How The Estimated Poor Rates May Be Calculated By Some Actual Data Such As Deprived Rates And Demographical Factors

Descriptive statistics are digits that are used to summarize and describe a given range of data (Klenke, 2008). Basic descriptive data includes, mean, median, mode, variance and standard deviation. The data can be rearranged in an ascending order as follows:

Poverty, according to (SHRIDER and CREAMER) in

M2: This table shows that people who live in most deprived areas are more likely to smoke, are less likely to have a good education, they more than likely lived in poverty as a child. This table also shows that the least deprived people are the people with the most education and then end up becoming a professional

|Within Scotland, there were 980,000 people living in relative poverty and 620,000 across Britain working but living in relative |

economic issues of a place where poverty pervades a third of the population is posed and 1

Clearly, we can see from the table that 11% of all households whose total amount of population was 1,837,000 lived in poverty. The proportion of poor single person with no children was 19% (population 54,000), whereas sole parent had 21% people (population 232,000) who lived in poverty. However, couple with no children had a 7% of population (population 211,000) living in poverty, whilst the proportion of poor couple with children who was poor account for 12% (933,000). In addition, there was 6% of single aged person who lived in poverty, whereas only 4% aged people (population 48,000) lived in poverty.

This section of the paper consists of three main parts. First, the research questions that will be addressed, the expected hypotheses and an identification of independent and dependent variables. Second, the supporting literature for the hypotheses is discussed. Third, a conceptualization (definition) and operationalization (measurement) of each independent and dependent variable. Research Questions

Measuring the amount of households currently receiving subsistence allowances and during the second year of an income increase, conduct another measurement of the households receiving government assistance. The reason for my design and method, is because some of the participants may not want to expose themselves to such a research, because they may feel humiliated by their livelihood. The method of the data analysis is to attempt to get at 100 participants from four regions in the country; the Northeast, Southeast, Midwest and West Coast. With the collected data from these regions, the questions can be answered though the responses. The responses can contribute to theory by providing evidence that increase in the minimum works or does not

Fifty-eight point three percent (58.3% ) of the population lives below the poverty line, with great disparities existing whether urban or rural and the poverty incidence decreases from North to South . Moreover, the monetary poverty approach of the Unified Questionnaire of Basic Indicators and Welfare statistics (in French QUIBB) has revealed that, on average the poverty line has increased by 14.5% from 2006 to 2011

Statistical analysis was conducted as mean and standard deviation using Statistical Package for Social Sciences (SPSS), version 16.0 for Windows (SPSS, Chicago, IL).

In this book, it is found that the author manage to give detail explanations on each factors that is believed to cause poverty. The author makes progress and develops the ideas convincingly by providing adequate information based on statistical data and empirical evidences. In chapter four, the author argues that level of education affects the potential earnings of household. Indeed he writes, “The access to education expands the potential for human capital thereby enabling one to qualify for better-paid jobs” (29). To support his argument, the author proves it based on the results of the survey in which it is presented in table 4.1 that show the distribution of household heads based on the level of general education.

The situation keeps aggravating despite of several poverty alleviation programs that are implemented every year. The main reason for such failure, apart from the implementation issues, would be the measurement and numbers on which these programs are based. Appropriate measurement is central to analyzing and understanding poverty and its alleviation.

When refer to define poverty, the difference between the traditional unidimensional approach and contemporary multidimensional approach for the measurement of poverty should be considered. While only one variable is submitted in terms of the traditional approach, for example, consumption or income, multidimensional approach, for example, Sen’s capability theory, expands the amount of dimensions alongside which poverty is determined. The multifaceted reality of poverty, conversely, makes it tricky to confine the essence of this experience by means of a single uni- or multidimensional approaches for measurement (Fusco, 2003).

The objective of this chapter is to describe the procedures used in the analysis of the data and present the main findings. It also presents the different tests performed to help choose the appropriate model for the study. The chapter concludes by providing thorough statistical interpretation of the findings.

Following the approaches used in other ‘poverty–environment’ relationships studies (Ellis, 2001; Cavendish, 2002; Twnie et al., 2003; Fisher, 2004; Narain et al., 2008; Babulo et al., 2009), the following simplifying assumptions are made in household income accounting.