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Data Analysis (Applied Research Method) - Essay Example

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Data Analysis (Applied Research Method)

Household public transport miles per week
-0.202
-0.074
-0.085
-0.404*
-0.176
0.558**


Total leisure miles per household per year
0.584**
0.451*
0.424*
0.398*
0.397*
-0.05
-0.161

Total household gas and electric bills per annum
0.498**
0.379
0.491**
0.313
0.544**
0.05
0.003
0.153
**. Correlation is significant at the 0.01 level (2-tailed), *.Correlation is significant at the 0.05 level (2-tailed).


C2: Number of Negative Correlations
Twelve out of 36 independent correlations are observed to be negative correlations. In which number of public transport users in household negatively correlates with total city CO2 emissions per household per annum, number of household members, average household income per annum, number of cars per household and household car miles per year i.e. r = -0.16, -0.12, -0.188, -0.443, -0.235 respectively. Similarly we observe that there re some more negative correlations like Household public transport miles per week verses total city CO2 emissions per household per annum, number of household members, average household income per annum, number of cars per household and household car miles per year i.e. r = -0.202, -0.074, -0.085, -0.404 and -0.176 respectively. Finally we observe that total leisure miles per household per year also negatively correlates with the variables number of public transport users in household and household public transport miles per week i.e. calculated as r = -0.05 and -0.161 respectively.

C3: r = 0.889 is the most strongly correlated correlation value which has measured by household car miles per year verses total suburban domestic CO2 kg emissions per household per annum.

C4: Correlation is significant at the 0.01 level (2-tailed). A correlation coefficient of r = 0.889 indicates a very good...
r = -0.202, -0.074, -0.085, -0.404 and -0.176 respectively. Finally we observe that total leisure miles per household per year also negatively correlates with the variables number of public transport users in household and household public transport miles per week i.e. calculated as r = -0.05 and -0.161 respectively.
C4: Correlation is significant at the 0.01 level (2-tailed). A correlation coefficient of r = 0.889 indicates a very good linear relationship between household car miles per year and total suburban domestic CO2 kg emissions per household per annum. Since r2 = 0.7903, we can say that about 79% of the variation in the household car miles per year is accounted for by a linear relationship with total suburban domestic CO2 kg emissions per household per annum.
C5: r = -0.16 is the least strongly correlated correlation coefficient value which has measured by number of public transport users in household verses total suburban domestic CO2 kg emissions per household per annum.
C6: Correlation is significant at the 0.05 level (2-tailed). A correlation coefficient of r = -0.16 indicates a strongly weak linear relationship between number of public transport users in household and total suburban domestic CO2 kg emissions per household per annum.
Bivariate ...Show more

Summary

Twelve out of 36 independent correlations are observed to be negative correlations. In which number of public transport users in household negatively correlates with total city CO2 emissions per household per annum, number of household members, average household income per annum, number of cars per household and household car miles per year i.e…
Author : rosanna98
Data Analysis (Applied Research Method) essay example
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