The relationship between the two variables is not perfectly linear, but there is a clear trend in the data. This can be seen from the scatterplot, which shows that as the hours of exercise per week increase, the BMI decreases. (1) In this case, the correct answer would be (D) There is a moderate negative correlation between hours of exercise per week and BMI. The correlation coefficient indicates the cause and effect relationship between two variables.Ĭorrelation implies causation, meaning that if two variables are correlated, one variable must cause the other.Ī scatterplot can show nonlinear relationships between two variables.Ī scatterplot can be used to predict the value of one variable based on the value of the other variable. The correlation coefficient indicates the strength and direction of the relationship between two variables. The correlation coefficient only measures linear relationships between two variables. (E) There is no correlation between hours of exercise per week and BMI.Ī scatterplot is a graphical representation of the relationship between two variables.Ī correlation coefficient of 1 indicates a strong positive correlation between two variables.Ī correlation coefficient of -1 indicates a strong positive correlation between two variables.Ī correlation coefficient of 0 indicates no correlation between two variables. (D) There is a moderate negative correlation between hours of exercise per week and BMI. (C) There is a moderate positive correlation between hours of exercise per week and BMI. (B) There is a strong negative correlation between hours of exercise per week and BMI. (A) There is a strong positive correlation between hours of exercise per week and BMI. Here are some scatterplots and their values of r:īased on the scatterplot, which of the following statements is true? As mentioned before, correlation =/= causation! □ Examples Additionally, the correlation coefficient does not indicate the cause and effect relationship between the two variables. It does not indicate the strength or nature of any nonlinear relationships that may exist. It's important to note that the correlation coefficient only measures the linear relationship between two variables. A correlation coefficient of 0 means that there is no correlation between the data points. The coefficient takes a value between -1 and 1, where r = -1 means that the points fall exactly on an decreasing line while r = 1 means that the points fall exactly on a increasing line. It can be positive or negative and this is the same as the direction of the scatterplot. The correlation coefficient shows the degree to which there is a linear correlation between the two variables, that is, how close the points are to forming a line. Correlation is a measure of the strength and direction of the relationship between two variables, and this is numerically represented with the correlation coefficient, which in stats we denote as r.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |