In this comprehensive analysis, we delve into the world of Univariate Analysis of Variance (ANOVA) to examine the doubling time of 18 patients across three distinct cohorts. We begin by providing detailed descriptive statistics, offering insights into the central tendencies and variabilities within the data. An essential aspect of our investigation is the evaluation of heteroskedasticity, a crucial assumption in ANOVA. Our findings reveal the presence of varying variances among cohorts. Subsequently, we perform ANOVA, uncovering significant differences in mean doubling times. Post hoc testing then pinpoints the specific cohort pairs with statistically significant variations, offering valuable information for researchers and healthcare professionals alike.

## Problem Description

In the ANOVA assignment, we conducted a Univariate Analysis of Variance (ANOVA) to explore the variation in doubling time among three distinct cohorts of patients. Doubling time is an essential parameter in medical research, especially when studying the growth of cells or diseases. Our goal was to determine whether significant differences in doubling time among these cohorts exist. Additionally, we tested for heteroskedasticity, a condition where within-group standard deviations differ, which could potentially affect the ANOVA results.

## Summary of Assignment Solution:

Descriptive Statistics: The following tables present descriptive statistics for doubling time across the entire dataset and for each of the three cohorts separately:

Statistics | Overall Data | Cohort 1 | Cohort 2 | Cohort 3 |
---|---|---|---|---|

N | 18 | 3 | 11 | 4 |

Mean | 2.78 | 10.2 | 1.17 | 1.63 |

Std. Deviation | 3.51 | 1.6 | 0.46 | 1.12 |

Kurtosis | 2.43 | - | - | - |

Skewness | 1.94 | - | - | - |

**Table 1: Descriptive Statistics for Doubling Time.**

For the overall dataset, the mean doubling time for the 18 patients is 2.78, with a standard deviation of 3.51, skewness of 1.94, and kurtosis of 2.43. Cohort 1, comprising 3 participants, has a mean doubling time of 10.2 with a standard deviation of 1.6. Cohort 2, with 11 participants, has a mean doubling time of 1.17 and a standard deviation of 0.46. Cohort 3, with 4 participants, has a mean doubling time of 1.63 and a standard deviation of 1.12.

Heteroskedasticity Testing: Heteroskedasticity violates one of the assumptions of ANOVA, indicating that the variances of doubling time for the three cohorts are different. We employed the Levene Test for Equality of Variances to determine this:

**Null Hypothesis**: The variances are equal across the three cohorts (homoscedasticity).**Alternative Hypothesis**: The variances are different across the three cohorts (heteroscedasticity).

The test resulted in a p-value less than the significance level, leading us to reject the null hypothesis and conclude that heteroskedasticity is present in the dataset. This underscores the importance of testing this assumption, as unequal variances can influence the ANOVA results.

ANOVA and Post Hoc Analysis: To compare the cohorts for differences in means, we performed an ANOVA. The F-test revealed a significant difference in cohort means, as indicated by F(2,15) = 134.54, p = 0.000. This means that at least one cohort's mean doubling time differs significantly from the others.

To determine where these differences lie, we conducted a post hoc analysis using the Games-Howell test due to the presence of unequal variances. The results are as follows:

- Cohort 1 vs. Cohort 2: p = 0.02 (significant difference)
- Cohort 1 vs. Cohort 3: p = 0.009 (significant difference)
- Cohort 2 vs. Cohort 3: p = 0.738 (no significant difference)

This post hoc analysis clarifies that there are significant differences between the mean doubling time of Cohort 1 compared to Cohort 2 and Cohort 3, but no significant difference between Cohort 2 and Cohort 3.

In conclusion, the ANOVA and subsequent post hoc analysis allowed us to identify significant differences in doubling time means between certain cohorts, providing valuable insights for our research.

## Related Samples

Explore a plethora of exemplary statistics assignments showcasing diverse topics and methodologies. Delve into our curated collection to gain insights into various statistical concepts, analysis techniques, and problem-solving strategies. Each sample provides a valuable reference point for understanding complex statistical problems and refining your skills. Dive into our repository to enrich your understanding and excel in statistical analysis.

Statistics

Statistics

Statistics

Statistics

Statistics

Data Analysis

Data Analysis

Data Analysis

Statistics

Statistics

Statistics

Data Analysis

Statistical Analysis

Statistics

Statistical Analysis

tableau

Statistical Analysis

Data Analysis

Statistics

Statistical Analysis