Psychologicalpredispositiontonicotineuse MA3022/MA4022/MA7022

MA3022/MA4022/MA7022 DataMiningandNeuralNetworks

ComputationalTask1

Duetill10.02.2025

100marksavailable

Psychologicalpredispositiontonicotineuse

Yourworkshouldanswerthequestion:Doesthepsychologicalpredispositiontodrugconsumptionexist?

Nowadays, after many years of research and development, psychologists have largely agreed that the personality traits of the modern Five Factor Model (FFM) constitutes the most comprehensive and adaptable system for understanding human individual differences. The FFM comprises Neuroticism (N), Extraversion (E), Openness to Experience (O), Agreeableness (A), and Conscientiousness (C).

Thefivetraitscanbesummarizedthus:

NNeuroticismisalong-termtendencytoexperiencenegativeemotionssuchasnervousness,tension,anxietyand depression (associated adjectives: anxious, self-pitying, tense, touchy, unstable, and worrying);

EExtraversion manifested in characters who are outgoing, warm, active, assertive, talkative, and cheerful; these persons are often in search of stimulation (associated adjectives: active, assertive, energetic, enthusiastic, outgoing, and talkative);

OOpenness to experience is associated with a general appreciation for art, unusual ideas, and imaginative, creative, unconventional, and wide interests (associated adjectives: artistic, curious, imaginative, insightful, original, and wide interest);

AAgreeableness is a dimension of interpersonal relations, characterized by altruism, trust, modesty, kindness, compassionandcooperativeness(associatedadjectives: appreciative,forgiving,generous,kind,sympathetic,and trusting);

CConscientiousnessisatendencytobeorganizedanddependable,strong-willed,persistent,reliable,andefficient (associated adjectives: efficient, organised, reliable, responsible, and thorough).

Twoadditionalcharacteristicsofpersonalityareproventobeimportantforanalysisofsubstanceuse,Impulsivity (Imp) and Sensation-Seeking (SS).

ImpImpulsivityisdefinedasatendencytoactwithoutadequateforethought;

SSSensation-Seeking is defined by the search for experiences and feelings, that are varied, novel, complex and intense, and by the readiness to take risks for the sake of such experiences.

Sevenpsychologicaltraitswereusedtocharacterisetheparticipants:N,E,O,A,C,Imp,andSS.

Task0. Preparationdataforanalysis

The dataset is onlinehttps://leicester.figshare.com/articles/dataset/Drug_consumption_database_quantified_categorical_attributes/7588409

Databasedescriptionisavailableat

https://leicester.figshare.com/articles/dataset/Drug_consumption_database_description/7588412

There are much more attributes than you need.Prepare the table.For every participant, leave the following information: 7 psychological traits and nicotine user/non-user (in the last year).

Theuser/non-userclassificationwillbethemaintask.

Task1. Descriptivestatistics(20marks)

For both classes (users and non-users) find the mean values of the 7 attributes and their stan- darddeviations.Evaluatethe95%confidenceintervalsformeanvalues.(Takethedefinitions fromanyelementarytextbookinstatistics. Averysimpleonlinetutorialabout95%confidence interval is here:http://www.itl.nist.gov/div898/handbook/eda/section3/eda352.htmA very simple textbook, The Little Handbook of Statistical Practice, is here:https://forum.disser.ru/index.php?act=attach&type=post&id=638.

Creategraphicalillustration(psychologicalprofilesofnicotineusersandnon-userswithcon- fidence intervals).

Task2.Significanceofdifferences(10marks)

Report,whichdifferencesbetweenthesemeansforusersandnon-usersaresignificant. For significance evaluation use p-values.

Task3. Oneattributeclassifier(15marks)

Trytocreatepredictorsuser/non-userbyoneattribute(7suchpredictors). Forthispurpose, create histograms for each attribute and each class and select the best threshold for each at- tributexfor the decision rule:ifx>athen one class (users or non-users) and ifx<athen another class (non-users or users) (the optimal cut).Find the classification error for each at- tribute.Which attribute gives the best prediction?Arrange the attributes in their prediction ability.

Task4.kNNclassifier(20marks)

Test1NNand3NNclassificationrules. Presenttheclassificationerrors. Whichruleisbet- ter?

Task5. Fisherslineardiscriminantdescription(10marks)

Find in the literature description and explanation of Fishers linear discriminant.Read, understand and write a comprehensive description of the algorithm with main formulas and explanation (not more than 1 page!)

Task6.Fisherslineardiscriminantusage(15marks)

Apply Fishers linear discriminant to the prepared data set.Analyse the quality of classifi- cation. Compare to 1NN and 3NN methods.

Extra10marksforclearandwell-writtenreport.