# Statistical Analysis: Income, Household Size, Amount Charged

May 21st, 2016
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Statistical Analysis: Income, Household Size, Amount Charged

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1) Descriptive StatisticsIncome(\$1000s)MeanStandard ErrorMedianModeStandardDeviationSampleVarianceKurtosisSkewnessRangeMinimumMaximumSumCountConfidenceLevel(95.0%)HouseholdSize43.482.057786425414.55074211.7241-1.247720.0958564621672174504.135275MeanStandard ErrorMedianModeStandardDeviationSampleVarianceKurtosisSkewnessRangeMinimumMaximumSumCountConfidenceLevel(95.0%)AmountCharged (\$)3.420.24593321.7389893.024082-0.722810.527896617171500.494215MeanStandard ErrorMedianModeStandardDeviationSampleVarianceKurtosisSkewnessRangeMinimumMaximumSumCountConfidenceLevel(95.0%)3964.06132.01640903890933.4941871411.2-0.74183-0.1295138141864567819820350265.2961All three sets of data are significantly spread: the standard deviation is a high percentage of themean. The income data seems to be uniformly distributed, as displayed in the histogram.Household size data is very skewed, due to the fact that two people represent the majority ofhouseholds. The amount charged data seems to be more normally distributed.The following communication matrix displays the variance amongst the data:Income_(\$1000s)Hous ehold_Siz eAmount_Charged (\$)There is a correlation between amount charged and income and between amount charged andhousehold size. One may also predict with very high level of confidence (better that 99%) thatthere is no relationship between income and household size. The scat

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