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| What are the 4 steps for data-driven problem solving? |
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1. Practical problem 2. Statistical problem 3. Statistical solution 4. Practical solution |
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| What is the practical problem? |
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| Problem is stated in practical terms-billing errors cost us millions |
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| What is the statistical problem? |
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Express the practical problem as a statistical one.
What factors are significant predictors of billing errors? |
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| What is a statistical solution? |
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Obtain statistical solution using data analysis tools.
Inconsistency in the order filling process causes billing errors |
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| What is a practical solution? |
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Convert statistical solution to practical terms.
Simplify the billing process and train |
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| The science (and art) of data-based decision-making |
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| The entire process output which we wish to draw a conclusion |
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| The subset used to represent the population |
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| A sample drawn such that every member of the population has an equal chance of being selected |
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| A quantity associated with the population |
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| A quantity calculated from the sample |
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| What are the 2 types of statistical tools? |
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aim to describe and summarize the important features of a population or process
graphical-charts, graphs etc numerical-mean, median, mode |
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Use sample data to help make comparisons or draw inferences on the overall population
Analysis tools hypothesis testing data modeling |
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| What is a enumerative study? |
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study aimed to answer questions about the current population like how many or in what proportion
focuses on historical rather than predictive |
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| Which type of statistic is used in enumerative studies? |
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Answers questions like why or what are the causes of
Generalizes results to future states of the population |
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| Which statistical tool does analytical studies use? |
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| both descriptive and inferential |
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| What are the 2 types of measurement? |
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representations of categories or attributes
people, cars, animals
good/bad boy/girl |
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derived from a scale or continuum that is infinitely divisible.
seconds, minutes, inches
measurement of time, temp, weight |
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Data that is counted
discrete measurement term because they sort or count items based on attributes
-defects |
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Data that is measured
term for continuous measurement since they can take on infinite values within any 2 points |
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Groups are labels, no order -profession -color of car |
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groups are a logical order
-small -medium -large |
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Number of items or events -# of new car sales in a week -# of accidents in a year |
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Measurements made along a continuum -gas mileage -speed of pitched ball |
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Shows the relative frequency of defects in rank-order
Used to pick the low hanging fruits |
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| Takes into account severity ratings in addition to frequency |
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| Simple visual that conveys a lot of information |
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| Divide data into groups to reveal sub-patterns to help pinpoint cause of variation. |
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targets there is both upper and lower limits |
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| Average that is close to the target |
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| Average of a distribution |
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Sum of all observations ---------------------------- Total number of observations |
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| Divides data to 2 halves-it's the value in the middle |
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| Most frequently occurring value in the set |
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| Common measurements of dispersion |
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Range variance standard deviation |
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| difference between the largest and smallest data in the set |
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| average squared distance between mean and individual observations |
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| positive square-root of the variance |
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68-95-99.7% rule
68% of values in the normal curve fall within one SD of the mean, 95% fall within 2 SD of the mean, and 99.7% fall within 3 SD |
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| The x is the mean, median and mode |
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| As sample size increases, distribution of the mean tends to take the normal shape of the actual population |
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| how many standard deviations the observed value is from the mean |
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