An article in the New York Times (Jan. 27, 1987) reported that heart attack risk could bereduced by taking aspirin. This conclusion was based on a designed experi ment involvingboth a control group of individuals that took a placebo having the appearance ofaspirin but known to be inert and a treatment group that took aspirin according to aspecified regimen. Subjects were randomly assigned to the groups to protect againstany biases and so that probability-based methods could be used to analyze the data. Ofthe 11,034 individuals in the control group, 189 subsequently experienced heart attacks,whereas only 104 of the 11,037 in the aspirin group had a heart attack. The incidencerate of heart attacks in the treatment group was only about half that in the control group.One possible explanation for this result is chance variation—that aspirin really doesn’thave the desired effect and the observed dif ference is just typical variation in the sameway that tossing two identical coins would usually produce different numbers of heads.However, in this case, inferential methods suggest that chance variation by itself cannotadequately explain the magnitude of the observed difference.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
An article in the New York Times (Jan. 27, 1987) reported that heart attack risk could be
reduced by taking aspirin. This conclusion was based on a designed experi ment involving
both a control group of individuals that took a placebo having the appearance of
aspirin but known to be inert and a treatment group that took aspirin according to a
specified regimen. Subjects were randomly assigned to the groups to protect against
any biases and so that probability-based methods could be used to analyze the data. Of
the 11,034 individuals in the control group, 189 subsequently experienced heart attacks,
whereas only 104 of the 11,037 in the aspirin group had a heart attack. The incidence
rate of heart attacks in the treatment group was only about half that in the control group.
One possible explanation for this result is chance variation—that aspirin really doesn’t
have the desired effect and the observed dif ference is just typical variation in the same
way that tossing two identical coins would usually produce different numbers of heads.
However, in this case, inferential methods suggest that chance variation by itself cannot
adequately explain the magnitude of the observed difference.
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