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Noah
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Avraham
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Should I use a Simple Linear Regression or Simple LinearLogistic Regression

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barnsm2
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Should I use a Simple Linear Regression or Simple Linear Regression

I have this reproducible dataset:

structure(list(age = c(62.84998, 60.33899, 52.74698, 42.38498, 79.88495, 93.01599, 62.37097, 86.83899, 85.65594, 42.25897), death = c(0, 1, 1, 1, 0, 1, 1, 1, 1, 1), sex = c("male", "female", "female", "female", "female", "male", "male", "male", "male", "female"), hospdead = c(0, 1, 0, 0, 0, 1, 0, 0, 0, 0), slos = c(5, 4, 17, 3, 16, 4, 9, 7, 12, 8), d.time = c(2029, 4, 47, 133, 2029, 4, 659, 142, 63, 370), dzgroup = c("Lung Cancer", "Cirrhosis", "Cirrhosis", "Lung Cancer", "ARF/MOSF w/Sepsis", "Coma", "CHF", "CHF", "Lung Cancer", "Colon Cancer"), dzclass = c("Cancer", "COPD/CHF/Cirrhosis", "COPD/CHF/Cirrhosis", "Cancer", "ARF/MOSF", "Coma", "COPD/CHF/Cirrhosis", "COPD/CHF/Cirrhosis", "Cancer", "Cancer"), num.co = c(0, 2, 2, 2, 1, 1, 1, 3, 2, 0), edu = c(11, 12, 12, 11, NA, 14, 14, NA, 12, 11), income = c("$11-$25k", "$11-$25k", "under $11k", "under $11k", NA, NA, "$25-$50k", NA, NA, "$25-$50k"), scoma = c(0, 44, 0, 0, 26, 55, 0, 26, 26, 0), charges = c(9715, 34496, 41094, 3075, 50127, 6884, 30460, 30460, NA, 9914), totcst = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), totmcst = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ ), avtisst = c(7, 29, 13, 7, 18.666656, 5, 8, 6.5, 8.5, 8 ), race = c("other", "white", "white", "white", "white", "white", "white", "white", "black", "hispanic"), sps = c(33.8984375, 52.6953125, 20.5, 20.0976562, 23.5, 19.3984375, 17.296875, 21.5976562, 15.8984375, 2.2998047), aps = c(20, 74, 45, 19, 30, 27, 46, 53, 17, 9), surv2m = c(0.262939453, 0.0009999275, 0.790893555, 0.698974609, 0.634887695, 0.284973145, 0.892944336, 0.670898438, 0.570922852, 0.952880859), surv6m = c(0.0369949341, 0, 0.664916992, 0.411987305, 0.532958984, 0.214996338, 0.820922852, 0.498962402, 0.24899292, 0.887939453), hday = c(1, 3, 4, 1, 3, 1, 1, 1, 1, 1), diabetes = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0), dementia = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0), ca = c("metastatic", "no", "no", "metastatic", "no", "no", "no", "no", "metastatic", "metastatic"), prg2m = c(0.5, 0, 0.75, 0.899999619, 0.899999619, 0, NA, 0.799999714, 0.049999982, NA), prg6m = c(0.25, 0, 0.5, 0.5, 0.8999996, 0, 0.6999998, 0.3999999, 0.0001249999, NA), dnr = c("no dnr", NA, "no dnr", "no dnr", "no dnr", "no dnr", "no dnr", "no dnr", "dnr after sadm", "no dnr"), dnrday = c(5, NA, 17, 3, 16, 4, 9, 7, 2, 8), meanbp = c(97, 43, 70, 75, 59, 110, 78, 72, 97, 84), wblc = c(6, 17.0976562, 8.5, 9.09960938, 13.5, 10.3984375, 11.6992188, 13.5996094, 9.69921875, 11.2988281), hrt = c(69, 112, 88, 88, 112, 101, 120, 100, 56, 94), resp = c(22, 34, 28, 32, 20, 44, 28, 26, 20, 20), temp = c(36, 34.59375, 37.39844, 35, 37.89844, 38.39844, 37.39844, 37.59375, 36.59375, 38.19531), pafi = c(388, 98, 231.65625, NA, 173.3125, 266.625, 309.5, 404.75, 357.125, NA), alb = c(1.7998047, NA, NA, NA, NA, NA, 4.7998047, NA, NA, 4.6992188), bili = c(0.19998169, NA, 2.19970703, NA, NA, NA, 0.39996338, NA, 0.39996338, 0.19998169), crea = c(1.19995117, 5.5, 2, 0.79992676, 0.79992676, 0.69995117, 1.59985352, 2, 1, 0.79992676), sod = c(141, 132, 134, 139, 143, 140, 132, 139, 143, 139), ph = c(7.459961, 7.25, 7.459961, NA, 7.509766, 7.65918, 7.479492, 7.509766, 7.449219, NA), glucose = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), bun = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), urine = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), adlp = c(7, NA, 1, 0, NA, NA, 0, NA, NA, 0), adls = c(7, 1, 0, 0, 2, 1, 1, 0, 7, NA), sfdm2 = c(NA, "<2 mo. follow-up", "<2 mo. follow-up", "no(M2 and SIP pres)", "no(M2 and SIP pres)", "<2 mo. follow-up", "no(M2 and SIP pres)", NA, NA, NA), adlsc = c(7, 1, 0, 0, 2, 1, 1, 0, 7, 0.4947999)), row.names = c(NA, 10L ), class = "data.frame")

I am wanting to obtain the proportions of male and female patients within the different races of the study patients. I used this code to get the proportions.

SB_xlsx11$sex_f = as.factor(SB_xlsx11$sex) SB_xlsx11$race_f = as.factor(SB_xlsx11$race) prop.table(table(SB_xlsx11$race_f, SB_xlsx11$sex_f)) ## ## female male ## asian 0.003530840 0.005185921 ## black 0.076133731 0.077347457 ## hispanic 0.014895730 0.017102505 ## other 0.005627276 0.006730663 ## white 0.336864173 0.456581706

I believe this is correct, as it separates male and female by race. I want to see if there is evidence to indicate that sex is associated with the race of the study patients. Would this be a simple linear regression or a simple logistic regression? I'm also feeling uncertain about which variable would be my predictor (x) and response (y) (y~x) if I want to see if sex is associated with race of study patients.