Sharp EL-512 - Two-Variable Statistics and Linear Regression

DisplayKey in:
35
IdmaI
1
45 l DATA l 2
55 x 5
[data]
7
65 x 2
[data]
9
Key in: Display:
Mean:
e m
53.88888889
Standard Deviation:
§<§ [si]
9.279607271
Variance:
i g a
86.11111111
Correct Data (CD):
The last entry above is an error and must be changed to 60 x 2.
Key in: Display
65 [X ] 2 7
60 [X] 2 (m§ 9
Note: When you correct the mis-entry before pressing the (daw) key, use [cp] key.
3. Two-Variable Statistics and Linear Regression.
In addition to the same statistical functions for Y as for X in single-variable statistics,
the sum of the products of samples Z XY is added in two-variable statistics.
In Linear Regression there are three important values; r, a, and b. The correlation
coefficient r shows the relationship between two variables for a particular sample.
The value of r is between —1 and 1. If r equals —1 or 1, all points on the correla
tion diagram are on a line. The further the value of r is from —1 and 1, the less the
points are massing about the line and the less reliable is the correlation. If r is more
than 0, it shows a positive correlation (Y is in proportion to X) and if r is less than 0,
it is a negative correlation (Y is inverse proportion to X).
The equation for the straight line is Y = a + bX. The point at which the line crosses
the Y axis is a. The slope is b.
r Correlation coefficient
Sxy
Sjcx ' Syy
a a=y — bx ) Coefficient of linear
b b _ $xy > regression equation
Sxx ) Y = a + bx
/ V -v- \ 2
45