Week 6 DQ 2 Response To Classmate Martha Gricem Statistics Homework Help

The initial perception of the study performed by the pharmaceutical company is that they enlisted a large sample size and performed either a t-test or ANOVA, depending on the number of conditions. The length of time is the independent variable and the size of the tumor is the dependent variable. The conditions of the length of time are the varying time lengths. If there were two lengths of time, a t-test may have been performed. If there are three or more conditions, it difficult to tell if it is a repeated measures ANOVA test with each measurement of the tumor being on the same individual, measured at different times, or an independent measures ANOVA with different individuals having tumor measurements, each at different time intervals of being on the drug. The large sample size, 2,400, provides statistical strength. The negative sign of the Pearson correlation coefficient, -.21, indicates there is a negative correlation between the length of time that a patient is on the drug and the size of the tumor (Pearson’s Product-Moment, 2015). Cohen (1998) provided guidelines about strengths of coefficient values, which defines the Pearson correlation coefficient of -.21 as a small correlation between the length of time one is on the drug and the tumor size. The significance, p<.0001, suggests that the correlation is significant (Gravetter & Wallnau, 2013). If the r2, is calculated, the coefficient of determination = .044 or 4.4% (Gravetter & Wallnau, 2013; Pearson’s Product-Moment, 2015). This means that the length of time one is taking the drug statistically explained 4.4% of the variability in tumor size. An r 2 of < .7 indicates that the instrument has low reliability and a low level of validity (Correlation, 2015). This data does not imply a causal relation, rather it explains that there is a very weak correlation between the length of time one takes the drug and the tumor size.

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