#Plot dataset plt.figure("Regular plot") plt.title("Regular plot") plt.scatter(x=bmgr_download_speeds["Date"],y=bmgr_download_speeds["Download Speed (Mbps)"],c="r",label="BMGR-E") plt.scatter(x=pm_download_speeds["Date"],y=pm_download_speeds["Download Speed (Mbps)"],c="b",label="Point Mugu") plt.xticks(rotation=90) plt.legend() plt.figure("Logarithmic plot") plt.title("Logarithmic plot") plt.scatter(x=bmgr_download_speeds["Date"],y=np.log10(bmgr_download_speeds["Download Speed (Mbps)"]),c="r",label="BMGR-E") plt.scatter(x=pm_download_speeds["Date"],y=np.log10(pm_download_speeds["Download Speed (Mbps)"]),c="b",label="Point Mugu") plt.xticks(rotation=90) plt.legend()

# stats f_oneway functions takes the groups as input and returns ANOVA F and p value fvalue, pvalue = stats.f_oneway(a[a["Location"]=="BMGR-E"]["Download Speed (Mbps)"], a[a["Location"]=="Point Mugu"]["Download Speed (Mbps)"]) print(fvalue, pvalue) # get ANOVA table as R like output import statsmodels.api as sm from statsmodels.formula.api import ols # # Ordinary Least Squares (OLS) model c = a.copy() c.reset_index(inplace=True) c.columns = ['index', 'Location', 'DownloadSpeed'] print(c.head()) model = ols('DownloadSpeed ~ C(Location)', data=c).fit() print(model.summary()) anova_table = sm.stats.anova_lm(model, typ=2) anova_table

# Create the data schema two_way_df = download_speeds[["Location","Station ID","Download Speed (Mbps)"]] two_way_df.reset_index(inplace=True) two_way_df.columns = ['index', 'Location', "StationID", 'DownloadSpeed'] # Print first 5 lines of the data schema print(two_way_df.head()) # Plot the data sns.boxplot(x="StationID", y="DownloadSpeed", hue="Location", data=two_way_df, palette="Set3")

# Ordinary Least Squares (OLS) model model = ols('DownloadSpeed ~ C(Location) / C(StationID)', data=two_way_df).fit() print(model.summary()) # ANOVA - Anova table for one or more fitted linear models. # URL: # https://www.statsmodels.org/dev/generated/statsmodels.stats.anova.anova_lm.html anova_table = sm.stats.anova_lm(model, typ=2) # Main Variable: Site/Location # Nested: Field Session/TripNumber # Print results anova_table

# Create the data schema two_way_df = download_speeds[["Location","Trip Number","Download Speed (Mbps)"]] two_way_df.reset_index(inplace=True) two_way_df.columns = ['index', 'Location', "TripNumber", 'DownloadSpeed'] # Print first 5 lines of the data schema print(two_way_df.head()) # Plot the data sns.boxplot(x="TripNumber", y="DownloadSpeed", hue="Location", data=two_way_df, palette="Set3")

# Ordinary Least Squares (OLS) model model = ols('DownloadSpeed ~ C(Location) / C(TripNumber)', data=two_way_df).fit() print(model.summary()) # ANOVA - Anova table for one or more fitted linear models. # URL: # https://www.statsmodels.org/dev/generated/statsmodels.stats.anova.anova_lm.html anova_table = sm.stats.anova_lm(model, typ=2) # Main Variable: Camera trap ID/StationID # Nested: Field Session/TripNumber # Print results anova_table

# Create the data schema two_way_df = download_speeds[["Station ID","Trip Number","Download Speed (Mbps)"]] two_way_df.reset_index(inplace=True) two_way_df.columns = ['index', 'StationID', "TripNumber", 'DownloadSpeed'] # Print first 5 lines of the data schema print(two_way_df.head())

# Ordinary Least Squares (OLS) model model = ols('DownloadSpeed ~ C(StationID) / C(TripNumber)', data=two_way_df).fit() print(model.summary()) # ANOVA - Anova table for one or more fitted linear models. # URL: # https://www.statsmodels.org/dev/generated/statsmodels.stats.anova.anova_lm.html anova_table = sm.stats.anova_lm(model, typ=2) # Main Variable: Site/Location # Nested Variable: Camera trap ID/StationID # Print results anova_table

# Create the data schema two_way_df = download_speeds[["Location","Trip Number","Station ID","Download Speed (Mbps)"]] two_way_df.reset_index(inplace=True) two_way_df.columns = ['index', 'Location', "TripNumber", "StationID", 'DownloadSpeed'] # Print first 5 lines of the data schema print(two_way_df.head())

# Ordinary Least Squares (OLS) model model = ols('DownloadSpeed ~ C(Location) / C(TripNumber) / C(StationID)', data=two_way_df).fit() print(model.summary()) # ANOVA - Anova table for one or more fitted linear models. # URL: # https://www.statsmodels.org/dev/generated/statsmodels.stats.anova.anova_lm.html anova_table = sm.stats.anova_lm(model, typ=2) # Main Variable: Site/Location # Nested Variable: Camera trap ID/StationID & Station ID # Print results anova_table