.
What is entropy?
- Entropy is the measure of data's uncertainty or randomness; the greater the randomness, the higher the entropy. Entropy is used by information gain to make choices. Information will increase if the entropy decreases.
- Decision trees and random forests leverage information gained to determine the appropriate split. Therefore, the split will be better and the entropy will lower the more information gained.
- Information gain is calculated by comparing the entropy of a dataset before and after a split.
- Entropy is a way to quantify data uncertainty. The goal is to maximize information gain while minimizing entropy. The method prioritizes the feature with the most information, which is utilized to train the model.
- Entropy is actually used when you use information gain.
Learn more about entropy here,
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They have different number of Neutrons and protons, so their masses are different
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