Page 168 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

Result Interpretation for Example # 4

The value 6.8 is the mean of X values which means that on average
all values has the worth of 6.8. This is the central tendency of the
summation of all observations divided by number of observations.
Similarly the mean of Y values is 4.25 that indicate that 4.25 is the
balancing point for all Y values.

Sum of square of x values is 73.2 and y values is 35.75; it is the
mathematical approach to determine the dispersion of data points.
This tells the summation of difference of each raw score from mean
value. The sum of square x and y is the square of deviation scores
which is a key calculation in regression analysis.

Variance indicates the variation of a set of scores from their mean.
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Here S x =3.85, it indicates that each score of x is vary 3.85 from its
mean. Standard deviation (SD) is the measurement of dispersion for
a set of data from its mean. Here S x=1.96, it means each value of X is
1.96 away from the mean. Similarly S y=1.37, that indicates that each
value of Y is dispersed 1.37 from its mean.

(r xy=0.80) indicates correlation between X and Y. Since r xy is positive
and equal to 0.8, it can be inferred that two variables X and Y has

positive highly correlated. The regression equation is defined
. Where, 0.5601 is the slop of the regression
line. It can be inferred from this equation that when x increases y
increases. This regression line shows positive moderate correlation
between predictors and criterion variables.

2
̅
Regression sum of square = SS reg=∑ (Y' - ) = 22.964. Regression
sum of square or ‘explained sum of square’ is the amount of total
2
sum of squares (∑y ) that can be explained in regression model. It

The Theory and Applications of Statistical Inferences 152