Statistic discussion response 5

Statistic discussion response 5

please respond to these discussions with a reference

Discussion 1

There is absolutely a correlation (relationship or association) between the number of cigarettes smoked and the pulse rate, but not necessarily stating causation. It is possible that smoking cigarettes cause the heart rate to go up. However, there are many variables to consider before reaching the conclusion of cigarettes cause the pulse rate to increase. Such variables that need to be considered are: age, gender, # and type of cigarettes, duration or history of smoking, and other health conditions such as Atrial Fibrillation, Anxiety, Asthma etc. These variables can differ from one another and affect the outcome. Without considering these factors, it is not proven that cigarettes are the only reason, causing the heart rate to increase. In this given scenario, linear correlation is depicting the strength of the relationship among these two continuous variables (cigarette and pulse rate). It is extremely difficult to control and randomize all relevant factors, but they do help us explore casual relationships. Many cause, and effect relationships like in this discussion topic are detected through linear correlation due to more than one variable is needed to predict the outcome of another variable (Huber, 2007). Of course, there is an association between cigarettes smoked and the pulse rate increases. If cigarettes contain nicotine, it produces adrenaline which makes the heart rate go faster (British Heart Foundation, n.d.). When stating null hypothesis, there is no relationship between cigarettes smoked and the pulse rate, the correlation is 0. The alternative hypothesis is the opposite of the null hypothesis, stating there is a relationship (positive, negative, curvilinear relationship) between cigarettes smokes and the pulse rate, the correlation is not 0. For the calculation, we need to find the mean and deviation scores of both of these variables. After that, squaring each of the deviations scores will help us get rid of the negative numbers, then take the sum of the squared deviation scores and sum of cross products to reach the Pearson’s r (correlation coefficient- could be weak, strong, or non- existent). Pearson’s r will be necessary to measure the linear relationship between two interval/ratio level variables. Conducting a hypothesis test will determine the significance of the test or an experiment. Therefore, the concept of regression is necessary to accurately measure or predict the value for one variable given a value of another variable.

References:

British Heart Foundation (n.d.). Smoking. Retrieved May 28, 2018, from

https://www.bhf.org.uk/heart-health/risk-factors/s…

Grove, S. K., Cipher, D. J. (2017). Statistics for Nursing Research: A Workbook for Evidence-

Based Practice, 2nd Edition. Retrieved from https://pageburstls.elsevier.com/#/books/978032335…

Discussion 2

A linear correlation is the strongest association between two variables meaning one variable has an effect on the other. If the association is positive, when variable an increase so does variable b. If the association is negative, when variable an increase, variable b decreases. Correlation is showing the relationship between two variables. While it is tempting to think something with a strong correlation results in causation, even cigarette use and increased heart rate, it does not. An example would be a linear correlation between hot weather and increased drowning events. Hot weather surely does not cause drowning, however, hot weather is when people are more likely to be in the water, thus increasing risk of drowning events. In order for causation to be proved a randomized study would need to be conducted to look at any other factors that may play into the scenario. For example if there is a strong correlation between number of winnings in a sport team while playing in their hometown, we would need to conduct a random study to see what factors could be playing into the success of the team while playing a home game. Factors could be increased crowd and the cheers are adding to confidence, comfort of their own playing field resulting in less anxiety, and the list goes on. Correlation does not always result in causation. More testing needs to be done to determine causation.

References

Green, N. (2012, January 06). Correlation is not causation | Nathan Green’s S word. Retrieved from https://www.theguardian.com/science/blog/2012/jan/…

D. (2015, January 14). Proving causation. Retrieved from https://learnandteachstatistics.wordpress.com/2013…

Discussion 3

Correlation is used to study the relationship between 2 variables (Grand Canyon University, 2013). In a positive correlation, the data will go in an upward direction and to the right. In positive correlations, as one variable increases so does the other (Grand Canyon University, 2013). In a negative correlation, the data will go downward and to the right. When we have a negative correlation, as one variable decreases, the other increases. With correlation, we are showing a relationship between two variables but not a cause and effect relationship (Grand Canyon University, 2013). A linear regression does show a cause and effect relationship between data. In linear regression variables do not necessarily show relationships or similar characteristics but rather the variables explain and respond to one another (Grand Canyon University, 2013). One of the variables will explain the other variable while the second variable will respond to the explanatory variable (Grand Canyon University, 2013).

In reading the discussion question and the explanation and conclusion, it seems that it is an assumption that all cigarettes increase pulse rate. However, the sentence before that talks about how as the number of cigarettes smoked increased then the pulse rate increased. We can gather from this that it is not the fact that the cigarette is being smoked but rather how many are being smoked when we see an increased heart rate. We can see here that there is a positive correlation with the amount of cigarettes smoked and heart rate because as one increases so does the other.

Grand Canyon University. (2013). Discovering Relationships and Building Models. Lecture

Discussion 4

The error in the conclusion, cigarettes cause the pulse rate to increase. Linear correlation means that the there is a line represented on a graph by the data received. In the variables that have been given to us, there is a lot of unknowns. We do not know the ages of the population that was studied. We do not know how many cigarettes that were smoked to obtain the data. And we do not know the length of time the study was conducted, or how many people were used in this study. The type of cigarettes may affect the study and if they were male of female. The hypothesis of the study was not addressed. The conclusion: Cigarettes cause the pulse rate to increase, leave a lot of variable to be imagined. The pulse rate could have risen due to other causes. There is not a graph that shows that this would be a linear correlation or straight line of data obtained. The data obtained from the individuals smoking would have probably have resulted in many different heart rate zones for it to be considered a linear conclusion. I feel more in formation and tested would need to be completed and submitted before coming to a conclusion.

Linear-Correlation. (n.d.). Retrieved May 28, 2018, from https://lc.gcumedia.com/hlt362v/the-visual-learner…

Linear Relationship: Definition, Examples. (n.d.). Retrieved May 28, 2018, from http://www.statisticshowto.com/linear-relationship

Discussion 5

A linear correlation analysis is a statistical technique that measures causation, direction, degree and significance of co-variation between two or more variables. In other words, Correlation is a bivariate analysis that measures the strength of association between two variables and the direction of the relationship. In this case, the conclusion cigarettes cause the pulse rate to increase is not valid because linear correlation tells us the strength of the relationship. The correlation we are dealing with shows not causal relation. In a causal relation, it is supposed to be portrayed how an increase in one variable leads to a significant increase in the other variable.

In this case, it cannot be determined if there is a direct causal relationship between variables. A valid linear correlation between two variables will exist if there is a mutual influence from one variable on the other and it can be proved that indeed the relationship exists. For instance, it is easy to detect and prove that a correlation exists between demand and supply (Pagano, 1981). Where demand can cause to increase or decrease the supply as an effect. In our case, there are different factors that could lead to an increase in pulse rate other than cigarette smoking. It is hard to determine if one’s pulse rate increase is solely due to cigarette smoking hence the conclusion is not valid.

References

Pagano, R. R. (1981). Understanding statistics in the behavioral sciences. St. Paul: West Pub. Co.

Discussion 6

A linear correlation is measured between two variables and it indicates what kind of relationship the variables have. R, or a relationship in a linear correlation determines the linear relation of two variables. A relationship has a positive correlation when both variables slope upward toward the right side of the graph, and both variables increase as they slope up. In a positive relationship the r coefficient is used to describe the relationship, and this value can be negative or positive, but must be between -1 and 1. “A positive correlation is obtained when the scatter of data points representing the two variables slopes upward to the right; as one variable increases, so does the other” (Grand Canyon University, 2013). A negative relationship slopes downwards towards the right, and both variables decrease as they both slope down the graph. The r coefficient for a negative relationship must be negative since the graph slopes downwards.

The error in the given conclusion of cigarettes causing the pulse rate to increase is false because not enough data is given. It is an assumption to conclude that cigarettes are the cause for the heart rate to increase. Without evidence and data to prove this, the given conclusion is an assumption. There are many contributing factors that may also increase the pulse rate such as the person’s age, gender, how long they have been smoking, what kind of cigarettes, how many cigarettes, the weather, the location, their health conditions, etc. The given study topic does not specify how many people were in the study population or how long they have been researched in this study. The linear correlation between cigarettes and the pulse rate may be positive as stated in the given information, however without evidence, one cannot conclude that the cause of the positive correlation is solely because of the number of cigarettes smoked in this example.

Reference

Grand Canyon University. (2013). Discovering Relationships and Building Models. Lecture.