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Ivahew [28]
2 years ago
8

Analyze how water affects mass movements by using two examples of mass movement.-

Chemistry
1 answer:
Natali [406]2 years ago
6 0

Answer:

Types Descriptions

Flows occur when soil or rock acts like a liquid

Slumps a slice of material that moves as one piece along a curved surface

Landslides made up of unconsolidated rock; includes rockslides and avalanches

Creeps material that moves slowly down gently sloping areas

Explanation:

Googled it.

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What does a circle filled in with a dark color represent in a pedigree chart?
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A circle or square that is not shaded indicates that a person neither expresses the trait nor is a carrier of the trait. within a family, is used. In a pedigree, a circle represents a female, and a square represents a male. A filled-in circle or square shows that the individual has the trait being studied.

7 0
3 years ago
You must make 1 L of 0.2 M acetic acid (CH3COOH). All you have available is concentrated glacial acetic acid (assay value, 98%;
Sergeu [11.5K]

Answer:

The correct answer is "11.44 ml".

Explanation:

Molarity,

= 0.2 M

Density,

= 1.05 g/ml

Volume,

= 1 L

As we know,

⇒  Molarity=\frac{No. \ of \ moles }{Volume \ of \ solution}

or,

⇒  No. \ of \ moles=Molarity\times Volume

On putting the values, we get

⇒                         =0.2\times 1

⇒                         =0.2 \ moles

Now,

⇒  No. \ of \ moles=\frac{Mass \ taken}{Molecular \ mass}

or,

⇒  Mass \ taken=No. \ of \ moles\times Molecular \ mass

⇒                      =0.2\times 60.05

⇒                      =12.01 \ gram

hence,

⇒  Density= \frac{Mass }{Volume}

or,

⇒  Volume=\frac{Mass}{Density}

⇒                =\frac{12.01}{1.05}

⇒                =11.44 \ ml

7 0
3 years ago
The stock concentration of NaOH is 0.5M. 0.5M = 0.500mol/1.0L.
valentinak56 [21]

Answer:a) 0.1 mole. b) 4g. c) 2% d)  196 mL

Explanation: in 200mL , 0.1mole

mw NaOH = 40g/mol —> 4g in 0.1 mole

4g in 200mL so 2g in 100mL

density NaOH = 1g/mL so if 4g in 200 mL,  4mL , 196 mL water

5 0
2 years ago
What are the two types of numbers in experimental calculations?
FinnZ [79.3K]
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Gain familiarity with the experimental material,

Ensure that treatments are not obviously excessively mild or severe

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Exploratory experiments can be used to generate data with which to develop hypotheses for future testing. They may “work” or “not work”. They may have no clearly stated hypothesis (“let’s see what happens if..” is not a valid hypothesis on which to base an experiment).

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Depending on the nature of the data, statistical analysis will often be done using an analysis of variance (ANOVA)

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The basic principles are:

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Their aim is to test a “null hypothesis” that there is no difference among the groups for the specified outcome.

If the null hypothesis is rejected at a certain level of probability (often 5%) this means that the probability of getting a result as extreme as this or more extreme in the absence of a true effect is 5% (assuming also that the experiment has been properly conducted). So it is assumed that such a difference is likely to be the result of the treatment. But, it could be a false positive resulting from sampling variation.

Failure to reject the null hypothesis does not mean that the treatment has no effect, only that if there is a real effect this experiment failed to detect it. “Absence of evidence is not evidence of absence”.

Experimental subjects need to be independently replicated because individuals (of whatever type) vary. Two subjects can normally be regarded as being independent if they can theoretically receive different treatments.

Subjects need to be assigned to groups, held in the animal house and measured at random in order to minimise the chance of bias (a systematic difference between groups)

As far as possible the experimenter should be “blind” with respect to the treatment group in order to minimise bias.

The experiments need to be powerful, i.e. they should have a high probability of detecting an effect of clinical or scientific importance if it is present.

In many cases a formal experimental designsuch as a “completely randomised”, “randomised block”, “Latin square” etc. design will be used.

In most cases it is useful if the experiment has a wide range of applicability. In other words the results should hold true under a range of different conditions (different strains, both sexes, different diets, different environments etc.). At least some of these factors should be explored using factorial and randomised block designs.

Experiments to explore relationships between variables. A typical example would be a growth curve or a dose-response relationship. In these experiments the aim is often to test whether the two variables are associated, and if so, what is the nature of that relationship. The typical statistical analysis involves correlation and/or regression.

 


8 0
3 years ago
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