The first Pie Chart shows that there were 50% more women than men who answered the questions in our survey.

The Histogram of people’s ages shows that half of the people interviewed were under twenty-two. There were the same number of people in each of the 22-30 and 31-45 age groups and less in both of the older age groups. We did not interview any woman who was over 50, or any man who was over 65 years old. The Comparative Pie Charts show that the men we interviewed were more equally spread over the age ranges than were the women. Over 60% of women were aged 16 -18, and over 80% were under 22.The Cumulative Frequency Graph shows the age ranges of men, women and combined in graphical form. The closeness of the lower quartile and median results indicate the concentration of young people in the data obtained, particularly for the women’s data.

The concentration of women’s ages in the 16-22 age groups is even more clearly demonstrated by the Box and Whisker Diagram, while the equivalent diagram for men shows a more uniform distribution of ages. The Bar Chart of people’s occupations show that most people were classified as “other”. This category included students and those who had unusual occupations.Apart from this group there were more professional people than any other type. The Comparative Pie Charts for men and women’s occupations show that over 50% of women were classified as ‘other’, compared to 25% of men. Two-thirds of men were in managerial or professional occupations, compared to only under 25% of women.

There were no men in clerical, personal services or sales occupations, and no women in craft occupations. The Histogram showing the number of days people worked indicated that the majority of people worked for five days a week.Everybody worked for at least two days a week, but nobody worked all seven days. A third of all people worked for three days a week or less.

The Comparative Bar Chart of days worked shows that more than 80% of the men worked for five or six days, with under 20% working a shorter week. Less than 50% of women worked five or six days, and exactly 50% only worked for two or three days. The Frequency Density Polygon for the number of rooms in peoples houses shows a wide variation in the size of houses. Most people live in houses with between four and nine rooms, with only 20% of people living in larger houses.The overall shape of the graph follows the profile of a normal distribution curve, but not all points lie near the profile, due to the limited number of people surveyed. The Cumulative Frequency Graph of house sizes reflects the approximately regular increase across the responses. It shows clearly that nobody lived in either very small houses of less than four rooms, or in very large houses of more than fourteen rooms.

In between there was a regular increase. The Scatter Diagram comparing people’s equivalent earnings for five days with their occupation shows that Managerial and Professional people are paid more than any other groups.It also shows that men are paid more than women in all groups. The diagram also shows that decrease from managerial to sales occupations. The Scatter Diagram comparing people’s earnings with their house size does not show any particular correlation, although it does show the wide diversity of house sizes.

The data for men only does show more of a trend of larger house size with increasing earnings. The data for women is less meaningful as so many of them are in the lowest income group. The data points for the people living in houses with twelve or more rooms all came from 16-18 year olds in occupational group 9.My assessment of these is that they were all students living in ‘houses in multiple occupation’ or student accommodation.

The Scatter Diagram comparing people’s equivalent five day earnings with their house size does not produce a significant trend either. For men there is generally an increase of house size with earnings, but this effect is far less obvious for women. This is due to the large number of women on low earnings. The calculation of Spearman’s Correlation Coefficient gives an answer of 0. 07, which indicates almost no positive correlation.This result is mainly caused by the fact that there was no data for nine of the eighteen categories.

There were only four values for men’s earnings and five values for women’s earnings. Of these only two categories had values for both men and women, while another two categories had no data at all. Because of the poor range of data it is not surprising that the results show no correlation. The calculations for Mean Deviation and Standard Deviation of men’s, women’s and all peoples earnings show that the mean weekly earnings of the men we surveyed was i??436-67p, compared to i?? 108-33p for women. The mean deviation for men was i?? 172-78 for men, compared to i?? 75-31p for women.

The standard deviation for men was i?? 235-74p, but was i?? 108-06p for women. These figures show that the women surveyed only earned about one quarter of the amount men earned. This is a far smaller amount than I expected and is probably due to the high proportion of students in the sample. The values for the mean and standard deviations show that there is a wide range of earnings for both men and women.In particular the standard deviation for women’s earnings is almost the same as the mean earnings. The calculations for Grouped Mean and Standard Deviations of men’s, women’s and all peoples ages show that the mean ages were 37 for men, 21 for women, and 27 for everybody.

The Mean Deviation for men was 11. 5 and the Standard Deviation was 13. 5. This shows that the age range for men was quite widely spread. The figures for women were 5.

5, and 8. 5 respectively, which shows that the ages were far closer together, and that most of the women I questioned were under thirty.The results for all people were almost identical to those for men, which shows that the effect of a predominantly young group of women had little effect on the overall age ranges of the people I surveyed. Conclusions From my survey of eighteen women and twelve men, I have been able to compare gender, age, occupation, earnings, working week and house size in a variety of ways. The results differ from my predictions in several ways. There was a higher proportion of young people than I expected. I think this was because of the number of students interviewed, and it may have been just after the end of their lectures for the day.

There were more people in full time employment than I expected. I think this was because most of the men we interviewed were in professional occupations, and many of the women also worked full time. The range of people’s house sizes was greater than I predicted, and was also not related to earnings. This could be because many young people live in large shared houses with other young people, and better off people also have larger houses, so that there was a wide variety of results. The biggest difference to my predictions was the relatively low earnings of women compared to men.

This was due to the high proportion of women students in the sample. The data for men showed a higher proportion of professional people and consequently higher earnings and more people in full time employment. Generally my results were approximately in accordance with my predictions. The variations were mainly caused by the small sample size and the short time the survey was carried out over. If I was able to carry out a survey for a longer period, over several days, and at different locations, then my results would reflect more accurately the type of people who use Wigan Town Centre, their occupations, earnings and house size.