Analysis Essay Example
Analysis Essay Example – In the research, 100 patients were treated with four different drugs to test their effects on blood pressure. An equal number of 25 patients were treated with each drug, which were: placebo (designated control in this analysis), drug A, drug B, and drug A and B together.
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Analysis Essay Example
In the test, several statistical tests were performed to extract the results. Bar graphs were drawn to show descriptive statistics, Shapiro wilk test to investigate whether variables were normally distributed, histograms to show blood pressure grouped by drug type both at BP and afterwards. The scatterplot was also drawn to verify the correlation.
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In Figure 1a, the mean age of all patients was 65 years with a standard deviation of 10.9 (approximately 11 years). This means that many of the patients were between 54 and 76 years old. The mean arterial pressure of all patients before the test was 140.65 mm Hg with a standard deviation of 16.516 mm Hg. This indicates that most patients had blood pressure ranging from 124 mm Hg to 156 mm Hg. (fig. 2a). The above finding shows that there was a significant difference in the characteristics of the pre-test subjects, as the descriptive statistics of age and pre-test BP were not significant. Another difference is that most of the patients tested had prehypertension, as their BP was greater than 120 mm Hg and slightly greater than 140 mm Hg. However, there was a similarity in the fact that in both figures (1a, 2a) a normal curve was perfectly fitted, 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 BMI with a slight increase in age associated with a slight increase in BMI.
Histogram analysis depicts drug effects. Figures (3b and 3c) show that of those tested with the placebo drug (control), before the test, 15 patients had BP between 120-150 mm Hg. After the test, BP levels increased further with more patients recording a BP of 120-180 mm Hg. In the pretest of patients tested with drug A, about 16 patients had BP between 130-150 mm Hg. In the post-test, this number was even higher, with more than 19 patients registering BP between 130-160 mm Hg and another 4 registering BP above 160 mm Hg.
Before the test, 20 patients tested with drugs A and B had BP between 120-160 mm Hg. 4 patients had a BP of 160-180 mm Hg, with the exception of one patient who had a BP of 100 mm Hg. After the application of combined drugs A and B, the BP of many patients was reduced, with 24 patients having a BP of 90-150 mm Hg. Of these, 17 had BP ranging between 120 and 150 mmHg. Before the test, 21 patients had a BP of 130-160 mmHg from those tested with drug B. In the application of the drug, the BP of 12 patients increased above 150 mmHg and 6 patients recorded BP above 180 mmHg.
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After analyzing the data, it is prudent to reach a conclusion derived from a set of statistical procedures used in the study. For this case, bar graphs were drawn to show the descriptive statistics. Bar graphs were then used to obtain a histogram which was important for showing the distribution of blood pressure by drug type during and after BP. After grouping, the Shapiro-Wilk test was used to check for normal distribution of variables. Thus, it was concluded that there is a normal distribution. Using this, multiple comparison tests on the normally distributed histogram showed that an improvement in BP levels is observed when drug A and drug B are combined for some of the patients. This was seen in 17 of the 20 patients tested. This was also evident when scatterplots were used to test the correlation between improvement and drug combination in BP levels. Using the above procedure, it is statistically correct to state that the combination of drugs A and B leads to a return to normalcy in patients with BP problems.
To obtain the results, several statistical procedures were performed, including regression analysis used to identify the relationship between improvement in bench press, session volume and session intensity. Scatter plot to investigate the correlation effect of lifters’ weight on the relationship between improvement, session volume, and session intensity.
The average intensity of the session found was 85.046 with a standard deviation of 5.5745. The average session volume was 2,931.002 with a standard deviation of 612.6289 (Table 1a). The overall mean improvement was 24.786 with a standard deviation of 5.9612.
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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 bench press improvement. To identify which of the two predictors makes the greatest contribution, we consider the standardized column of the Beta coefficient. Average session volume is the strongest predictor of outcome as it has a higher Beta value (0.280) than session intensity (0.148).
Adjusted R-square (0.45) suggests that predictor variables could be used to explain 45% of the variation in bench press improvement. (Table 1b). The appropriate model for making future predictions can be deduced from Table 2a, which is:
The scatterplot demonstrates that the lifter’s weight affects the ratio as shown in Table 3a. The relationship changes rapidly when overweight is studied with weight change associated with high improvement. The weight below 85 Kgs does not explain much the change in the ratio. However, Pearson’s moment correlation coefficient R2 = 0.2999 suggests that weight can explain only 29.99% of the variation in the relationship between improvement, session volume and session intensity.
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From the analysis of the data, a conclusion for the study can be reached. The following statistical procedure was used to arrive at the conclusion. First, it was necessary to perform a regression and correlation test using the SPSS software. The SPSS output provided information on the mean and standard deviation of the three variables: session volume and session intensity and bench press improvement. Using the information derived from the output, regression analysis showed that there was a significant relationship between the three variables. The scatterplot was important in helping to identify whether the lifter’s weight has an impact on all three variables and this proved to be true. Using the information in the Beta Coefficient column, it was concluded that average session volume is the strongest predictor of outcome based on high Beta value compared to session intensity. The combination of the above statistical procedures was therefore an important factor in the conclusion-making process.¶
Table 2a; showing the model coefficients in the regression analysis to verify the relationship between improvement, session volume and intensity.
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Poetry Analysis Essay Examples
Abuse Analysis Art Career Accounting Business Behavior Communication Community Corporate Culture Developmental Disability Developmental Disability Education Environment Family Finance Government History Literature American Literature History Literature Health and Social Care Other Politics Psychology Philosophy Research Religion Strategy Students Sociology Science Security Society Technology United States World Teaching literary analysis is fundamental to teaching ELA in high school. Students should be able to build thematic arguments and demonstrate them through textual evidence. But this process can be daunting for students and teachers alike. It can be confusing and frustrating figuring out where to start and how to sort instructions. We all know that scaffolding is the key to student success, and this same concept applies to teaching literary analysis. The Mega Pack Bespoke ELA Literary Analysis follows the same basic structure described below.
The first step is to start with the templates. Students should see examples of final products before attempting to write anything. This concept is not just limited to the literary analysis essay, but applies to the writing of any type of essay. The more examples and models students see before they begin the writing process, the more they understand the process and expectations. However, students must go beyond passively looking at samples. They have to take two extra steps =
As students read samples and models, they should make a list of their observations. Here are some guiding questions for the observation process:
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After students have gathered their observations, the next step is for students to create a rubric
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