Quantitative Analysis
The final project entails systematic extraction of decision-aiding insights out of a dataset. The project will provide hands-on experience in conducting and interpreting different types of function-wise statistical analysis. The focus of the analysis will be on marketing strategies and analysis-related topics (South University Online, 2012). The sample set was examined thoroughly to reveal findings relevant to the marketing strategies and the interpretation of the data. The median income is between the $50,000 income and the $74,999 income. The frequency of 449 is the highest between the income level of 50,000 and 74,999. The distribution of income is skewed and it is slightly right. The mean age is around 51 with the standard deviation being 14.72.The frequency of the age 48-52 is the highest at 276.
The mean wealth score is 301.8376. The ratio of men to women were very close. The men had a mean of 50. Whereas, the women were significantly close at 49. The single people make up 22 percent. Whereas, married people make up 77 percent. The interpretation of the diagnostic test are often left up to the model assumptions. The regression model reveals that the Wealth score and gender are statistically significant factors of the median school years. It also reveals that the other variables are statistically insignificant to the median school years. The median school years increase when the wealth score changes. The purpose and importance of the assessment is to find variables that correlate with each other. There may be a direct cause and effect relationship between variables. There also may be a reverse cause and effect between two variables. The relationships between the variables may also be caused by a third variable. It appears that all of the variables could be affected when measuring their association with the median school years. First, there has to be a relationship between the independent and dependent variables which it is.
The mean wealth score is 301.8376. The ratio of men to women were very close. The men had a mean of 50. Whereas, the women were significantly close at 49. The single people make up 22 percent. Whereas, married people make up 77 percent. The interpretation of the diagnostic test are often left up to the model assumptions. The regression model reveals that the Wealth score and gender are statistically significant factors of the median school years. It also reveals that the other variables are statistically insignificant to the median school years. The median school years increase when the wealth score changes. The purpose and importance of the assessment is to find variables that correlate with each other. There may be a direct cause and effect relationship between variables. There also may be a reverse cause and effect between two variables. The relationships between the variables may also be caused by a third variable. It appears that all of the variables could be affected when measuring their association with the median school years. First, there has to be a relationship between the independent and dependent variables which it is.