Interpret the results (based on the regression output).
Temp | Humidity | MPG |
27.0 | 18 | 8.4 |
28.0 | 68 | 10.7 |
32.5 | 59 | 12.4 |
39.0 | 69 | 12.8 |
45.9 | 35 | 9.4 |
57.8 | 10 | 9.9 |
58.1 | 33 | 10.8 |
62.5 | 74 | 12.5 |
88.5 | 34 | 14.4 |
49.0 | 57 | 10.8 |
45.9 | 18 | 9.4 |
17.0 | 46 | 8.4 |
48.0 | 55 | 11.7 |
32.5 | 68 | 11.4 |
31.5 | 58 | 15.2 |
88.5 | 24 | 14.9 |
25.9 | 65 | 9.4 |
45.9 | 15 | 12.4 |
32.5 | 59 | 12.4 |
39.0 | 69 | 12.8 |
45.9 | 35 | 9.4 |
49.0 | 57 | 10.8 |
a. b.
Corr( MPG, Temp) = 0.4795
Corr ( MPG, Humidity) = 0.2413
There is positive correlation for both independent varianle with dependent variable.
The test for significance of correlation,
H0: r= 0 vs Ha: r=\ 0
Test statistic, t = r*√(n-2) /√(1-r^)
P value for test of correlation coefficient between MPG and Temperature is 0.0239 < 0.05. Thus it may concluded at 5% los that correlation between MPG and Temp is significant.
P value for test of correlation coefficient between MPG and Humidity is 0.2793 > 0.05. Thus it may concluded at 5% los that correlation between MPg and humidity is not significant.
C.
The fitted model, y = 6.0406+ 0.0708x1+ 0.0460x2 explain 42.9% variation in MPG.
For unit increase in temperature, MPG increases by 0.0708 and for unit increase in humidity, MPG increases by 0.0490
run a scatter plot for each of the two independent variables (IVs). The IVs are Temperature...
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