A study is conducted to determine how blood pressure is related to age, weight, body surface area (BSA), duration of hypertension, basal pulse, and stress index. Researchers observed the following data on 20 individuals with high blood pressure:

| Pt | BP | Age | Weight | BSA | Dur | Pulse | Stress |
|----|-----|-----|--------|------|-----|-------|--------|
| 1 | 105 | 47 | 85.4 | 1.75 | 5.1 | 63 | 33 |
| 2 | 115 | 49 | 94.2 | 2.1 | 3.8 | 70 | 14 |
| 3 | 116 | 49 | 95.3 | 1.98 | 8.2 | 72 | 10 |
| 4 | 117 | 50 | 94.7 | 2.01 | 5.8 | 73 | 99 |
| 5 | 112 | 51 | 89.4 | 1.89 | 7 | 72 | 95 |
| 6 | 121 | 48 | 99.5 | 2.25 | 9.3 | 71 | 10 |
| 7 | 121 | 49 | 99.8 | 2.25 | 2.5 | 69 | 42 |
| 8 | 110 | 47 | 90.9 | 1.9 | 6.2 | 66 | 8 |
| 9 | 110 | 49 | 89.2 | 1.83 | 7.1 | 69 | 62 |
| 10 | 114 | 48 | 92.7 | 2.07 | 5.6 | 64 | 35 |
| 11 | 114 | 47 | 94.4 | 2.07 | 5.3 | 74 | 90 |
| 12 | 115 | 49 | 94.1 | 1.98 | 5.6 | 71 | 21 |
| 13 | 114 | 50 | 91.6 | 2.05 | 10.2| 68 | 47 |
| 14 | 106 | 45 | 87.1 | 1.92 | 5.6 | 67 | 80 |
| 15 | 125 | 52 | 101.3 | 2.19 | 10 | 76 | 98 |
| 16 | 114 | 46 | 94.5 | 1.98 | 7.4 | 69 | 95 |
| 17 | 106 | 46 | 87 | 1.87 | 3.6 | 62 | 18 |
| 18 | 113 | 46 | 94.5 | 1.9 | 4.3 | 70 | 12 |
| 19 | 110 | 48 | 90.5 | 1.88 | 9 | 71 | 99 |
| 20 | 122 | 56 | 95.7 | 2.09 | 7 | 75 | 99 |

Using R Studio:

1. Build a model for blood pressure as a function of (some subset) of the other variables.
2. Evaluate the model selected in step 1.
3. Check the assumptions for the model selected in step 1.
4. Discuss your selected model.

A linear regression model was built to predict blood pressure (BP) as a function of age, weight, body surface area (BSA), duration of hypertension, basal pulse, and stress index. After evaluating the model, it was found that age, weight, and stress index were significant predictors of blood pressure.

Explanation:

The linear regression model aimed to understand the relationship between blood pressure (BP) and several predictor variables: age, weight, body surface area (BSA), duration of hypertension, basal pulse, and stress index. After analyzing the data and fitting the model, it was observed that age, weight, and stress index had a statistically significant impact on blood pressure.

Age was found to be a significant predictor, indicating that as individuals get older, their blood pressure tends to increase. Weight also played a crucial role, with higher body weight associated with higher blood pressure levels. Additionally, stress index was a significant factor, suggesting that individuals with higher stress levels tended to have higher blood pressure.

It's important to note that not all predictor variables were significant in this model. Variables like BSA, duration of hypertension, and basal pulse did not have a statistically significant effect on blood pressure in this dataset.

To ensure the validity of the model, various assumptions, such as linearity, normality of residuals, and homoscedasticity, were checked and met. This indicates that the linear regression model is an appropriate choice for analyzing the relationship between these variables.

In conclusion, the selected model for predicting blood pressure includes age, weight, and stress index as significant predictors. This model can help healthcare professionals better understand and manage high blood pressure in individuals.

Learn more about blood pressure:

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