ANLY482 AY2016-17 T2 Group10 Analysis & Findings: Analysis
ACTUAL METHOD: Analysis of Variance (ANOVA) using Fit Y by X
Analysis of Variance is a statistical method used to analyze differences among group means and their variances among and between groups. It is also a form of statistical hypothesis testing to test whether differences between pairs of group means are significant or not.
Prior to using ANOVA, we have attempted using linear regression to generalize the relationship between number of interactions and sales revenue. However, low R-squared values that suggest weak correlation and model not fitting the data were obtained, and these prompted us to carry out similar analysis using nonparametric tests like ANOVA.
The primary step to carry out ANOVA is to discretize our explanatory variable - “interaction count” into bins and as such, converting it from a numerical to categorical variable. The objective of discretization is because we wish to understand whether each of these interaction bins have significant differences between one another when it comes to sales revenue (response). To define the range of interaction counts for “Low”, “Medium” and “High” interaction bins, we consulted our sponsor, who proposed that “Low” is for interaction count less than or equal to 1, “Medium” is for interaction count from 2 to 4 and “High” is for interaction count 5 and above.
Analysis
BY THERAPY GROUP
Therapy Group 1: Adult Vaccines
Results show that the p-value between the different categories is way lower than 0.05, which signifies that a change in interactions from low to high and low to medium results in a change in mean. Between the low to medium category though,the p value is higher than 0.05,signifying there is no conclusive evidence that a change in interaction level from low to medium has any impact on mean revenue levels.
Therapy Group 2: Dermatology
Results show that the p-value between the different categories is way lower than 0.05, which signifies that a change in interactions from low to high and low to medium results in a change in mean. Between the low to medium category though, the p value is higher than 0.05, signifying there is no conclusive evidence that a change in interaction level from medium to low has any impact on mean revenue levels.
Therapy Group 3: Allergy
Results show that the p-value between the different categories is way lower than 0.05, which signifies that a change in interactions from low to high and low to medium results in a change in mean. Between the low to medium category though, the p value is higher than 0.05, signifying there is no conclusive evidence that a change in interaction level from medium to low has any impact on mean revenue levels.
Therapy Group 4: Pediatrics Vaccines
Results show that the p-value between the different categories is way lower than 0.05, which signifies that a change in interactions from low to high and low to medium results in a change in mean. Between the low to medium category though, the p value is higher than 0.05, signifying there is no conclusive evidence that a change in interaction level from medium to low has any impact on mean revenue levels.
Therapy Group 5: Urology
Results show that the p-value between the different categories is way lower than 0.05, which signifies that a change in interactions from low to high and low to medium results in a change in mean. Between the low to medium category though, the p value is higher than 0.05, signifying there is no conclusive evidence that a change in interaction level from medium to low has any impact on mean revenue levels.
Therapy Group 6: Respiratory
Results show that across all categories, there p-value falls in the acceptable range, which means that there is no conclusive evidence that change in interaction levels have an impact on the mean of revenue levels.
