# Math 533 Statistics Report

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Math 533 Statistics Within this report I will discuss 3 variables and the comparison of 3 different pairs of variables. Location: The location data provided falls under three categories Suburban, Urban and Rural. This particular variable is qualitative thus certain numerical calculations could not be completed. The frequency for each category was determined and is depicted by the following table. Location Type | Frequency | Suburban | 15 | Urban | 21 | Rural | 14 | Total | 50 | Following calculating the frequency a pie chart was developed to determine which particular category had the highest percentage from the sample data provided. The pie chart demonstrates that the Urban category holds the highest…show more content…
Like the Income variable the Credit Balance variable is also a quantitative variable. With the sample data provided one was able to compute the following calculations. Data Calculations: | | | | Median | 4090 | | | Mode | 3890 | | | Mean | 3964.06 | | | Standard Deviation | 933.4941 | | | Total Sample Variance | 871411.2 | | | Maximum | 5678 | | | Minimum | 1864 | | | Range | 3814 | | | Class width | 476.75 | rounded :500 | In the process of analysing the data one drafted a frequency distribution table which included a total of 8 classes. The frequency for each class was developed along with the calculation of the classes’ midpoint. Taking this information a Histogram bar graph was created. Frequency Distribution Table | 8 classes | Classes | Frequency | Midpoint | 1864 | 2363 | 1 | 2113.5 | 2364 | 2863 | 5 | 2613.5 | 2864 | 3363 | 9 | 3113.5 | 3364 | 3863 | 5 | 3613.5 | 3864 | 4363 | 14 | 4113.5 | 4364 | 4863 | 7 | 4613.5 | 4864 | 5363 | 6 | 5113.5 | 5364 | 5863 | 3 | 5613.5 | | | 50 |…show more content…
With each households income being depicted by the vertical axis this plot clearly shows no sign of relationship. There is no specific pattern or array of data thus proving there is lack of correlation between such variables. In analysing the Income vs. Size the information output is similar to that of the comparison of location vs. income. The variables do not show a clear sign of correlation or relationship as shown within the scatterplot. The points are slapdash or disorganized thus making it evident that there is no relationship between the two variables. Overall one can determine that in comparing certain variables there are some matches that provide some cohesiveness whereas all aren’t completely relative to one another. By being able to take sample data and effectively review and analyse it one was able to record which households of what size is more likely to hold a higher credit limit or even what location. With such information AJ Davis Department Stores can have a better outlook as to how their customers are more or less likely to be and can futuristically categorize them based on such