Question: Find the Placekick.csv dataset on Canvas. This parameters are week (week of the season), distance (distance of the placekick in yards), change (binary, lead-change

Find the Placekick.csv dataset on Canvas. This parameters are week (week of
the season), distance (distance of the placekick in yards), change (binary, lead-change

Find the Placekick.csv dataset on Canvas. This parameters are week (week of the season), distance (distance of the placekick in yards), change (binary, lead-change (1) vs non-lead change (0). Considers if kick has potential to change which team is in the lead), elap30 (time remaining before end of the half, overtime kicks value is 0), PAT (PAT attempt = 1 and field goal attempt = 0), type (outdoor = 1, dome = 0), field (grass = 1, astroturf = 0), wind (windy = 1, not windy = 0), and good (successful kick = 1, unsuccessful kick = 0, binary response variable). == a. Group the data by distance and create a group for number of successes (by distance), the number of attempts (by distance), and the proportion of good kicks. See example below: distance success trials proportion 123456 18 2 3 0.6667 19 7 7 1.0000 20 776 789 0.9835 21 19 20 0.9500 22 12 14 0.8571 23 26 27 0.9630 b. Create the logit model for this data and explain the results. week distance change elap30 PAT type field wind good 1 1 1 20 1 28 1 1 1 1 1 20 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2222222222752222222222222222252222222222212522222224242222222232222222222722 1 24.7167 0 1 1 0 1 0 15.85 0 1 1 0 1 0 0.45 1 1 1 0 0 13.55 0 1 1 0 1 0 21.8667 1 0 0 0 1 0 17.6833 0 0 0 0 0 12.6833 1 0 0 1 4.7833 0 0 0 1 1 0.9167 0 0 0 0 0 25.7667 0 0 0 0 0 6.2667 0 0 0 0 0 1 0 0 0 0 1 1 19.7833 0 0 0 29.1833 1 1 1 0 12.5167 1 0 1 0 10.85 0 1 1.8 1 1 0 1 0 0.0667 1 0 1 0 21.0833 0 19.0333 0 15.75 1 0 0 6.7167 0 0 1.9333 1 1 0 1 0 11.45 1 0 0 1 0 0.65 0 0 1 22.4667 0 0 0 22.15 1 0 0 15.5833 0 0 0 12.1167 1 0 0 0 4.0167 1 0 0 1 1 20.15 0 1 0 0 19.9333 1 0 14.1833 1 1 1 0 19.6 0 1 0 1 0 16.5833 1 0 0 5.3833 1 1 0 1 0 23.7167 1 1 0 1 0 14.9167 0 1 8.4167 0 1 1 0 2.9333 0 1 0 0 0 0.0333 0 0 10.3833 0 1 0 1 0 3.3667 1 1 1 0 1 0 24.0333 1 0 0 18.3833 0 0 0 0 16.2833 0 0 0 0 7.1833 1 0 0 1 1.6833 0 0 0 0 0 24.9833 1 0 0 0 1 1 0.4333 1 0 0 19.0167 1 1 1 23.2167 1 1 1 0 18.0333 1 1 0 1 25.9333 0 0 0 0 0 19 1 0 0 0 1 0 10.4167 0 0 0 0 20 0 6.6667 1 0 0 0 0 0 0 0 0 21.3 1 0 0 0 20 0 2.8667 1 0 0 19.0667 1 1 1 0 1 18.7167 1 1 1 0 1 0 6.5167 1 1 1.2 1 1 0 3.6 0 1 0 21.7167 0 0 7.25 0 1 0 0 0 24.6333 0 1 0 1 0 3.0333 0 1 0 26.9333 1 0 11.9667 1 0 1 0 26.05 1 0 1 0 12.55 0 20.4 0 1 0 1 0 10.9333 1 1 0 1 1.3667 1 0 0 1 100 14.1833 0 1 10.7833 0 0.2667 1 1 1

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