Introduction to regression
Introduction to regression
Introduction to regression
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118 Further Mathematics<br />
THINK WRITE/DISPLAY<br />
4<br />
5<br />
Find the gradient and y-intercept of the leastsquares<br />
<strong>regression</strong> line.<br />
Press STAT .<br />
Select CALC and 4:LinReg(ax+b).<br />
After LinReg(ax+b) appears, type L1, L2, Y1.<br />
(To type Y1, press VARS and select Y-VARS<br />
and 1:Function then 1:Y1. Press ENTER .<br />
Write the least-squares <strong>regression</strong>.<br />
y = −0.63x + 11.73<br />
Press GRAPH <strong>to</strong> view the data and <strong>regression</strong><br />
line. You may need <strong>to</strong> adjust the WINDOW<br />
settings.<br />
Using arithmetic<br />
Calculate the means.<br />
1<br />
2<br />
Draw up the required table and complete<br />
all calculations.<br />
Column 1: x-values<br />
Column 2: y-values<br />
Column 3: x-deviations from the x mean<br />
Column 4: x-deviations squared<br />
Column 5: y-deviations from the y mean<br />
Column 6: y-deviations squared<br />
Column 7: Product of x- and y-deviations<br />
Total each column.<br />
Note: Sum of columns 3 and 5 is 0.<br />
66<br />
x = -----<br />
8<br />
= 8.25<br />
x y ( )<br />
1 11 –7.25 56.5625 4.5 20.25 –32.625<br />
3 9 –5.25 27.5625 2.5 6.25 –13.235<br />
4 10 –4.25 18.0625 3.5 12.25 –14.875<br />
7 6 –1.25 1.5625 –0.5 0.25 0.625<br />
10 8 1.75 3.0625 1.5 2.25 2.625<br />
12 4 3.75 14.0625 –2.5 6.25 –9.375<br />
14 3 5.75 33.0625 –3.5 12.25 –20.125<br />
15 1 6.75 45.5625 –5.5 30.25 –37.125<br />
66 52 0 195.5 0 90 –124<br />
3 Use the formula <strong>to</strong> calculate the gradient by Σ( x – x)<br />
( y– y)<br />
substituting values derived from the table.<br />
m =<br />
Σ( x – x)<br />
2<br />
------------------------------------<br />
52<br />
y = -----<br />
8<br />
= 6.5<br />
x – x ( x – x)<br />
2 y– y ( y– y)<br />
2 ( x – x)<br />
( y– y)<br />
4 Use the formula <strong>to</strong> calculate the y-intercept.<br />
– 124<br />
= ------------<br />
195.5<br />
= −0.6343.<br />
b = y − mx<br />
= 6.5 − (−0.6343)8.25<br />
= 11.733<br />
5 Write the equation of the least-squares linear<br />
<strong>regression</strong> line.<br />
y = −0.634x + 11.733