In: Statistics and Probability
Use SPSS to create a bivariate regression equation where “LowBirthweight” is the dependent variable and “UrbanPop” is the independent variable. The variable “LowBirthweight” assesses the percentage of children born at what is considered “below normal” weight. The variable “UrbanPop” measures the percentage of people in each country who live in cities. The SPSS output for this regression is:
Model Summary |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.309a |
.095 |
.087 |
5.204 |
a. Predictors: (Constant), UrbanPop: Percentage of Population in Cities
Coefficientsa |
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Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
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B |
Std. Error |
Beta |
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(Constant) |
14.117 |
1.293 |
10.920 |
.000 |
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UrbanPop: Percentage of Population in Cities |
-.070 |
.021 |
-.309 |
-3.280 |
.001 |
a. Dependent Variable: LowBirthweight: Percentage Children Low Birthweight
When the percentage of Population in Cities is constant, percentage Children Low Birthweight will increase by 14.117, on average.
For every additional percentage of Population in Cities, percentage Children Low Birthweight will decrease by 0.070.
The hypothesis being tested is:
H0: β1 = 0
H1: β1 ≠ 0
The p-value is 0.001.
Since the p-value (0.001) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that the regression equation is statistically significant.
Yes, because as the percentage of Population in Cities increases, percentage of Children Low Birthweight decreases.