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42 Cards in this Set
- Front
- Back
Cross sectional Data
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Looking at a large number of subjects over one specific time period
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Time series data
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looking at one subject over various times will almost always use a sample since a full population is hard to see
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4 Ways to measure Data
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Nominal
Ordinal Interval Ratio |
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Nominal Scale
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taken a qualative value and assigning it a number. The most non-descript way to measure
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Ordinal Scale
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Uses numbers to rank things. Tacitly assumes that spaces between numbers are relatively equal but NOT ALWAYS
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Interval Scale
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Data Measured using fixed intervals. i.e. a thermometer. DOES NOT HAVE A TRUE ZERO
ex. 0 degrees does not mean a total abscence of heat. |
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Ratio Scale data
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Interval scale data with a true zero. ex. money, salary, etc.
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Frequency distribution
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A table that shows the data you are studying and divides it in to catergories
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Classes
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types of fields in a frequency distirubion (columns/categories). Classes should be mutually exclusive
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Relative Frequency
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Percentage of total values in a specific freq
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Cumulative frequency
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making each interval additive to the following interval
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Cumulative relative frequency
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making each interval additive to the following interval and then making that value a percentage
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Histogram
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Bar chart that presents the same info in the table
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Frequency Polygon
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Line chart that connects dots on an x/y plane. We use the midpoint from each category.
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Geometric mean
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nth root of the product of all n returns
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FInding the Geomtric mean
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Step 1: add 1 to each percentage value so that we can always have a positive value under the radical sign
Step 2: subtract 1 from final answer |
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Median
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value that divides sample in half. Half of observations will be above the mediam, half will be below
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Median for even number of values
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add the two middle values and divide by 2
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Mode
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observation that occurs most frequently
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Symmetric Skewness
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Mean=Median=mode
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Positive Skewness
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Skewed tot he right. Have outliers that are high in value.
Mean>Median>mode |
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Negative skewness
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Skewed to the left (long tail to the left) outliers are low in value
Mean<Median<mode |
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Kurtosis
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Referst to the narowness of the distribution, and the tails of the distribution
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Normal Kurtosis
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Has normal looking tails and a fairly rounded point
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Excess Kurtosis
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High, thin peak, very fat long tails
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Range
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Largest observation - smallest observation
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Mean Absolute Deviation
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1/sample (sum of absolute values from it's sample mean
Not used much because of absolute values |
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Population Variance
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1/n * (sum of all deviations-mean)^2
Not used so often because some units can't be squared so much i.e. squared dollars |
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Population Standard Deviation
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Square root of population variance.
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Sample Variance
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Same as population variance but instead of multiplying by 1/n, we multiply by n-1
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sample standard deviation
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square root of sample variance
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Chebyshev's Theorum
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minimum proportion within k standard deviations of the mean is 1-(1/k^2)
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Example of Chebyshev's Theorum for k=2, 3, 4
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k=2=1-(1/4)=.75 Therefore 75% of all results are within 2 standard deviations.
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coefficient of variation
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sample standard deviation / sample standard mean
Allows us to compare |
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Holding Period Return HPR
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Beginning Price-Ending Price + Dividends / beginning price
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Sharpe Ratio
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Sample mean return - risk free rate / Standard Deviation of portfolio
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What does Sharpe ratio tell us
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Tells us the expected return per unit of risk
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Example: Sharpe Ratio 1/3 vs. Sharpe Ratio 1/2. Which one to pick
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We would want to choose asset with 1/2 sharpe ratio as we get more bang for the buck
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Geometric Mean use
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used to find past compound rates of return
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Weighted or arithmetic mean
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Used to find expected future return
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Ex. Geometric Mean: stock
Period 1: 100 Period 2: 200 Period 3: 100 |
Geometric Mean = Square root of (1+1) * (1-.5) = 1. Now need to take away 1 and you have a 0% return
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Ex. Arithmetic Mean: stock
Period 1: 100 Period 2: 200 Period 3: 100 |
1+-.5 / 2 = .25. This is incorrect because it seems to imply that we made money when we didn't.
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