Alternately, you could use a Pearson's correlation to understand whether there is an association between blood pressure and time spent exercising (i.e., your two variables would be "blood pressure", measured in mm/Hg, and "time spent exercising", measured in hours per week). If there was a strong, positive association, we could say that more time spent revising was associated with higher test performance. A value of 0 (zero) indicates that there is no relationship between the two variables.įor example, you could use a Pearson's correlation to understand whether there is an association between test performance and revision time (i.e., your two variables would be "test performance", measured as the exam mark achieved, and "revision time", measured in hours per week). Its coefficient, r, indicates the strength and direction of this relationship and can range from -1 for a perfect negative linear relationship to +1 for a perfect positive linear relationship. The Pearson product-moment correlation, often shortened to Pearson correlation or Pearson's correlation, is used to assess the strength and direction of association between two continuous variables that are linearly related. On a separate piece of paper, show in theory (an argument that would prove any example) the relationship between the means and variances for rolls of one die and two dice.Pearson's correlation using Minitab Introduction Type your answer into the session window.
How is the variance for the roll of one die related to the variance for the roll of two dice? Why would it be difficult to see a relationship between the standard deviations? Explain your results. What do you notice about the means for your sample rolls of one die and two dice? Explain your results. Repeat process except find the Standard Deviation of the Roll x column.Take two dice and roll them 1000 times.Therefore, C5 should contain the numbers 2 through 12.Ĭount the number of time each value of x appears in the table of sums you made for the rolls of two dice. The x column should contain all the possible outcomes for a roll of two fair dice. Set up the next four columns of your worksheet to look like those below. Fill out the rest of the table below by adding together the results of each row and column. Therefore, x can be any number from 2 to 12. Let x = the sum of the numbers we see when two fair dice are rolled. Repeat process except find the Standard Deviation of the Roll z column.Let z = the number we see when we roll a fair die. We want to again roll a fair die 1000 times and find the mean, standard deviation, and variance for our new sample. By hand (with a calculator) square the standard deviation to get the variance.Repeat process except find the Standard Deviation of the Roll y column.Select the Counts and Percents check boxesĬalculate the Mean, Standard Deviation, and Variance.Set up your Minitab worksheet to look similar to the table below. Let y = the number we see when one fair die is rolled. This lab will involve examining probability distributions and expected values for when one fair die and two fair dice are rolled.