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MATH38172 Generalised Linear Models

创建日期：2024-04-30 16:24:15

所属分类：数学mathematics

标签： MATH38172

Generalised Linear Models

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MATH38172 Generalised Linear Models

Instructions. Attempt the questions below and submit your work online via Blackboard by the deadline
of Thursday 5th May at 11am. Your submission must be a single file. It may contain any sensible mix
of word-processed and (scanned) handwritten parts, for example using LaTeX, RMarkdown or Microsoft
Word. You should include any R code used. A complete solution is possible in 6 or 7 pages of type 10 font;
please limit your response to 12 pages at most. The coursework may take up to 10 hours to complete. The
submitted work MUST be your own. Plagiarism will not be tolerated and will result in serious consequences if
discovered.
Background. The file birthweight.csv on Blackboard contains data on 189 births collected at a medical
centre in the USA. The data concern several variables relating to the mother, the pregnancy, and the outcome
of interest which is whether the baby is born with a low birth weight (defined as less than 2500 grams). The
variables contained in the data are as follows.
Variable name Meaning
Low Indicator for low birth weight
Age Mother’s age in years
MotherWeight Mother’s weight before the pregnancy (in pounds)
Race Mother’s race
Smoke Indicator for whether the mother smoked during pregnancy
Premature Number of times the mother has previously given birth prematurely
Hypertension History of hypertension (1 - Yes; 0 - No)
UI Uterine irritability (1 - Yes; 0 - No)
Visits Number of physician visits during the first trimester
Questions
(a) Read the dataset into R and fit a logistic regression model to explain the probability of low birth weight
using all explanatory variables, treating “white” as the baseline level for race. Present the summary for
your fitted model. (2 marks)
(b) For which variables is there significant evidence of an association with low birthweight? Explain your
methodology. (3 marks)
(c) Fit a simplified model that only includes the significant explanatory variables and the intercept. (1
mark)
(d) Write down the simplified model from part (c) in equation form, and interpret the parameters and their
estimated values. What characteristics of a mother are associated with the highest probability of low
birth weight? (5 marks)
(e) Using the model from part (c) estimate the probability of low birth weight for a 28 year old black
mother, who prior to pregnancy weighed 130 lbs, does not smoke, is not experiencing any medical
conditions and received no physician visits. (1 mark)
(f) Using the model from part (c), estimate the weight at which a mother has a 20% chance of having
a baby with low birth weight, given that she is a white non-smoker with uterine irritability, but no
hypertension or physician visits. Give a 95% Wald confidence interval for this weight. What do you
notice? (4 marks)
(g) Do your results in part (b) about which variables are significantly associated with the probability of
low birthweight change if ‘history of premature labour’ and ‘number of physician visits’ are treated as
categorical variables? Explain your working and methodology, and posit reasons for any differences you
observe. (4 marks)

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MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 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