sachthakur

[sachthakur]

Mars crater ANOVA

An analysis of the Martian crater database, which contains over 300,000 craters with a diameter of 1 km or more, was conducted to investigate the relationship between the morphology of ejecta material and the size of the craters. ANOVA was used to determine whether there is a statistically significant difference in crater size across different morphology ejecta types. Python code used: import pandas as pd import statsmodels.api as sm from statsmodels.formula.api import ols # read the csv file into a pandas dataframe df = pd.read_csv("mars_craters.csv") # perform ANOVA on the "DIAM_CIRCLE_IMAGE" column, grouped by the "MORPHOLOGY_EJECTA_1" column model = ols('DIAM_CIRCLE_IMAGE ~ C(MORPHOLOGY_EJECTA_1)', data=df).fit() anova_table = sm.stats.anova_lm(model, typ=2) # print the ANOVA table print(anova_table) Results:

The total sum of squares was 2.837302e+07.

The sum of squares for the "MORPHOLOGY_EJECTA_1" variable was 1.640291e+06.

The degrees of freedom (df) for the "MORPHOLOGY_EJECTA_1" variable was 155.

The mean square for the "MORPHOLOGY_EJECTA_1" variable was 1.057317e+04.

The F-statistic for the "MORPHOLOGY_EJECTA_1" variable was 152.085735.

The p-value for the "MORPHOLOGY_EJECTA_1" variable was 0.0, indicating statistical significance.

The sum of squares for the residual was 2.673273e+07.

The degrees of freedom (df) for the residual was 384187.

The analysis indicated that there is a statistically significant difference in crater size across different morphology ejecta types. The F-statistic, which compares the between-group variation to the within-group variation, was large, suggesting that the differences between groups were larger than the differences within groups. The p-value was very small, indicating that the observed effect was unlikely to be due to chance, and therefore was statistically significant.

In summary, the analysis suggests that morphology ejecta type may be an important factor in determining crater size.