- if A and B ARE mutually exclusive then the events cannot occur at the same time
- Then there is no intersection
- Since they cannot both occur so in that case it's just P(A) + P(B)
**For**NON mutually exclusive events they can both occur at the same time**s**o we have an intersection of the events A and B, so we still have P(A) + P(B) but now see if we add A and B we are also adding the intersected part, so really adding a part of A and a Part of B twice so to just get A or B we have to remove that part, so subtract off P(A and B)

# Math Easy As π

Math Tips, Tricks and More

## Friday, March 31, 2017

## Monday, March 20, 2017

### hypothesis test for proportions, things to remember

Here's a few things to remember

Z scores for proportion

(p^ - p)/square root(p*(1-p)/n)

Z for difference of two proportion

(p1^ - p2^)/square root(p-bar(1-p-bar)/n1 + (p-bar(1-p-bar)/n2))

p-bar = (x1 +x2)/(n1 + n2)

Note that p-bar might also be noted at p-pooled

For hypotheses, remember that Ho always contains = and Ha contains <, > or "does not equal"

Confidence intervals for single proportion

p^ +/- Z*square root(p^(1-p^)/n)

For difference of two propotions

p1^ - p2^ +/- Z*square root(p1^(1-p1^)/n1 + p2^(1-p2^)/n2)

Z values for confidence intervals

90% = 1.645

95% = 1.96

98% = 2.33

99% = 2.575

You can also get these from Z chart

P-values are the value from the Z chart for corresponding Z score if Ha contains < and 1- value from the Z chart for corresponding Z score if Ha contains >. If Ha is "does not equal" you take 1 - value from the Z chart for corresponding Z score then multiply the result by 2.

You can also get p-values from Z scores using the link below.

http://www.socscistatistics.com/pvalues/normaldistribution.aspx

Z scores for proportion

(p^ - p)/square root(p*(1-p)/n)

Z for difference of two proportion

(p1^ - p2^)/square root(p-bar(1-p-bar)/n1 + (p-bar(1-p-bar)/n2))

p-bar = (x1 +x2)/(n1 + n2)

Note that p-bar might also be noted at p-pooled

For hypotheses, remember that Ho always contains = and Ha contains <, > or "does not equal"

Confidence intervals for single proportion

p^ +/- Z*square root(p^(1-p^)/n)

For difference of two propotions

p1^ - p2^ +/- Z*square root(p1^(1-p1^)/n1 + p2^(1-p2^)/n2)

Z values for confidence intervals

90% = 1.645

95% = 1.96

98% = 2.33

99% = 2.575

You can also get these from Z chart

P-values are the value from the Z chart for corresponding Z score if Ha contains < and 1- value from the Z chart for corresponding Z score if Ha contains >. If Ha is "does not equal" you take 1 - value from the Z chart for corresponding Z score then multiply the result by 2.

You can also get p-values from Z scores using the link below.

http://www.socscistatistics.com/pvalues/normaldistribution.aspx

## Monday, March 6, 2017

### Finding half life

Solving for the half life is easy.

Suppose A(t) = Ao*e^(-4t)

To find the half life, let A(t) = (1/2)Ao

(1/2)Ao = Ao*e^(-4t)

1/2 = e^(-4t)

ln (1/2) = ln(e^(-4t))

ln (1/2) = -4t

t = (-1/4)ln(1/2)

Suppose A(t) = Ao*e^(-4t)

To find the half life, let A(t) = (1/2)Ao

(1/2)Ao = Ao*e^(-4t)

1/2 = e^(-4t)

ln (1/2) = ln(e^(-4t))

ln (1/2) = -4t

t = (-1/4)ln(1/2)

## Monday, February 27, 2017

### Bayes Theorem

Bayes Theorem

P(B/A)=P(A and B)/P(A) but from Bayes Theorem we have

P(B/A) = P(A/B)*P(B)/P(A)

P(B/A) = P(A/B)*P(B)/P(A)

in our problem let A = identify correctly and B = cat person

therefore A' = identify incorrectly and B' = dog person

therefore A' = identify incorrectly and B' = dog person

P(A) = P(A/B)*P(B) + P(A/B')P(B')

Note the tree diagram in the written work.

The values used and obtained are as follows

P(B) = .33

P(B') = .67

P(A/B) = .96

P(A'/B) = .04

P(A/B') = .71

P(A'/B') = .29

P(A and B) = .33(.76) = .3168

P(A' and B) = .33(.04) = .0132

P(A and B') = .67(.71) = .4757

P(A' and B') = .67(.29) = .1943

P(B') = .67

P(A/B) = .96

P(A'/B) = .04

P(A/B') = .71

P(A'/B') = .29

P(A and B) = .33(.76) = .3168

P(A' and B) = .33(.04) = .0132

P(A and B') = .67(.71) = .4757

P(A' and B') = .67(.29) = .1943

Notice that all the joint probabilities add to 1

Now put those values into the formula and you'll get P(B/A) = .3997

## Monday, February 20, 2017

### Assumptions for hypothesis test with proportions

First we must see if np >10 and n(1-p)> 10

Also the sampling method must be a simple random sample, with only two possible outcomes, p (success) and 1-p (failure).

The sample must include at least 10 successes and 10 failures and the population size is at least 20 times larger than the sample size.

## Wednesday, February 15, 2017

The sample data distribution tends to resemble the population distribution more closely than the sampling distribution. A random sample of data from a population should be representative of the population, and its distribution should be similar to the population distribution.

Suppose that we draw all possible samples of size n from a given population and then get the mean, standard deviation, proportion or other statistic for each sample. The probability distribution of this statistic is called a sample distribution.

Now suppose we take all possible samples of a certain size from a population. Once we obtain the samples, we get the mean for each sample. This is called the sampling distribution of the sample mean.

## Thursday, February 9, 2017

The price ceiling which is the highest the price can be is lower than the price equilibrium, so when the price is $5, you see the supply is 10 and the demand is 30. This means there is a higher demand than what is in supply, therefore there is a shortage of 20.

The price floor is the lowest the price can be and since it's still $5, the supply and demand is the same as in the first part. But since the price floor can be increased, there is no surplus or shortage, it can be moved to create equilibrium or surplus. Basically it isn't fixed, whereas with the price ceiling at $5, there is no way to rectify the situation of the shortage.

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