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[UNLOCK] Descriptive Statistics for Missing Values

   

Added on  2020-07-22

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SPSS
[UNLOCK] Descriptive Statistics for Missing Values_1

TABLE OF CONTENTSINTRODUCTION...........................................................................................................................1Methods used in research study, any variable created and decisions made to include exlcudethe variables from analysis..........................................................................................................1Descriptive statistics logistic regression......................................................................................2Way in which odds for needing mental health treatment in 12 months but not receiving it areinfluenced by variables in model.................................................................................................4Identification of characteristics of group of people who have highest odd of needing mentalhealth treatment but not receiving it............................................................................................4Demographic feature of people included and exluded from model............................................5Cohort of people having high probability of exlusion.................................................................5Comparison of proportion of cohort of people exluded from model and same of white hispanicmale..............................................................................................................................................5CONCLUSION................................................................................................................................5REFERENCES................................................................................................................................7APPENDIX......................................................................................................................................8
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INTRODUCTIONHealthcare is the one of important sector to which most of nations of the world give dueimportance. Analytics is the one of field that have wide scope across all industries of the worldand same is used in healthcare sector to make varied sort of decisions. In current report main aimis to identify predictors that have significent impact or play an important role in cretion ofthought that someone is suffered from mental problem but do not receive any sort of treatment.In present research study logistic regression will be applied on data set and prediction will bemade whether an individual will suffered be in condition where it is suffered from mental diseaseand not receive treatment or vice verse. Apart from this, in second part of the report proportionof missing values is identified and on that basis useful facts are identified. At end of the report,conclusion section is prepared on the basis of analysis of facts and figures.Methods used in research study, any variable created and decisions made to include exlcude thevariables from analysisIn the present research study logistic regression model is used to make prediction. It isone of the important tool that is used to make prediction. In case of logistic regression there issingle dependent variable that is of categorical in nature and multiple independent variableswhich may be continuous or categorical in nature (Dhingra and et.al., 2010). There is differencebetween both sort of variables which is that in case of categorical variable there are differentclassifications of variable and in case of continuous variable there are no categories and differentvalues in respect to variables are observed in data set. Logistic regression is used to makeprediction whether person thought they need mental health treatment receive or did not receiveit. In this regard some of predictors are taken in to account. These predictors can be classified into demographic factor, lifestyle factor, medical variables and attitudinal variables. By usinglogistic regression it is identified whether inclusion of to demographic factor, lifestyle factor,medical variables and attitudinal variables have any impact on dependent categorical variable (4Lifestyle changes that will boost your mental health, 2015). No new variable is created and old one are used and under this age, gender, marital statusand education. In case of lifestyle variables two variables are taken in to account for analysispurpose like SNMOV5Y2 and SUMYR. SNMOV5Y2 reflect number of times individual movedin past 5 years and SUMYR reflect illicit drug used in past years. Apart from this, as predictors1 | P a g e
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in case of medical variables asthma and depression variables which are LIFASMA and LIFDABare taken in to account. In same way no variable is exluded from the model because missing values are there infew variables. Moreover, proportion of these missing values are very low in variable data. Due tothis reason none of variable is removed from the model. Usually, in practice of analytics specificvariable is excluded or not considered in model when percentage of missing values are high indata set. Same thing is not observed in data set and due to this reason no variable is removedfrom model.On the basis of coding that is done in SPSS Syntax editor prediction is made whether anindividual will suffered be in condition where it is suffered from mental disease and not receivetreatment or vice verse. In this regard some parameters or funtions are used and lots of things arespecified within them. Functions that are used in SPSS Syntax editor are logistic regressionvariables, categorical, method, save and criteria function. In second part of the report as part of method in order to identify demographiccharacteristic of exluded number of missing values were identified by generating output for eachvariable. Then data of variables paste in excel and from same missing values rows wereidentified and finally descriptive of same is computed. In order to make percentage comparisoncross tab function used and for relevant variable percentage is computed.Descriptive statistics logistic regression2 | P a g e
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