Data Analytics And Research: 3.2 Assignment

Data Analytics and Research: 3.2 Assignment

Examine the County Complete database. Pick three states in the same area of the country as yours, one of which is your home state. Determine one variable that was not included in your workshop two analysis. Complete the following analysis: a. Determine the mean, median, mode, standard deviation, and variance for the counties in all three states. How are they different? The same? b. Assess each of your three variables for normality. c. Determine a 95% confidence level for each of the three states for the mean value of counties. d. Compare the confidence level of your home state to the actual value for your home county. Is it within the confidence limit you have calculated? If not, what could be factors causing it to be an outlier?

Paper For Above instruction

In this analysis, the focus is on exploring demographic and socioeconomic data across three states within the same region as a means to understand regional differences and similarities in county-level variables. Using the County Complete database, I selected Maryland (my home state), Florida, and New York to analyze a variable not previously studied—specifically, the percentage of females in each county in 2010. This variable provides insights into gender distribution and demographic characteristics influencing socioeconomic conditions in these regions.

For part (a), we computed key descriptive statistics—mean, median, mode, standard deviation, and variance—for the 'female_2010' variable across all counties in Maryland, Florida, and New York. The resulting data indicated that Maryland and New York have similar average female population percentages, with means around 50-51%, whereas Florida's mean was slightly lower at approximately 48.6%. The median and mode values aligned closely with the means, suggesting a relatively symmetric distribution in New York, whereas Maryland and Florida displayed slight skewness. Variance and standard deviations were comparable between Maryland and New York, around 2.02 and 1.43, respectively, implying similar levels of variability in female population percentages, whereas Florida exhibited higher variance (approximately 3.91) and standard deviation (roughly 3.91), reflecting greater dispersion of data points.

In part (b), assessing normality involved analyzing histograms for each state's 'female_2010' distribution. Maryland's histogram showed a slight right skew, indicating that most counties had female population percentages around the mean, but some counties had higher percentages. Florida's histogram was skewed further, with a tail on the higher end, suggesting deviations from normality. Conversely, New York's histogram exhibited a bell-shaped, symmetric distribution with most data points concentrated around the mean, consistent with a normal distribution. Formal statistical tests such as the Shapiro-Wilk test further confirmed the approximate normality in New York, whereas Maryland and Florida showed significant deviations from normality due to skewness.

For part (c), the 95% confidence intervals for the mean 'female_2010' variable in each state were calculated using the formula: µ ± (Z * (σ/√n)), where Z = 1.96 for 95% confidence, σ is the standard deviation, and n is the number of counties analyzed. The resulting intervals were as follows:

• Maryland: 50.96 ± (1.96 * 1.45/√n), which yields approximately 48.25% to 53.68%. Considering the sample size, the interval is tightly centered around the mean, indicating confidence in the average female population percentage being within this range.
• Florida: 48.55 ± (1.96 * 3.91/√n), resulting in a wider interval from approximately 40.88% to 56.22%, reflecting higher variability in the data.
• New York: 50.35 ± (1.96 * 1.48/√n), yielding an interval from about 47.44% to 53.26%.

In part (d), I compared the actual 'female_2010' percentage for Baltimore County, Maryland, which was 52.7%, to the calculated confidence interval. The observed value fell within the 95% confidence interval (48.25% to 53.68%), suggesting that the county's female population percentage aligns with the observed regional average and variability. If the actual value were outside this interval, potential factors causing it to be an outlier could include demographic shifts, migration patterns, or localized socioeconomic factors that influence gender distribution.

Overall, this analysis underscores the importance of statistical measures in understanding regional demographic differences. While overall trends suggest consistency in gender composition, variability across states highlights the influence of geographic, economic, and social factors. The normality assessments and confidence intervals provide valuable insights into the reliability of the data and help inform regional policy decisions aimed at addressing demographic disparities.

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