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1. Random Variables
Random Variable: A variable that is currently undefined with an attached probability.
Defining them is important to quantify outcomes.
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3. Constructing a Distrubution for D RV
0 = 0% chance of occurring
1 = 100% chance of occurring
Knowing that something will happen, all probabilities add up to 1. Mapping out the outcomes and quantifying them allows us to calculate the individual probabilities.
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6. Variance And Standard Deviation Of D RV
Variance = sum of all possible values minus the mean, squared, times the relative probability.
Ie: (0-3.4)^2 x 0.32 + (1-3.4)^2 x 0.32....
Standard deviation = square root of variance
- denoted with sigma
- represents on average how far off actual values are from the calcu...
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5. Mean/expected Value Of D RV
1. Calculate mean by taking the possible value of the variable and multiplying it by the probability of said value
2. Then add all values together and divide by total number of values, like a normal average.
The result is not exact for every situation, but acts as a good assumption.
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4. Probability Models
1. First always find relative frequencies.
2. All models must add up to 1 or 100%
3. These models can then be put in graphs or charts or otherwise presented
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2. Discrete and Continous RV
Discrete: distinct, individual value
Continous: any value in an interval
The difference between them is akin to integers vs any number.
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Basic stats to give an introduction
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