CORRELATION AND REGRESSION
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Formula that xxxxxxxxxx x xxx
As a result, xx have:
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Formula that xxxxxxxxxx x xxx
As a result, xx have:
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x have xxxxx as much as possible xx explain to xxx xxx xxxxxxxx xxx me xxxx xx you need more xxxxx Also, Please DO xxx xxxxxx to rate me xx xxx xxxxx my xxxxxxxx A testimonial xxxx be x xxxxx xxx xx acknowledge! :)
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xxxx
xxx xxxxxxx xxx xxx correlation xxxxxxxxxxx is given xx
Now, all xxx xxxxxx xxx xxxxx in the xxxxxxxxx Substituting, xx get:
x x [10(5980)-(525*100)]/[sqrt((10*32085)-525 2 xxxxxxxxxxxxxxxxx 100
x xx
x = 7300/(212.66*43.81)
r x xxxxxx
xxxx
The correlation co-efficient represents the xxxxxx xx xxxxxxxxxxx of variation) xxxxxxxxx xx xxx xxxxxxxx
explained xx xxx xxxxx variable. xxxxx xxxxxx xx xxxxxxxxx in Y xx explaned xx xx
Section B
xx xxx xxxxxxx numbers xxxx been filled.
xx Y is xxxxxxxxx xxxxxxxxxx with X. xx xxxx xxxxx xxx every xxxxxxxxxx x, x xxxxxxx by 5. xxx
xxxxxx xxx calculated accordingly.
x Y
x x
x xx
x xx
xx 52
xx 67
b) Y is xxxxxxxxx negatively correlated xxxx X. This xxxxx that
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| xxxx | x | x | |||
| Motorboats xxxxxxxxxx | Manatee deaths | xy | x square | xy xxxxxx | |
| 1977 | x447 | xxx | 5811 | 199809 | xxx |
| xxxx | x460 | xx | xxxxxx | xxxxxx | 441 |
| xxxx | x481 | 24 | xxxxxxx | x231361 | 576 |
| 1980 | xxx | xxx | xxxx | xxxxxxx | xxxx |
| 1981 | xxxx | xx | xxxxx | 263169 | xxxx |
| xxxx | x512 | xx | xxxxx | xxxxxx | xxx |
| xxxx | 526 | xx | xx7890 | xxxxxx | 225 |
| 1984 | xxx | xx | xxxxx | xxxxxxx | x1156 |
| 1985 | xxxx | x33 | xxxxx | xxxxxxx | x1089 |
| xxxx | xxx | xx | xxxxxxx | xxxxxxx | x1089 |
| 1987 | 645 | xx | xxxxxxx | xxxxxxx | xxxxx |
| 1988 | xxxx | x43 | 29025 | xxxxxx | x1849 |
| 1989 | xxx | 50 | xxxxx | 505521 | 2500 |
| xxxx | 719 | xx | 33793 | xxxxxx | xxxx |
| xxx | xxxxx | ||||
| Ʃy | x412 | ||||
| xxxx | xxxxxx | ||||
| xxxxxxx | xxxxxxxx | ||||
| Ʃ(y^2) | xxxxxx | ||||
| r | 0.9414772888 |
| xxxxxxx OUTPUT | ||||||||
| Regression Statistics | ||||||||
| xxxxxxxx x | xxxxxxxxxxxxx | |||||||
| x Square | 0.8863794853 | |||||||
| Adjusted x Square | 0.8769111091 | |||||||
| xxxxxxxx Error | xxxxxxxxxxxx | |||||||
| Observations | 14 | |||||||
| xxxxx | ||||||||
| df | SS | xx | x | xxxxxxxxxxxxx F | ||||
| Regression | 1 | x1711.9786630483 | x1711.9786630483 | 93.6147301596 | xxxxxxxxxxxxx | |||
| Residual | x12 | xxxxxxxxxxxxxx | x18.287492365 | |||||
| xxxxx | 13 | x1931.4285714286 | ||||||
| xxxxxxxxxxxx | xxxxxxxxx xxxxx | xx xxxx | xP-value | xxxxx xxx | xxxxxx 95% | xxxxx 95.0% | xUpper 95.0% | |
| Intercept | -41.4304389485 | 7.4122172282 | xxxxxxxxxxxxx | 0.0001180993 | -57.5802729415 | xxxxxxxxxxxxxxx | xxxxxxxxxxxxxx | xxxxxxxxxxxxxxx |
| X xxxxxxxx x | 0.1248616923 | 0.0129049736 | x9.6754705394 | x0.0000005109 | x0.0967441702 | xxxxxxxxxxxx | xxxxxxxxxxxxx | xxxxxxxxxxxxx |
| xxx 821 boats, x = | x61.0810104252 | |||||||
| Confidence interval at 95% confidence xxxxx | xxxxxxxxxxxxx | xxx | xxxxxxxxxxxxxx |
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xxx
xxx xxxxxxxxxx co-efficient xx calculated by xxx following xxxxxxxx (In xxxxxx I put in this xxxxxxx
and it calculates xx xxxxxxx xx xx xxxx xxxx a calculator).
Now, The correlation coefficient (rxy) xxxxx xx xxxxx means you xxx predict xxx xxxx xxxxx xxxx
xxx xxxxxx xxx xxxx the error xx xxxxxxxxxx is xxxxxxxxxx xxxxxx xx would xxxxxxxx that this test
is xxxxxxxxx
The regression equation is presented below xxx xx is xxxxxxxx in a xxxxxxx fashion to the
xxxxxxxxxxx coefficient. xxx xxxxxxxxxx xxxxxxxx xx used to xxxxxxx values xx the dependant
xxxxx xxx xxxx values of xxx Independent xxxxx (X). The xxxxxxx x and x stand xxx the xxxxx and
xxxxxxxxx xxxxxxxxxxxxx
xxx slope and xxxxxxxxx xxx xxxxxxxx xxxxx the two equations below.
xxx xxx
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xxxx
Correlation(r) = xxxxx x (ΣX)(ΣY) x xxxxxxxxxxx - xxxxxxx xxxx [NΣY2 - (ΣY)2])
x 10 (5980)-(525)(100)/ sqrt ({10(32,085)-(525)^2} Sqrt {10 (1192)-(100)^2})
= 59800 xxxxxxx sqrt ({320850-275625} Sqrt xxxxxxxxxxxxxx
=7300/sqrt (45225) Sqrt xxxxxx
xxxxxxxxxxxx x xxxxx
=0.78
xxxx
r^2 xxx be xxxxxxxxxxx as xxx percentage xx xxxxxxxx xx y that xx xxxxxxxxx for by xx r^2 = 0.61 xx say xxxx 61 percentage of xxx variance xx y is xxxxxxxxx for xx xxxxxxxxxxx in x.
section B
Q1 xx xxxxxxx y= xx
b) x x 5,4,3,2,1
c) x: 2,3,4,5,6
xx xxxxxxxxx
x | xy | xx^2 | xxxx | xx |
xxx | x13 | xxxxxx | x169 | x5811 |
xxx | x21 | xxxxxxx | 441 | xxxxx |
xxx | xx | x231361 | xxxx | xxxxx |
498 | x16 | xxxxxxx | 256 | x7968 |
513 | xxx | 263169 | 576 | 12312 |
xxx | xx | xxxxxx | x400 | xxxxxx |
526 | xxx | x276676 | xxxx | xxxx |
xxx | xxx | x312481 | xxxx | 19006 |
xxx | 33 | xxxxxx | xxxx | xxxxxx |
xxx | 33 | xxxxxxx | x1089 | x20262 |
645 | xxx | 416025 | x1521 | 25155 |
xxx | xxx | xxxxxx | x1849 | xxxxxx |
xxx | x50 | 505521 | xxxxx | x35550 |
xxx | xxx | 516961 | 2209 | xxxxx |
| x
|
| x
| x
|
7945 | 412 | xxxxxxx | 14056 | xxxxxx |
Q2 xxxxxxxxxxxxxx = xxxxx x xxxxxxxxxx x Sqrt([NΣX2 x (ΣX)2] xxxx xxxxxx - xxxxxxxx
xx xxxxxxxxxxxxxxxxxxxxx xxxx xxxxxxxxxxxxxxxxxxxxxx sqrt ( xxxxxxxxxxxxxxxxx
3465294-3273340/ xxxx xxxxxxxxxxxxxxxxxxxxxx xxxx x xxxxxxxxxxxxxxxxx
191954/sqrt(
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17.2
Correlation(r) x xxxxx - (ΣX)(ΣY) x Sqrt([NΣX2 x (ΣX)2] xxxx xxxxxx x (ΣY)2])
= 10 xxxxxxxxxxxxxxxxxx xxxx ({10(32,085)-(525)^2} xxxx xxx (1192)-(100)^2})
x 59800 xxxxxxx sqrt xxxxxxxxxxxxxxxx xxxx {11920-10000})
=7300/sqrt (45225) Sqrt xxxxxx
=7300/212.66 x xxxxx
xxxxx
17.3
r^2 can be xxxxxxxxxxx xx xxx percentage of variance in x that xx accounted xxx by xx xxx x 0.61 xx xxx xxxx xx percentage of the xxxxxxxx in x xx accounted for xx differences in xx
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