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### 14 Cards in this Set

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 descriptive stats gathering of data; often expressed through polls, surveys, charts statistical inference/generalizations this involves drawing conclusions about the larger picture; predicting the future population the entire group that is being studied; sample a subgroup of teh population that is being studied; conclusions are drawn about the data from this sample as it is supposed to be rep. of the population random sample - a sampling technique to held make samples unbaised; yet, it is not always rep. of the populatoin summation notation look at notes parameter any numerical value describing a population; - often described using greek lettes statistic any numerical value describing a sample; - often denoted by english letters; often these are used to estimate for the parametere as the ral parameter is often too large to work with - note: if several samples are taken, they will not all be the same yet they should be close to each other measures of central tendency - what are they? any measure used to locate teh center of a collection of data 1. mean - avg. used most often 2. median - number in the middle (line up the numbers and find the middle one; even= find avg bt 2 middle ones; in large samples: teh middle value is the number of values plus one divided by 2 3. mode - most often; can be bimodial or na measures of variation - this is testing how spread out the data is; and how it compares to the mean - 3 types: 1. range: biggest value - smallest 2. variance: involves finding the deviation from the mean; where the deviation of an observation from teh mean is gotten by subtracting the mean from the observation; denoted by sigma sq. ; thiis number is almost always postive, but it can be zero if all the data is the same as mean; this number is sq of the std 3. standard deviation (std) - used for a measure of variability in some units of data, we take the sq. root of the variance to get this number ; always use the postiivve sq roott note: - use the same unit throughout problem chebshev's theorem - for any collection of values, at least the fraction 1-1/k2 of the observations; know three 3 numbers: 2, 3, 4; for 2: (either at least 3/4 or at most 1/4); for 3: (at least 8/9 , at most 1/9); for 4: (at least 15/16, at most 1/16) - note: the more spread out the data, the larger the length of the inverval and the sigma; z scores - used to do a comparision for ranking purposes from 2 groups - formula: z = x-'u'/ sigma - it is an observation (x) from a population with a mean and std that has a z-score or a z-value - this gives the number of std away from teh mean - z is just a number, does not use units frequency distribution - a table, which is the result of grouping data into classes or intervals and finding out the number of observations in eac. class - steps: 1. decide number of class intervals req. (usually given) 2. determine the range 3. divide the range by number of intervals to = class width - always whole number so round up 4/5. get class limits and class boundaries 6. tally and record frequencies for each class -voc: * class limits: the upper and lower - usally the same iterval, and they don't over lap, whole numbers * class boundaries: overlap, and go out .5 from teh class limits * class width: the upper class boundary minus the lower class boundary; it stays constant through a problem; should be one more than the class limit differnce; always a whole number