please respond to these discussions with a reference

Discussion 1

Thereis absolutely a correlation (relationship or association) betweenthe number of cigarettes smoked and the pulse rate, but notnecessarily stating causation. It is possible that smokingcigarettes cause the heart rate to go up. However, there are manyvariables to consider before reaching the conclusion of cigarettescause the pulse rate to increase. Such variables that need to beconsidered are: age, gender, # and type of cigarettes, duration orhistory of smoking, and other health conditions such as AtrialFibrillation, Anxiety, Asthma etc. These variables can differ fromone another and affect the outcome. Without considering thesefactors, it is not proven that cigarettes are the only reason,causing the heart rate to increase. In this given scenario, linearcorrelation is depicting the strength of the relationship amongthese two continuous variables (cigarette and pulse rate). It isextremely difficult to control and randomize all relevant factors,but they do help us explore casual relationships. Many cause, andeffect relationships like in this discussion topic are detectedthrough linear correlation due to more than one variable is neededto predict the outcome of another variable (Huber, 2007). Of course,there is an association between cigarettes smoked and the pulse rateincreases. If cigarettes contain nicotine, it produces adrenalinewhich makes the heart rate go faster (British Heart Foundation,n.d.). When stating null hypothesis, there is no relationshipbetween 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, curvilinearrelationship) between cigarettes smokes and the pulse rate, thecorrelation is not 0. For the calculation, we need to find the meanand deviation scores of both of these variables. After that,squaring each of the deviations scores will help us get rid of thenegative numbers, then take the sum of the squared deviation scoresand 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 linearrelationship between two interval/ratio level variables. Conductinga hypothesis test will determine the significance of the test or anexperiment. Therefore, the concept of regression is necessary toaccurately measure or predict the value for one variable given avalue of another variable.

References:

BritishHeart 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 NursingResearch: A Workbook for Evidence-

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

Discussion 2

A linearcorrelation is the strongest association between two variablesmeaning one variable has an effect on the other. If the associationis positive, when variable an increase so does variable b. If theassociation is negative, when variable an increase, variable bdecreases. Correlation is showing the relationship between twovariables. While it is tempting to think something with a strongcorrelation results in causation, even cigarette use and increasedheart rate, it does not. An example would be a linear correlationbetween hot weather and increased drowning events. Hot weathersurely does not cause drowning, however, hot weather is when peopleare more likely to be in the water, thus increasing risk of drowningevents. In order for causation to be proved a randomized study wouldneed to be conducted to look at any other factors that may play intothe scenario. For example if there is a strong correlation betweennumber of winnings in a sport team while playing in their hometown,we would need to conduct a random study to see what factors could beplaying into the success of the team while playing a home game.Factors could be increased crowd and the cheers are adding toconfidence, comfort of their own playing field resulting in lessanxiety, and the list goes on. Correlation does not always result incausation. 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 fromhttps://www.theguardian.com/science/blog/2012/jan/…

D. (2015, January 14). Proving causation. Retrieved fromhttps://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 datawill go in an upward direction and to the right. In positivecorrelations, as one variable increases so does the other (GrandCanyon University, 2013). In a negative correlation, the data will godownward and to the right. When we have a negative correlation, as onevariable decreases, the other increases. With correlation, we areshowing a relationship between two variables but not a cause andeffect relationship (Grand Canyon University, 2013). A linearregression does show a cause and effect relationship between data. Inlinear regression variables do not necessarily show relationships orsimilar characteristics but rather the variables explain and respondto one another (Grand Canyon University, 2013). One of the variableswill explain the other variable while the second variable will respondto the explanatory variable (Grand Canyon University, 2013).

In reading the discussion question and the explanation andconclusion, it seems that it is an assumption that all cigarettesincrease pulse rate. However, the sentence before that talks about howas the number of cigarettes smoked increased then the pulse rateincreased. We can gather from this that it is not the fact that thecigarette is being smoked but rather how many are being smoked when wesee an increased heart rate. We can see here that there is a positivecorrelation with the amount of cigarettes smoked and heart ratebecause as one increases so does the other.

Grand Canyon University. (2013). Discovering Relationshipsand Building Models. Lecture

Discussion 4

The error in the conclusion, cigarettes cause the pulse rate toincrease. Linear correlation means that the there is a linerepresented on a graph by the data received. In the variables thathave been given to us, there is a lot of unknowns. We do not know theages of the population that was studied. We do not know how manycigarettes that were smoked to obtain the data. And we do not know thelength of time the study was conducted, or how many people were usedin this study. The type of cigarettes may affect the study and if theywere male of female. The hypothesis of the study was not addressed.The conclusion: Cigarettes cause the pulse rate to increase, leave alot of variable to be imagined. The pulse rate could have risen due toother causes. There is not a graph that shows that this would be alinear correlation or straight line of data obtained. The dataobtained from the individuals smoking would have probably haveresulted in many different heart rate zones for it to be considered alinear conclusion. I feel more in formation and tested would need tobe 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 thatmeasures causation, direction, degree and significance of co-variationbetween two or more variables. In other words, Correlation is abivariate analysis that measures the strength of association betweentwo variables and the direction of the relationship. In this case, theconclusion cigarettes cause the pulse rate to increase is not validbecause linear correlation tells us the strength of the relationship.The correlation we are dealing with shows not causal relation. In acausal relation, it is supposed to be portrayed how an increase in onevariable leads to a significant increase in the other variable.

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

References

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

Discussion 6

A linear correlation is measured between two variables and itindicates what kind of relationship the variables have. R, or arelationship in a linear correlation determines the linear relation oftwo variables. A relationship has a positive correlation when bothvariables slope upward toward the right side of the graph, and bothvariables increase as they slope up. In a positive relationship ther coefficient is used to describe the relationship, and thisvalue can be negative or positive, but must be between -1 and 1.“A positive correlation is obtained when the scatter of datapoints representing the two variables slopes upward to the right; asone variable increases, so does the other” (Grand CanyonUniversity, 2013). A negative relationship slopes downwards towardsthe right, and both variables decrease as they both slope down thegraph. The r coefficient for a negative relationshipmust be negative since the graph slopes downwards.

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

Reference

Grand Canyon University. (2013). Discovering Relationshipsand Building Models. Lecture.