1. The total number of census division is , and the number of usable data is .
2. a)
Minimum: -59.7
Maximum: 36.3
Mean: -14.9
Standard Deviation: 14.12
The number of observations above and below the mean: 135
b) No, because it shows that the change is a negative change in poverty, which means it, is not the smallest change. To find this sort data table to acceding data in order to find the lowest percent change being 0%.
c) Decreased, because most data range in the negative with large poverty change.
d) Normal distribution, it tells us that the data is weighted heavily in the mean with extreme values.
e) Yes, there are outliers and the cause of them could be due to population number, the types of communities (aboriginal reserves)
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I had chosen these classes in order to see, in which classes the percentage of poverty change is heavily weighted and in which classes the percentage of poverty change is minimal. Organizing the data in the following classes allows the reader to spatially determine the areas with high and low concentrations of poverty and which ranges they fall in. I believe the best method to display the poverty percentage change in Canada is presented through natural breaks. The reason being natural breaks seeks to reduce the variance within classes and maximize the variance between classes. Therefore allowing the reader to clearly distinguish the different classes while grouping regions with similar percentage poverty change in the same classification. Being able to see clearly in Canada region of high and low percentage poverty change. A limitation I had found with attribute data was the issues of shapefiles, which store numeric attributes in character format rather than binary format. Therefore any numbers containing decimal places, lead to rounding errors when I was graphing the data. A way around this limitation would to be, either round all data points prior to inputting data and informing the reader that the data points are rounded or obtain data that has already been rounded or avoid data with decimals. A geographic …show more content…
The first being the dot mapping method, I had used this method in my map because it is a highly effective way to show density differences in geographic distributions across a landscape, and the dots show exactly where the event occurs. The second mapping method I had used was the proportional point symbol to indicate the scale and size with simple symbols proportionally to the data value found at that location. Large circles represent a higher density of population (urban areas) and smaller scale is a smaller density of population (rural areas). I had use the Miller cylindrical map projection for my two-variable thematic map for the following reason; first being the map is due to the accuracy and clearly defined of local shapes. We are displaying geographic data and it is important to have a projection that does not distort the area to effectively map the data. Secondly the map avoids scale exaggeration, which is also important when mapping geographic data within regions. One manipulation I had made to my map to improve the communicative effectiveness of my map, was to only emphasize the borders of France to clearly define the geographic area where the data is applicable. Advantages to using ArcGIS to map this data was that it provides a great simplistic map to communicate the data to spatial regions, and the program
The number 65 is the greatest outlier of the given values. The inbalanced influence of the integer can be highlighted by witholding 65 from the values and solving for the mean 13+3+35+19+12+15+5+42+11 = 155/9 = 17.22...
6. Based on questions 3, 4, and 5 is the mean or median a better estimate for the parameter of interest? Explain your reasoning.
6. Based on questions 3, 4, and 5 is the mean or median a better estimate for the parameter of interest? Explain your reasoning.
The mean birth weight of infants born at a certain hospital in the month of April was 128 oz. with a standard deviation of 10.2 oz. Which of the following is a correct interpretation of standard deviation?
2. Quantitative because the data given is concrete and generalizations like mean and mode can easily be identified.
C. The researchers analyzed the data as though it were at the interval/ratio level since they calculated means (the measure of central tendency that is appropriate only for interval/ratio level data) and standard deviations (the measure of dispersion for interval/ratio data) to describe their study variables.
d. It should appear in the tail signifying a negative relationship, with .05 in that tail.
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
|absolute and relative poverty. You will also read about the relationship between poverty and inequality, covering the types of |
As discussed in the previous section, a normal distribution has particular characteristics it conforms to. i.e.
2. Outliers from intentional or motivated misreporting: these occurs when participants purposefully report incorrect data to experimenters. A participants may make conscious effort to sabotage the research or may be acting from other motives. 3. Outliers from sampling error:
The first chart displayed shows that for the past few decades, family incomes decline a third of American children. The years of 1975-2011, the chart shows the changes in family income of children by income percentile. As the bar increases towards the right side of the graph, it represents that more
17. Put the following regions of the country in order from the highest poverty rate to the lowest poverty rate: Midwest, Northeast, South, and West.
lt is not all that unusual to find some outliers especially in a large population. For example if l surveyed 100 30 year old women about their weight to try find out what the average weight is for 30 year old women in my town, l would likely have one or two outliers. The outliers stand out in a set of data, and they sometimes "skew" the data altering the mean and range of the data for sure, but even though it may seem appealing, a true statistician would not just drop the outliers, unless he was absolutely sure the data entered was an error, (committed during data entry or any other step).
We divide groups based two things appearance and economic stability. The people on top of the pyramid are whites in which have the most money out of all races. I tend to see a lot of white people being the most economically dominant, for example how mostly every owner in the NBA is a white American male. Money, Money can have power, It does not matter what race you are but if you are exceedingly wealth then you have a voice in this country. From my my experience I have seen that we mostly revolve around the celebrities and if they do something that good for the community or if they do a criminal thing then we have them all around news. We are all individuals and I don 't see why people should be treated differently then others just because they are more economically stable. Therefore I am not saying there is people that I admire, but I admire their talent not their overall living. I 'd rather focus on ways I can get better financially. In this country we all play the game of the white people, the make it so they get richer by paying the lowest possible in order so they can produce enough of the product. We as the low income race we are forced to get the low income jobs because that is what society expects from us, especially because there are very little whites of whom don 't want to get dirty. I see many of my community having these low income jobs because without having legal documents you are limited to jobs.