In: Statistics and Probability
Researchers want to examine whether or not weight can predict systolic blood pressure. They collect weight in pounds and blood pressure measurements for 31 adult men. For your convenience, I have prepared an Excel file with the data :
Weight | Systolic Blood Pressure |
162 | 134 |
163 | 149 |
167 | 128 |
168 | 129 |
173 | 128 |
173 | 118 |
174 | 133 |
175 | 138 |
184 | 143 |
184 | 147 |
185 | 144 |
218 | 137 |
186 | 149 |
188 | 137 |
235 | 138 |
189 | 150 |
189 | 128 |
194 | 153 |
195 | 154 |
196 | 120 |
196 | 154 |
177 | 159 |
207 | 134 |
209 | 160 |
211 | 162 |
211 | 143 |
212 | 164 |
217 | 166 |
182 | 167 |
220 | 168 |
221 |
169 |
Test the relationship between weight and systolic blood pressure at the α = 0.05 level. While doing the calculations, leave four decimal places as appropriate. Round your final answer to two decimal places.
How do you interpret this finding? For every one unit increase in weight, there is a corresponding ___________ ______________ in systolic blood pressure.
Based on this information, the researcher should make the decision to ___________.
What percentage of variance in systolic blood pressure is explained for by weight?
What is the value for the correlation between weight and systolic blood pressure?
What is the best predicted value for systolic blood pressure given that a man weighs 175 pounds?
Here the dependent variable y = Systolic Blood Pressure
independent variable x = weight
To find the relationship between X and Y Goto Data in menu bar ---> Data Analysis ---> Regression
Now enter the weights column in X
and Systolic BP column data in Y.
select labels option and then click on Ok.
The results are displayed in new wor book.
SUMMARY OUTPUT
Regression Statistics | |
Multiple R | 0.456 |
R Square | 0.208 |
Adjusted R Square | 0.181 |
Standard Error | 13.308 |
Observations | 31 |
ANOVA | |||||
df | SS | MS | F | Significance F | |
Regression | 1 | 1347.582 | 1347.582 | 7.608485 | 0.009953 |
Residual | 29 | 5136.354 | 177.1156 | ||
Total | 30 | 6483.935 |
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 78.8608 | 24.1898 | 3.2601 | 0.0028 | 29.3872 | 128.3344 |
Weight | 0.3453 | 0.1252 | 2.7583 | 0.0100 | 0.0893 | 0.6013 |
Slope coefficient b = 0.3453
For every one unit increase in weight, there is a corresponding 0.3453 unit increase in systolic blood pressure.
Based on this information, the researcher should make the decision to Reject H0 and conclude that there is a significant linear relation between X and Y.
What percentage of variance in systolic blood pressure is explained for by weight? = R2 = 0.208
20.8% of variance in systolic blood pressure is explained for by weight.
What is the value for the correlation between weight and systolic blood pressure?
Sqrt(R2) = 0.456
What is the best predicted value for systolic blood pressure given that a man weighs 175 pounds?
Y = 78.8608 + 0.3453 *X
for X = 175
Y = 78.8608 + 0.3453 *175 = 139.2883.