Financial Chapter 10
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Financial Chapter 10
Chapter 10: Analyzing population data
The use of mathematical techniques to collect, organize, describe, analyze, and interpret large amounts of numerical data in order to help ppl make decisions.
The numerical data used in statistical analysis.
In statistical analysis, a complete set of collected data.
Statistics that summarize or describe a complete set of collected data, known as a population, or data set.
contrast with inferential statistics
measure of central tendency
A representative value that describes the values in the middle of a population.
The numerical "average" of a series of values.
An extremely high or low value that is not representative of the other values in a population.
The middle value in a set of values that is arranged in numerical order.
The statistical measure that identifies the value that appears most often in a population.
measure of dispersion
A representative value that describes the distribution of data around specified central values.
The difference b/t the highest and lowest values in a particular population.
The avg squared distance b/t the population mean and each individual item in a population.
For a population, the square root of the variance of the population.
The likelihood that a given event, observation, or result will occur.
A type of probability distribution where the number of values that are less than the mean is the same as the number of values greater than the mean.
A variable that includes a finite, or limited, number of values.
A variable whose values represent all possible outcomes.
Objective: describe the primary benefits descriptive statistics provide to managers.
Objective: Calculate the 3 primary measures of central tendency - the mean, the median, and the mode - and describe the strengths and limitations of each measure.
Objective: Calculate the 3 primary measures of dispersion-- the range, the variance, and the standard deviation --and explain their importance to insurers and other users.
Distinguish b/t normal and non-normal distributions and identify insurance situations in which they apply.
Identify the characteristics necessary to ensure data quality.