In: Statistics and Probability
Individual | Bettendorf Salary | Experience (X1) | Education (X2) | Sex (X3) |
1 | 53600 | 5.5 | 4.0 | F |
2 | 52500 | 9.0 | 4.0 | M |
3 | 58900 | 4.0 | 5.0 | F |
4 | 59000 | 8.0 | 4.0 | M |
5 | 57500 | 9.5 | 5.0 | M |
6 | 55500 | 3.0 | 4.0 | F |
7 | 56000 | 7.0 | 3.0 | F |
8 | 52700 | 1.5 | 4.5 | F |
9 | 65000 | 8.5 | 5.0 | M |
10 | 60000 | 7.5 | 6.0 | F |
11 | 56000 | 9.5 | 2.0 | M |
12 | 54900 | 6.0 | 2.0 | F |
13 | 55000 | 2.5 | 4.0 | M |
14 | 60500 | 1.5 | 4.5 | M |
What is the “slope” of the linear relationship?
Select one:
a. 425
b. 223.0
c. 498.0
d. .305
2. What can you conclude about the relationship?
Select one:
a.
There is a strong negative relationship between the data.
b.
There is a weak positive relationship between the data.
c.
There is a strong positive relationship between the data.
d.
There is a weak negative relationship between the data.
What is the “slope” of the linear relationship?
b. 223.0
2. What can you conclude about the relationship?
b. There is a weak positive relationship between the data.
r² | 0.036 | |||||
r | 0.190 | |||||
Std. Error | 3540.188 | |||||
n | 14 | |||||
k | 1 | |||||
Dep. Var. | Bettendorf Salary | |||||
ANOVA table | ||||||
Source | SS | df | MS | F | p-value | |
Regression | 56,17,003.7047 | 1 | 56,17,003.7047 | 0.45 | .5159 | |
Residual | 15,03,95,139.1524 | 12 | 1,25,32,928.2627 | |||
Total | 15,60,12,142.8571 | 13 | ||||
Regression output | confidence interval | |||||
variables | coefficients | std. error | t (df=12) | p-value | 95% lower | 95% upper |
Intercept | 55,613.5041 | |||||
Experience (X1) | 223.0234 | 333.1381 | 0.669 | .5159 | -502.8223 | 948.8691 |