identify the factors that might have some influence on a chosen topic/variable. You will be collecting and analysing data for three different variables using a secondary source. There will be one dependent variable and two independent variables for which data will be needed.

Section 1

Dependent variable:Choose the main variable that you want to measure, versus other variables. This will go on the vertical axis of your scatter plot. Provide rationale for why you chose this variable and brainstorm different variables that may impact it. Research the variable on the internet and collect data for at least 10 years (the more the better) or over at least 10 different places (e.g., provinces and territories across Canada, countries in the world, teams in a sport) in the same year.

Section 2

Independent variables:Choose the independent variables that you want to measure against the dependent variable. You will find two different variables for which you can use a line of best fit to test correlation. This will be your biggest challenge of the assignment. You need to find variables that overlap with the data you chose for your dependent variable.

For example: If you found the years 1990 to 2010 for your dependent variable, you will need to find those same years for each of your independent variables. If you had data across the provinces and territories for your independent variable for 2016, you will need data across the provinces and territories for 2016 for each of your independent variables.

Section 3

One variable measures with graphs and distributions: Describe each of your three variables (two independent and one dependent) by calculating and making reference to the measures ofcentral tendency andspread. Also use a histogram and box plots. Describe thedistribution of your data as well. Describe the meaning of each of the measures of each variable, as you state what they are.

Section 4

Two variable measures with linear models and correlations: use two scatter plots, one for each of your independent variables. use the linear model to describe the relationship. Describe the strength of the correlation and the meaning of the slopes and y-intercepts.

Section 5

Identify and remove relevant points: Identify one point on each scatter plot that has the greatest impact on the model by using residual plots. Describe the percentile that the point would be, if the data were normally distributed, by using the mean, standard deviation, and z-score. Describe the impact of the point by comparing the new linear model to the original.

Section 6

Classification of correlation type:For each of the models, classify the correlation type as cause and effect, reverse cause and effect, accidental, or common cause. Explain your choice and brainstorm possible common cause factors, where applicable.

Section 7

Conclusions from linear models: Make conclusions from the two variable analysis, taking into consideration the removal of points that are far from the line of best fit. Discuss the overall impact, if any, that the independent variables have on the dependent variable, by referring to other sections of the report.

Please put screenshots of all the graphs used pls and thank you!

Mortality Life rate, infant Population Expectancy (per 1,000 growth GDP per capita Country Population at birth live births) (annual %) (current US$) Afghanistan 31,627,506 60.37446 68.1 3.03347 633.94786 Algeria 38,934,334 74.8081 22 1.9404 5484.06681 Argentina 42,980,026 76.15861 11.5 1.03306 12324.93879 Bangladesh 159,077,513 71.6259 32.1 1.21438 1086.80009 Brazil 206,077,898 74.40188 14.4 0.88636 11728.79939 Canada 35,587,793 81.95661 4.4 1.09807 50185.4815 China 1,369,435,670 75.78227 9.8 0.50631 7683.50204 Colombia 47,791,393 73.99315 14.1 0.944 7918.0789 Democratic Republic of the Congo 74,877,030 58.6592 76.5 3.15318 437.81493 Egypt 89,579,670 71.12171 21 2.21886 3365.70742 Ethiopia 96,958,732 64.03502 42.9 2.50681 573.56596 France 64, 121,249 82.37317 3.6 0.7909 42696.77883 Germany 80,646,262 80.8439 3.2 -1.41038 47902.65288 India 1,295,291,543 68.0138 39.3 1.22673 1576.81767 Indonesia 254,454,778 68.88849 23.6 1.26019 3499.58873 Iran (Islamic Republic of) 78, 143,644 75.38932 13.9 1.27655 5442.87477 Iraq 35,273,293 69.39968 27.2 3.36127 6484.52934 Italy 59,788,667 82.69024 3 0.9175 35365.10412 Japan 126,794,564 83.5878 2.1 -0.16255 38139.42236 Kenya 44,863,583 61.57637 36.6 2.64412 1368.49113 Mexico 125,385,833 76.72185 11.9 1.32122 10353.44798 Morocco 33,921,203 74.0161 24.6 1.39082 3186.60876 Myanmar 53,437,159 65.85785 40.7 0.85196 1227.13722 Nigeria 177,475,986 52.75427 71.5 2.66049 3203.2443 Pakistan 185,044,286 66.18337 67.4 2.10344 1320.55355Mortality Life rate, infant Population Expectancy (per 1,000 growth GDP per capita Country Population at birth live births) (annual %) (current US$) Peru 30,973, 148 74.52554 13.6 1.325 6490. 18119 Philippines 99, 138,690 68.26563 22.8 1.59325 2873.08819 Poland 38,619,974 77.25366 4.5 -0.07485 14341.85993 Republic of Korea 50,074,401 82.15585 3 0.40596 27989.35399 Russian Federation 143,429,435 70.36585 8.5 0.21763 14051.59912 Saudi Arabia 30,886,545 74.33722 12.9 2.24439 24406.47646 South Africa 53,969,054 57.18212 34.4 1.61574 6498.589 Spain 46,259,716 83.07805 3.6 -0.29895 29718.50022 Sudan 39,350,274 63.45854 48.8 2.14527 2087.70054 Thailand 67,725,979 74.42202 10.9 0.40622 5969.94012 Turkey 77,523,788 75.16351 12.3 1.69132 10303.69871 Uganda 37,782,971 58.46641 39.1 3.25377 734.74402 Ukraine 45,002,497 71.18659 8.1 -0.27891 3104.6583 United Kingdom of Great Britain and Northerr 64,331,348 81.0561 3.7 0.75335 46412.11727 United Republic of Tanzania 51,822,621 64.9439 36.2 3.15437 957.93453 United States of America 319,448,634 78.94146 5.7 0.7807 54539.66558 Viet Nam 92,423,338 75.62912 17.8 1.07421 2052.31908