Participant Characteristic

Published: 2019-10-15 08:00:00
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In the research, 100 patients were randomly treated using four different drugs to test their effects on their blood pressure. An equal number of 25 patients were treated with each drug, which were: placebo (assigned Control in this analysis), drug A, drug B, and drug A and B together.

b. Method

In the test various statistical tests were performed to draw the results. Bar graphs were drawn to display the descriptive statistics, Shapiro wilk test to investigate whether the variables were normally distributed, histograms to show blood pressure grouped by the type of drug in both Pre and Post BP. The scatter plot to check the correlation was also drawn.

Statistical Analysis

In fig 1a the mean of all the patients was 65 years with a standard deviation of 10.9 (approximately 11 years). This means that many of the patients were aged between 54 76 years. The mean blood pressure of the total patients prior to the test was 140.65 mm Hg with a standard deviation of 16.516 mm Hg. This indicates that most patients had a blood pressure ranging from 124 mm Hg 156 mm Hg. (fig 2a). The above finding demonstrates that there was significant difference in the subject characteristics prior to the test since the descriptive statistics in age and pre Test BP could not much. Another difference is that most of the patients under the test had a pre-hypertension since their BP was over 120mm Hg and slightly higher than 140mm Hg. However, there was a similarity in that both figures (1a, 2a) a normal curve was perfectly placed meaning that both age and Pre Test BP were normally distributed. This qualifies the data to be investigated using various statistical tools. Figure 3a shows that there was a weak correlation between age and the BMI with slight increase in age being associated with slight increase in BMI.

The analysis of Histogram outlines the effects of the drugs. Figures (3b and 3c) shows that out of those that were tested with the placebo (control) drug, before the test 15 patients had a BP between 120-150mm Hg. After the test, the BP levels went even higher with more patients recording a BP of 120-180mm Hg. In the pre Test of the patients tested with Drug A, around 16 patients had a BP of between 130-150mm Hg. In the post test this figure went even higher with over 19 patients recording a BP between 130-160mm Hg and another 4 recording a BP above 160mm Hg.

Before the test 20 patients of those that tested with drug A and B, had BP between 120-160mm Hg. 4 patients had a BP of 160-180mm Hg with an exception of one patient who had 100mm Hg. After application of the drug A and B combined, the BP of many patients lowered with 24 patients having a BP of 90-150mm Hg. Out of these, 17 had a BP ranging from 120- 150mmHg. Before the test, 21 patients had a BP of 130-160mmHg out of those that were tested with drug B. Upon applying the drug the BP of 12 patients hiked to above 150mmHg with 6 patients recording a BP of over 180 mmHg.

Conclusion

Following the analysis of data, it is prudent to come up with a conclusion that is derived from a set of statistical procedures used in the study. For this case, bar graphs were drawn to display the descriptive statistics. The bar graphs were then used to come up with a histogram that was important to show the distribution of the blood pressure by the type of drug in both the Pre and Post BP. After the grouping, the Shapiro wilk test was used to check whether there is a normal distribution for the variables. It was thus concluded that there is a normal distribution. Using this, multiple comparison tests on the normally distributed histogram showed that there is an improvement noted on the BP levels when drug A and drug B are combined for some of the patients. This was noted in 17 out of the 20 patients tested. This was also evident when the Scatter plots were used to test the correlation between the improvement and the combination of the drugs on the BP levels. Using the above procedure, it is thus statistically correct to state that combining drug A and B leads to a return to normalcy for patients with BP problems.

APPENDIX

Bench press study: Data Analysis

Participant Characteristics

The study consists of 50 trained power lifters selected randomly for the purpose of the study.

Method

To obtain the results, several statistical procedures were carried out which included Regression analysis used in identifying the relationship between the bench press improvement, session volume and session intensity. The scatter plot was used to investigate the correlation effect of the lifters weight on the existing relationship between improvement, session volume and session intensity.

Statistical Analysis

The average session intensity mean was found to be 85.046 with a standard deviation of 5.5745. Average session volume mean was 2931.002 with standard deviation of 612.6289 (table 1a). The improvement overall mean was 24.786 with a standard deviation of 5.9612.

From table 2a, there is an existing relationship between the three variables. The predictor variables which are session volume and session intensity, greatly affect the outcome dependent variable which is the bench press improvement. To identify which of the two predictors make the greatest contribution, we consider the standardized Beta coefficient column. The average session volume is the strongest predictor of the outcome since it has a higher Beta value (0.280) than the session intensity (0.148).

The Adjusted R Squared (0.45) suggests that the predictor variables could be used to explain 45% variance of the bench press improvement. (Table 1b) The model fitted to make future prediction can be deduced from table 2a which is:

Y= 3.320 + 0.159X1 + 0.003X2

Where X1 = average session intensity

X2 = average session volume

Y = the dependent variable

The scatter plot demonstrates that weight of the lifter affects the relationship as shown in Table 3a. The relationship changes rapidly when overweight is studied with change in weight associated with high improvement. The weight under 85 Kgs does not explain much the change in relationship. However, the Pearson moment correlation coefficient R2 = 0.2999 suggests that weight can only explain 29.99% variance of the relationship between improvement, session volume and session intensity.

Conclusion

Based on the analysis of the data, it was possible to come up with a conclusion for the study. The following statistical procedure had been used to come up with the conclusion. First it was necessary to conduct a regression and correlation test using the SPSS software. The SPSS output provided information about the mean and the standard deviation of the three variables: session volume and session intensity and bench press improvement. Using the information derived from the output, the regression analysis showed that there was a significant relationship between the three variables. The scatter plot was important in helping to identify whether the lifters weight has an impact on the three variables and this was proven to be true. Using the information from the Beta Co-efficient column, it was concluded that average session volume is the strongest predictor of the outcome based on the high Beta value compared to the session intensity. The combination of the above statistical procedures was thus an important factor in the conclusion making process.

THE APPENDIX

Descriptive Statistics

Mean Std. Deviation N

Improvement 24.786 5.9612 50

Average Session Intensity 85.046 5.5745 50

Average Session Volume 2931.002 612.6289 50

Table 1a; showing descriptive statistics of improvement, session volume and intensity

Table 1b; model summary showing the regression of improvement, session volume and intensity.Coefficients

Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations

B Std. Error Beta Zero-order Partial Part

1 (Constant) 3.320 14.315 .232 .818 Average Session Intensity .159 .152 .148 1.043 .302 .094 .150 .146

Average Session Volume .003 .001 .280 1.965 .055 .251 .276 .274

a. Dependent Variable: Improvement

Table 2a; showing the coefficients of the model in regression analysis to check the relationship between improvement, session volume and intensity.

Reference

Donnelly, R., & Kelley, W. M. (2009). The humongous book of Statistics Problems. Penguin.

sheldon

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