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'Id','Clear','Antibo','Age','Ear' 1,1,1,1,1 2,1,1,1,1 3,1,1,1,1 4,0,1,1,1 5,0,1,1,1 6,0,1,1,1 7,0,1,1,1 8,0,1,1,1 9,0,1,1,1 10,0,1,1,1 11,0,1,1,1 12,0,1,1,1 13,1,1,2,1 14,1,1,2,1 15,1,1,2,1 16,1,1,2,1 17,1,1,2,1 18,1,1,2,1 19,1,1,2,1 20,1,1,2,1 21,1,1,2,1 22,1,1,2,1 23,1,1,2,1 24,1,1,2,1

'Id','Clear','Antibo','Age','Ear' 1,1,1,1,1 2,1,1,1,1 3,1,1,1,1 4,0,1,1,1 5,0,1,1,1 6,0,1,1,1 7,0,1,1,1 8,0,1,1,1 9,0,1,1,1 10,0,1,1,1 11,0,1,1,1 12,0,1,1,1 13,1,1,2,1 14,1,1,2,1 15,1,1,2,1 16,1,1,2,1 17,1,1,2,1 18,1,1,2,1 19,1,1,2,1 20,1,1,2,1 21,1,1,2,1 22,1,1,2,1 23,1,1,2,1 24,1,1,2,1 25,1,1,2,1 26,1,1,2,1 27,1,1,2,1 28,1,1,2,1 29,1,1,2,1 30,1,1,2,1 31,1,1,2,1 32,1,1,2,1 33,1,1,2,1 34,1,1,1,1 35,1,1,2,1 36,1,1,2,1 37,0,1,2,1 38,0,1,2,1 39,0,1,2,1 40,0,1,2,1 41,0,1,2,1 42,0,1,2,1 43,0,1,2,1 44,1,1,3,1 45,1,1,3,1 46,1,1,3,1 47,1,1,3,1 48,1,1,3,1 49,1,1,3,1 50,1,1,3,1 51,1,1,3,1 52,1,1,3,1 53,1,1,3,1 54,1,1,3,1 55,0,1,3,1 56,0,1,3,1 57,0,1,3,1 58,0,1,3,1 59,0,1,3,1 60,0,1,3,1 61,0,1,3,1 62,0,1,3,1 63,1,2,1,1 64,1,2,1,1 65,0,2,1,1 66,0,2,1,1 67,0,2,1,1 68,0,2,1,1 69,0,2,1,1 70,0,2,1,1 71,0,2,1,1 72,0,2,1,1 73,0,2,1,1 74,0,2,1,1 75,1,2,2,1 76,1,2,2,1 77,1,2,2,1 78,1,2,2,1 79,1,2,2,1 80,1,2,2,1 81,1,2,2,1 82,1,2,2,1 83,1,2,2,1 84,1,2,2,1 85,1,2,2,1 86,1,2,2,1 87,1,2,2,1 88,1,2,2,1 89,0,2,2,1 90,0,2,2,1 91,0,2,2,1 92,0,2,2,1 93,0,2,2,1 94,0,2,2,1 95,0,2,2,1 96,0,2,2,1 97,0,2,2,1 98,0,2,2,1 99,0,2,2,1 100,0,2,2,1 101,0,2,2,1 102,0,2,2,1 103,0,2,2,1 104,0,2,2,1 105,0,2,2,1 106,0,2,2,1 107,0,2,2,1 108,0,2,2,1 109,0,2,2,1 110,0,2,2,1 111,1,2,3,1 112,1,2,3,1 113,1,2,3,1 114,1,2,3,1 115,1,2,3,1 116,1,2,3,1 117,1,2,3,1 118,1,2,3,1 119,1,2,3,1 120,1,2,3,1 121,1,2,3,1 122,0,2,3,1 123,0,2,3,1 124,0,2,3,1 125,0,2,3,1 126,0,2,3,1 127,0,2,3,1 128,0,2,3,1 129,1,1,1,1 129,1,1,1,2 130,1,1,1,1 130,1,1,1,2 131,1,1,1,1 131,1,1,1,2 132,1,1,1,1 132,1,1,1,2 133,1,1,1,1 133,1,1,1,2 134,1,1,1,1 134,1,1,1,2 135,1,1,1,1 135,1,1,1,2 136,1,1,1,1 136,1,1,1,2 137,1,1,1,1 137,0,1,1,2 138,1,1,1,1 138,0,1,1,2 139,0,1,1,1 139,0,1,1,2 140,0,1,1,1 140,0,1,1,2 141,0,1,1,1 141,0,1,1,2 142,0,1,1,1 142,0,1,1,2 143,0,1,1,1 143,0,1,1,2 144,0,1,1,1 144,0,1,1,2 145,0,1,1,1 145,0,1,1,2 146,0,1,1,1 146,0,1,1,2 147,1,1,2,1 147,1,1,2,2 148,1,1,2,1 148,1,1,2,2 149,1,1,2,1 149,1,1,2,2 150,1,1,2,1 150,1,1,2,2 151,1,1,2,1 151,1,1,2,2 152,1,1,2,1 152,1,1,2,2 153,1,1,2,1 153,1,1,2,2 154,1,1,2,1 154,1,1,2,2 155,1,1,2,1 155,1,1,2,2 156,1,1,2,1 156,1,1,2,2 157,1,1,2,1 157,0,1,2,2 158,1,1,2,1 158,0,1,2,2 159,1,1,2,1 159,0,1,2,2 160,1,1,2,1 160,0,1,2,2 161,1,1,2,1 161,0,1,2,2 162,1,1,2,1 162,0,1,2,2 163,0,1,2,1 163,0,1,2,2 164,0,1,2,1 164,0,1,2,2 165,0,1,2,1 165,0,1,2,2 166,0,1,2,1 166,0,1,2,2 167,0,1,2,1 167,0,1,2,2 168,0,1,2,1 168,0,1,2,2 169,1,1,3,1 169,1,1,3,2 170,1,1,3,1 170,1,1,3,2 171,1,1,3,1 171,1,1,3,2 172,1,1,3,1 172,0,1,3,2 173,1,2,1,1 173,1,2,1,2 174,1,2,1,1 174,1,2,1,2 175,1,2,1,1 175,0,2,1,2 176,1,2,1,1 176,0,2,1,2 177,0,2,1,1 177,0,2,1,2 178,0,2,1,1 178,0,2,1,2 179,0,2,1,1 179,0,2,1,2 180,0,2,1,1 180,0,2,1,2 181,0,2,1,1 181,0,2,1,2 182,0,2,1,1 182,0,2,1,2 183,0,2,1,1 183,0,2,1,2 184,0,2,1,1 184,0,2,1,2 185,0,2,1,1 185,0,2,1,2 186,0,2,1,1 186,0,2,1,2 187,0,2,1,1 187,0,2,1,2 188,1,2,2,1 188,1,2,2,2 189,1,2,2,1 189,1,2,2,2 190,1,2,2,1 190,1,2,2,2 191,1,2,2,1 191,1,2,2,2 192,1,2,2,1 192,1,2,2,2 193,1,2,2,1 193,0,2,2,2 194,0,2,2,1 194,0,2,2,2 195,0,2,2,1 195,0,2,2,2 196,0,2,2,1 196,0,2,2,2 197,1,2,3,1 197,1,2,3,2 198,1,2,3,1 198,1,2,3,2 199,1,2,3,1 199,1,2,3,2 200,1,2,3,1 200,1,2,3,2 201,1,2,3,1 201,1,2,3,2 202,1,2,3,1 202,1,2,3,2 203,0,2,3,1 203,0,2,3,2 Refer to the data set EAR.DAT on the Companion Website. We will only consider a subject's first ear (`Ear=1`).

Consider a subject "cured" if

* the subject is a unilateral case and the ear clears by 14 days, or * the subject is a bilateral case and both ears are clear by 14 days.

Read the documentation on the dataset. **Delete all observations for the second ear** from the dataset before you complete the following.

## Part a (6 points)

Run a logistic regression with outcome variable `Clear` and independent variables antibiotic (`Antibo`), and age (`Age`).

Report your results by writing the logistic model in equation form. You must also clearly define the variables used in the equation (namely, the $x_i$'s).

\vspace{0.75in}

## Part b (6 points)

What is the $OR$ for 6+ year old children vs. children younger than 2?

Write a sentence interpreting this number in the context of the problem.

\vspace{0.75in}

## Part c (6 points)

What is the $OR$ for children receiving AMO antibiotic vs. children receiving CEF antibiotic?

Write a sentence interpreting this number in the context of the problem.

\vspace{0.25in}

## part d (6 points) Suppose a two year old child's ear infection in the first ear was cleared within 14 days. Would you classify that child to the group who have been given an antibiotic?

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