1. Data was obtained from climate.nasa.gov and reported on Global Climate Change since 1880 to the present.
2. Central Tendency
I found in my report that the mean is 0.033, the median is 0.07, and the mode is 0.28. It is a right skewed distribution as seen in the boxplot.
3. Variation
The standard deviation is 0.336. The variance is 0.113 and it is the standard deviation squared. Variance at that point would be 113,000,000 which is the same as 10 to the 9^{th} place.
The range of my data is 1.47. This tells me that the maximum number is 0.99 and my minimum number is 0.48 and when they are subtracted, I get my range of my set of numbers.
4. Position
5# Summary is the minimum value, Quartile 1, Median, Quartile 3 and Maximum Value. The Minimum Value is 0.48. The Quartile 1 is 0.2. The Median is 0.07. The Quartile 3 is 0.22. The Interquartile Range is 0.42, which lies in between Q1 (0.2) and Q3 (0.22). My data has 3 sets of outliers. These outliers are for the year 2016 with 0.99 degrees, 2017 with 0.9 degrees and 2015 with 0.87 degrees.
6. This data set caught my interest because almost everything that has to do with global warming in some way affects us as people on this planet. I chose this data because I could see the way in which we as humans are destroying our planet. Every year surface temperature seems to be rising.
7. In the scatterplot that I created I can see that as each year passes by, the temperature change rises almost every year making them have a positive linear relationship.
8. The correlation coefficient is; r=0.8702
9. The correlation coefficient for this data set has a strong positive linear correlation.
10. The correlation is statistically significant or consistent because the y values (no smoothing or degrees) increases as the x values (year) increases and the data points appear more clustered together in the regression line.
11. The regression equation is; No_Smoothing= 14.12+0.0072 Year.
12. As the years increase, the degrees also increase by 0.0072, making it have a positive linear relationship.
13. The coefficient of determination is; rsquared or r2= 0.7572
14. This is the square of the correlation coefficient and it explains the ratio of the variation to total variation. It explains 75%, almost explaining 76% of the total variation of degrees in global climate change.
15. Coefficient of nondetermination is 1r2; 10.7572=0.2428
16. This is the percentage of variation in y (degrees) that is not explained by x (years) or the regression equation. The variation that is not explained is approximately 24%.
17. I have two hypotheses that can come to mind while analyzing all my data. My first hypothesis is that global temperature will keep on rising since we have not made many differences in the way we treat this planet and according to my data set. My second hypothesis is that we might make more changes to reduce global warming with all the new technology that is coming afloat and us as humans putting an effort to efficiently use less power when we don’t need to such as cars, phones, computers, electricity etc.
18. In conclusion, we have to step up our game in being more efficient with the things we don’t need and be more careful to not worsen global warming which is destroying many parts of the world each year that passes by.
Summary statistics:

Simple linear regression results:Dependent Variable: No_Smoothing Independent Variable: Year No_Smoothing = 14.120083 + 0.0072617633 Year Sample size: 139 R (correlation coefficient) = 0.87020861 Rsq = 0.75726302 Estimate of error standard deviation: 0.16616775 Parameter estimates:
Analysis of variance table for regression model:

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