unit Business
hootannie
example_unit_7.docx1. I chose the Base Ball values data set. The two numerical values are revenue and value.
2.
3. y = 7.7876x - 841.88 R² = 0.95918
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Linear Regression |
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Regression Statistics |
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R |
0.97953 |
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R-Squared |
0.95947 |
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Adjusted R-Squared |
0.95797 |
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S |
15.15335 |
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MSE |
6,199.85054 |
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RMSE |
78.73913 |
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PRESS |
7,013.21121 |
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PRESS RMSE |
15.55104 |
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Predicted R-Squared |
0.95415 |
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N |
29 |
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245 = 113.94496 + 0.12333 * 1000 |
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ANOVA |
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d.f. |
SS |
MS |
F |
p-value |
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Regression |
1. |
146,775.94257 |
146,775.94257 |
639.20097 |
0. |
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Residual |
27. |
6,199.85054 |
229.62409 |
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Total |
28. |
152,975.7931 |
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Coefficient |
Standard Error |
LCL |
UCL |
t Stat |
p-value |
H0 (5%) |
VIF |
TOL |
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Intercept |
113.94496 |
6.52285 |
100.56117 |
127.32875 |
17.46857 |
0. |
rejected |
** |
** |
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1000 |
0.12333 |
0.00488 |
0.11332 |
0.13334 |
25.28242 |
0. |
rejected |
** |
** |
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T (5%) |
2.05183 |
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4. There is a strong correlation
Revenue vs Value
Value 245 370 227 207 254 175 231 304 223 508 202 250 188 266 226 211 267 302 227 214 403 188 226 263 265 229 294 225 387 287 1000 2100 975 825 1125 800 700 1300 895 3200 725 1100 625 1220 870 840 1150 1800 885 855 2400 650 875 1350 1250 900 1400 890 2000 1280
