Page 158 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

Result Interpretation for Example # 3

The value 4.95 is the mean of X values which means that on average
all values has the worth of 4.95. This is the central tendency of the
summation of all observations divided by number of observations.
Similarly the mean of Y values is 5.50 that indicate that 5.50 is the
balancing point for all Y values.

Sum of square of x values is 134.95 and y values is 165; 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.

Standard deviation (SD) is the measurement of dispersion for a set
of data from its mean. Here S x=2.66, it means each value of X is 2.66
away from the mean. Similarly S y=2.9469, that indicates that each
value of Y is dispersed 2.9469 from its mean.

(r xy=0.6735) indicates correlation between X and Y. Since r xy in
Problem #1 is positive and above 0.6, it can be inferred that two
variables X and Y has positive moderate correlated.
According to data in problem #1, the regression equation is

defined . Where, 0.7447 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.
̅
Regression sum of square = SS reg=∑ (Y' - ) 2 =74.84031.
Regression sum of square or ‘explained sum of square’ is the

The Theory and Applications of Statistical Inferences 142