Frequency Distributions and Sampling

In the movie Unnatural Causes: In Sickness and in Wealth, it compared the lives of four individuals, Taylor, Young, Anderson, and Turner, in different locations, race, and socioeconomic background. The scale of difference between this group of people is that it goes from financially stable and healthy individuals to individuals with lower income and poorer health. This wealth-health gradient reflects that those who have easier access to healthier lifestyles (i.e. running outside without the concern of safety) are more likely to have a higher life expectancy than those who are in living environments that are not as developed and lack healthier options of nutrition. The difference of the average life expectancy scaled down from Jim Taylor whose neighborhood had an average life expectancy of 80 years, Young’s 75.3 years,

As discussed in the previous section, a normal distribution has particular characteristics it conforms to. i.e.

Standard deviation is a way of visualizing how spread out points of data are in a set. Using standard deviation helps to determine how rare or common an occurrence is. For example, data points falling within the boundaries of one standard deviation typically account for about 68% of data and those between (+/-)1 standard deviation and (+/-)2 standard deviations make about 27% combined. This can be better visualized by using a bell graph. Using the mean and standard deviation, the points where standard deviations occur can be drawn on the graph to better understand which data is rare and which is common.

society.The fact is that early death rates were much higher in history compared to what they are

·  Compare the measurements in the study with the standard normal distribution, what does this tell you about the data?

5) Describe how the normal range for any given measurement is obtained. Explain why published values for normal ranges may differ and why these values must be continually checked and updated.

21. Suppose that you are designing an instrument panel for a large industrial machine. The machine requires the person using it to reach 2 feet from a particular position. The reach from this position for adult women is known to have a mean of 2.8 feet with a standard deviation of .5. The reach for adult men is known to have a mean of 3.1 feet with a standard deviation of .6. Both women’s and men’s reach from this position is normally distributed. If this design is implemented:

Life expectancy, or the estimated years of life for a person or group of people, has increased over the years but it is still surprisingly lower than expected in places such as the United States. America ranks twenty-fourth on the life expectancy list under Japan, Australia, France Spain and Italy. What causes this disparity in the ranking and statistics in an advanced industrialized society such as the United States? There are major statistical factors that influence the United States ranking in world life expectancy, involving education level, poverty, race and gender. The diverse groups of people living in the United States affect the health statistics negatively, bringing in influences from education and income levels, race and

The article then refers back to the affects of inequality towards lifespan, that paychecks aren’t the only thing that is unequal but the wealthier live longer than

This study revealed that the wealthy live longer due to better access to health services, and quality of care. The study pointed at a strong relationship between job grade of civil servant and death rates from a range of causes. Those in lower job grades had death rates thrice that of men in the higher grades.

This fact remains accurate after government attempts at wealth redistribution such as taxes. This shows that the government is not successful at helping to redistribute wealth and the dramatic increases in wealth of the rich while the poor barely improve show the inefficacy of the “trickle-down economy” model. To figure out why the 10% is gaining wealth so quickly, the people that make up this small group must be analyzed. The top 10% is essentially comprised of three main groups: superstars, CEOs, and high-income professionals. However, the incomes of superstars and CEOs are increasing more rapidly than those of the high-income professionals (Belsie). While the incomes of high-income professionals and superstars are market driven, they do not benefit from the same rate that CEOs do.

Looking at the data, there needed to be some deleting and altering of the data. Some years had to be deleted from the data set because the number of records for those years were too low. There were single entries for years 1809,1810,1812,1863 and 1864. These entries were deleted from the data set. The year 1932 and the years 1948-1999 were also deleted. The year 1932 only had 15 records, so the number of records was too low. 1948-1997 were deleted because those years only had between 1 and 21 records so they were deleted because they were too low. The number of records was < 30 so we discarded these years. The sample sizes for these years were small and the data would not fit a normal distribution.

In fact, the statistical information of the Census related to average income for family was a positive distribution at the top between 1970 and 2000. Nevertheless, this positive share of income got to fall as a consequence of the

Statistical dispersion is measured by a number system. The measure would be zero, if all the data were the same. As the data varies, the measurement number increases. There are two purposes to organizing this data. The first is to show how different units seem similar, by choosing the proper statistic, or measurement. This is called central tendency. The second is to choose another statistic that shows how they differ. This is known as statistical variability. The most commonly used statistics are the mean (average), median (middle or half), and mode (most frequent data). After the data is collected, classified, summarized, and presented, then it is possible to move on to inferential statistics if there is enough data to draw a conclusion.

The life in the U.S compared to that of the Soviet Union during the 60’s is an amazing phenomenon of a declining life expectancy in a highly developed country just like in the case of the Soviet Union during the 60’s down to the 70’s. The result of the life expectancy rate in the Soviet Union shows that there is only a small part to true informal changes in the state of living. However, the weak point of the measures of life expectancy is of vital importance, although another factor is the unfavourable selection of risks by war, thereby making a less valuable comparison between the international and inter temporal. Another factor is the logical difference between period and the measurement of the group of people sharing a common factor