Ev Heading 2 Heading 3 Normal No Spacing Heading 1 R Program for Contingency Table of Liked x Military Tweet CrossTable(X$liked, X$mil_tweet, digits=3, max.width = 5, expected=FALSE, prop.c=TRUE, prop.r=FALSE, prop.t=TRUE, prop.chisq=FALSE, chisq = TRUE, fisher=FALSE, mcnemar=FALSE, resid=FALSE, sresid FALSE, asresideFALSE, missing.include=FALSE format=c("SAS","SPSS"), dnn = NULL) Total Observations in Table: 1351448 Cell Contents X$mil_tweet N / Col Total X$liked Row Total N / Table Total 1080442 157962 1238404 0. 917 0 . 909 0. 799 0. 117 97260 15784 113044 0 . 083 0 . 091 0. 072 0 . 012 Column Total 1177702 173746 1351448 0. 871 0 . 129 Russian Influence Operations on Twitter Non-Military Tweets Military Tweets Chi-Square P-value % Retweeted 7.70% 8.50% 157.00 0.000 % Liked 8.30% 9.10% 134.80 0.000 % Quoted 1.60% 1.80% 52.88 0.000 % Received Replies 3.40% 3.30% 0.58 0.440 This table is a common way to present cross-tab results. Each row represents a different variable, showing the main outcome category of interest. 1. What percent of military tweets received likes? What percent of non-military tweets? 2. Were military tweets more or less likely to receive likes than non-military ones (sign)? How much was the difference (size)? 3. Are the results statistically significant? 4. Overall, how did the Russian military tweets compare to the non-military ones on (all 4 of) our metrics of audience engagement? State your answer in terms of sign, size, and significance, and discuss how whether the results as a whole support or fail to support the hypothesis that the Russians were more successful in engaging audiences in their influence operations when they used military profiles and content. Foct