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Regression and Correlation
Name of the Student
Name of the University
REGRESSION AND CORRELATION
Part III: Regression and Correlation
Body fat versus weight data set
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In this case, it was necessary to identify the predictor variable and response variable for
simplifying the calculation of regression and correlation analysis. I would choose the responsible
value was to be based upon the value of predictor variable. In this, I could change the predictor
variable and I could see that how it would change the value of responsible variable. I would
identify the predictor variable as changing factor and response variable as influenced factor. In
below, x-variable was to be the predictor variable and because, the y-variable was varied upon
the value of x-variable (Miles, J., & Shevlin, M, 2001).
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Scatter plot of regression and correlation
In this case, I would choose the value of body fat was to be the predictor variable and the
value of weight was to be response variable. This referred that the weight was to be the changing
factor and body fat was to be an influenced factor and because the value of weight variable had
varied the value of body fat. In above scatter plot, we can see that, the value of body fat was
increased upon the value of weight. According to the above graph, this regression and correlation
was to be no correlation and because, in some values, the value of body fat was increased upon
the value of weight. In some values, the value of body was decreased upon the value of weight.
Moreover, the value of weight didn’t correspond with the value of body fat.
Correlation coefficient
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After applying the correlation coefficient formula, the value was got in above. According
to this, the value of correlation coefficient was to be 0.6122. When compared with the scatter
plot, the correlation coefficient had the value of 0.6122 and this value provided the relationship
between the variables of body fat and weight. This was to be given in above diagram and the
solution was that the correlation had no relationship between body fat and weight. The
conclusion was that the correlation value had less much of positive relationship between body fat
and weight. The strength of correlation had no relationship and but it had less much of positive
relationship (Donaldson, L, 1997).
Regression line to scatter plot
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Series1
y = 2.3249x + 134.89
Linear (Series1)
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R² = 0.376
Linear (Series1)
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Linear (Series1)
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0.00
0.0
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This linear plot was to be good for this data and because it had some body fat values which were
increased upon the value of weight of 252 men in gym. The slop means that the value of body fat
was increased rapidly, after getting some values of weight. The predicted value was to be -75.92
when the body fat value of Y was to be zero.
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Part IV: Putting it together
According to this, the statistical measures of above graph were plotted successfully and
the regression lines were to be implemented. The statistical measures stated that the average of
men who had been attended gym was to be inaccurate. In this case, the value of mean was to be
important than the value of median and because the mean value was to be average. According to
the hypothetical testing, the average body of men attending in gym was to be 20% and hence the
fat average body was to be 20%. Hence, the claim was to be inaccurate. In the case of regression
analysis, the relationship had less amount of positive relationship between the value of body
weight and body fat in gym. Hence, the regression analysis had no correlation and but it had less
amount of positive regression and correlation coefficient.
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References
Miles, J., & Shevlin, M. (2001). Applying regression & correlation: A guide for students and
researchers. London: Sage Publications. ISBN-0761962301.
Chatterjee, S., & Hadi, A. S. (2012). Regression Analysis by Example. Hoboken: Wiley. ISBN118456246.
Donaldson, L. (1996). For positivist organization theory: Proving the hard core. London: Sage
Publications. ISBN-0761952276.