Victor Kelsey's Observations

122 degrees in the summer months. 90% Of Australian citizens live outside the outback which

The concept of statistics is useful and necessary in many situations that we encounter on our daily life. Every morning when you walk out from your house do you wonder, what the weather will be like today, for the week or even for the month? Meteorologist use computer models that are built using statistics to help predict the future of the weather. Their main function is to gather and study data from past weather and identify the best indicators that will prognosticate a more accurate weather forecast. Using these statistical procedures are a great way to calculate the maximum temperature that will be on a specific day and place. By gathering together the wind velocity, direction, the amount of humidity, and cloudiness you can use this data

First, both of these Canadian cities have similar latitudes as noted before, within five degrees of each other. This similar latitude causes Vancouver and Halifax to be influenced by the same westerly prevailing wind. Another climatic aspect influenced by their similar latitude is their similar sunshine hours, at 1937.5 hours in Vancouver and 1962.5 hours in Halifax.2,4 This slight difference is due to Halifax’s lower latitude. Subsequently, their near identical sunshine hours are a cause to their similar peak average temperature, at 18.0 degrees in Vancouver and 18.7 degrees in Halifax, both in July and August.2,4 This time, the minute difference is caused by the ideal oceanic moderation Vancouver receives (warm ocean current, beneficial prevailing winds, and orographic barriers).

a much greater annual temperature range than City B. The most likely explanation for this fact is

Impact of Urban Density on local climate. Melbourne has an oceanic climate which sees big natural fluctuations in the temperature and weather conditions throughout the year, giving Melbourne the reputation of having “four seasons in one day” (“Melbourne Regional Office”). In any growing city, the urban architecture has a huge impact on the local climate of the city.

All the results show that the answers for variable Y have larger number than to variable X in 2012 and even larger compared to variable in 1990. Overall the one part that is remotely similar is the box and whisker plot; even though the numbers are not the same, the shapes of each of them are alike. Throughout all the measures, there is a noticeable pattern between the variables. Carbon emission has a larger result than the population per km2 in 2012 and 1990. The best measure to observe and best describes the central tendencies in both variables is the median, because the value is not distorted by the major outliers in the data set. Plus, since there are equal amount of data and both variables have a min lower than 5 and a max in the thousands, it seems fair that the median is the best central tendencies to describe the results; it is the only one that is snot

If temperature varies from 16 to 25 degrees each day, the standard deviation of temperature will be greater than if temperature only varies from 20 to 22 degrees each day. The standard deviation can be calculated using Excel (we will see how in class). You will want to make graphs and/or tables of these relationships for your report. Here is an example of the type of graph you will need to make. By convention, the independent variable (the “cause”) is plotted on the x-axis, while the dependent variable (the “effect” or consequence) is plotted on the y-axis. In the example below, which is the independent variable and which is the dependent variable? Age (years) 2 3 4 5 6 7 8 9 10 11 12 13 Height (cm) 91 99 107 112 117 124 130 135 140 145 150 155

The first variable considered is Location, a categorical variable. The three subcategories are Urban, Suburban and Rural. The frequency distribution and pie chart are included. Measures of central tendency and descriptive statistics are not calculated due to the categorical nature of the variable.

Indicating the individual number 65 gives a 5 point range to the mean. It seems the median is the most accurate way to discribe the data set, as it uneffected by the outlier value.

I noticed that the city has humid climate that is slightly modified by its relative proximity to the Atlantic Seaboard and The Great Lakes. The area experiences extremes of all four seasons, with temperatures ranging from zero to 90° F depending on the month. The city experiences a significant amount of rain most of the year and a slight bit of snow during the winter time.

In the past 50 years, the temperature has changed the most in the northern part of the world.

Preceding Black Saturday, Victoria experienced a heatwave that founded three consecutive days above 43˚C, the highest temperature reaching 46.4˚C. The heat wave was caused by a slow moving high pressure system in the Tasman Sea, from there a monsoon trough directed the hot and dry air

By using the box and whisker plot below, four outliers were calculated for points in the data set. Outliers were larger than 81 points, and smaller than 33 points.

This observation takes into account all the selected features x and y coordinates to come up with the mean center location. From here I did a Standard distance analysis to see if there was any pattern of distribution. As you can see in figure 5. And the results presented in table 3. The results of the test varied. BC, Ontario and NWT all had the highest mean distances but as you can see visually there are many outliers beyond the Standard distance circle. Provinces such as Manitoba and Saskatchewan are more compacted closer to the circle and a province such as PEI with the lowest standard distance, given how small the province are all contained close to the mean. The results of this test could show how results can be skewed by the present of outliers compared to the rest of the distribution.

Binding to a strict set of guidelines to measure weather temperature observation sites compiled by a department of the US government, a volunteer group found that 69% of the 1003 sites across the United States had a 2-degree or more error rating (Watts). In other words, these sites included the following: abundant vegetation close to the site and/ or being close to any artificial heating source which the list is incredibly liberal. Moreover, there is even the lingering problem that these observation sites aren’t evenly distributed across the world and equally important, the number of sites has been decreasing drastically since 1970 (McKitrick).