Using Microsoft Excel 2010 On The Heart Disease Mortality Data Rates Versus The Total Hardness

In the Chemistry of Natural Waters Lab we were to collect a sample of water, ranging from a fountain, stream, bottle, or tap water. After we collected the samples we all did many tests to see what the hardness was for each one. Water hardness is determined by the amount of Calcium and Magnesium in the water.(2) Water that has more Calcium or Magnesium is considered to be harder than water with less of those two elements. When you use soap and detergent, this is where you see water hardness coming into play in everyday life when you are washing things.

Information about statistical significance and confidence interval is presented and reviewed. There was good use of tables and figures that included titles and headings that were clearly and appropriately labeled. The results were also clearly displayed in tables with identifiable titles and labeled headings. The study included descriptive statistics. The study described the main characteristics in the dataset. The mean and standard deviation for each blood pressure measurement was calculated before and after crossing of the legs was performed by the study subjects. Inferential statistics were also present in this study. In order to test mean differences with three or more groups, an analysis of variance (ANOVA) statistical test is used. This research study conducted a repeated-measure ANOVA, which is when there are three or more measures of the same dependent variable

The results for the “Total Hardness” test varied between 20 ppm -128 ppm. The “Total Alkalinity” results varied between 28 ppm – 84 ppm. The results for the “Iron” test were between 0 ppm – 1 ppm. For the “Chloride” the results were as low as 20 ppm and went all the way to 8,000 ppm, but for most part the results were from 20 ppm – 60 ppm. The “Color in Water Low Range” test showed that the max APHA Color was 20 and the lowest APHA Color was 0, which is a great result. Results for the “Turbidity Column” test were Not Applicable (N/A). For both the “Chromate” and “Copper” test the results were 0 ppm. The results for “Zinc” varied between 0 ppm – 2

I will be applying descriptive statistics to visually display data and calculate measures of central tendency and dispersion to effectively pin point patterns of injury, what medications we give, and what topics to educate our Soldiers to prevent illness and injury. As this course comes to an end I have reflected on the course outcomes and determined that I have successfully achieved the intended learning objectives in the five Modules. I have demonstrated the ability to describe statistics, determine sources and users of Health Statistics. I have been able to identify and demonstrate the importance of the Central Limit Theorem to the study of Health Statistics. As mentioned in my reflective discussion posting I took special interest in the ability to demonstrate the ability to compute descriptive statistics in order to describe the characteristics of a data set as well as applying elementary probability theory to health sciences data. I also mentioned earlier that the ability to conduct parametric and nonparametric inferential statistical tests: t-tests, chi-square analyses proved to be the most difficult for me to learn. Overall this course was very useful for me and will improve the way I run my medical company here in Kuwait. I believe our work here will improve the quality of care through statistical review of our standard operating procedures and protocols in the aid station. I appreciate the learning opportunity this course offered and the feedback

Statistical modeling and analysis were performed using SAS software, version 9.3 (SAS Institute Inc). Matlab version 8.6 (The MathWorks Inc., Natick, MA, USA) was used for risk chart generation and model

| Based on explicit knowledge and this can be easy and fast to capture and analyse.Results can be generalised to larger populationsCan be repeated – therefore good test re-test reliability and validityStatistical analyses and interpretation are

In the following exploratory data analysis, of the relevant variables, it is our hope to identify the best preforming drug within the groups (groups A, group B, group C). The exploratory analysis provides a view of how each group performed over the course of 12 weeks. In figure 1, below is the descriptive table. The descriptive table provides a view of all variables associated with the HDL/LDL drug study. The output with the descriptive table contains the overall Norman clashers of the study, there is a section identified as group, statistics, and standard error. The section labeled group; it identifies the group of participants in the study. In addition, statistics are provided in the form of the mean scores for each group as well as the number of participants and error associated with each group (some data isn’t visible due to pivot table activation).

The blue bars are for group one’s values, tend to be the highest. Red is for group two, with gray being for group Pooja, and yellow being for group Bob. No groups had any growth for Pseudocel media. There was no growth for group two in the Columbian sheep agar, as well as none for mannitol salt. The only growth in Mannitol salt was for Pooja. Blood agar was the only to have growth for all three, with Wilkin-Chalgren that selected for anaerobes to only have growth in group one and two. The standard deviation for each group is applied to the bars. For the hypotheses group two and four claimed that they used mouth wash regularly and group one and three never used

The purpose of this paper is to speak about statistical data of heart disease in Hartford Connecticut.

Blood pressure: Blood pressure can be an important factor for predicting the mortality of patients. In most of the cases, the blood pressure of the patients is generally recorded when the patients are admitted to the hospitals. While conducting a study in UAE regarding factors affecting mortality it was observed that patients who died had significantly lower GCS and systolic blood pressure on arrival to hospital [6]. As per the study it was established that blood pressure was a significant factor for predicting the mortality. A study conducting in Stockholm it was observed that low blood pressure is directly related to mortality. Univariate chi-square tests showed that advanced age, male sex, lower education, institutionalization, cancer,

Now a days, due to change in lifestyle, the problem of heart disease has become very common. Many people lose their lives since they are not familiar with the reasons associated with the heart diseases. According to the fact sheet of WHO the 10 leading causes of death in the world during the year 2000 to 2012 where Ischaemic heart disease, stroke, lower respiratory infections and chronic obstructive lung disease have remained the top major killers during the past decade. Also it is mentioned that 7.4 million deaths are due to Ischaemic heart disease which is at first position of causes of death.

Low pH (4.6), the optimum level between 4.8 and 7.8 (CaCl2). Low because of the white clover

The purpose of this lab is to determine whether a liquid is hard water or not.

This study utilized the Worchester Heart Attack Study data and R Studio software to predict the mortality factors for heart attack patients. The medical data include physiological measurements about heart attack patients, which serve as the independent variables, such as the heart rate, blood pressure, atria fibrillation, body mass index, cardiovascular history, and other medical signs. This study employed the techniques of supervised learning and unsupervised learning algorithms, using classification decision trees and k-means clustering, respectively. In addition to performing initial descriptive statistics to estimate the general range of critical factors correlated with heart attack patients, R Studio was used to determine the weight of each of the significant factors on the prediction in order to quantify its influence on the death of heart attack patients. Furthermore, the software was used to evaluate the accuracy of the predicted model to estimate death of heart attack patients by using a confusion matrix to compare predictions with actual data. Finally, this study reflected on the effectiveness of the data mining software conclusions, compared supervised learning and unsupervised learning, and conjectured improvements for future data mining investigations.

Table 3: Hardness readings of four sample hardness test results in 10 minutes after quenching into water