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The respective research analysis is developed for evaluating the effective attainment of SPSS statistical analysis in the Dyslipidaemia Study. The evaluation of the objective is to determine demographic analysis based on which different types of haematological measurements can be reported in the analysis successfully. Analysis of the variables determined in the respective data set is being developed for evaluating correspondence findings based on which the description of the dyslipidaemia study has been generated efficiently. Respective studies have focused on evaluating the objective of the study and justification of the usage of the methods for statistical analysis and the key issues that are being evaluated in the statistical method are being determined gradually. The further sectioning of the research analysts has been focused on developing discussion segmentation on the outputs generated from the respective questions which help in demonstrating an effective explanation and findings of the impact respectively.

Objectives of the study

The objective of the study is to demonstrate an effective analysis based on the questions demonstrated helps in recording different classifications and representations of graphical analysis of dyslipidaemia data for demonstrating effective comparison and calculations of different standards based on which the evaluations of considered modules and outcomes can be determined successfully. The visualisations that can be developed through statistical analysis in SPSS software help in providing an effective justification of the findings that are demonstrated in the respective research work for concluding the advantages and challenges of the statistical method.

Justification of the usage

The attainment of SPSS statistical analysis has been determined in the respective research work because it helps in elaborating dynamic tables and graphs which can provide an effective data management segmentation of the data set for providing both quantitative and quantitative simulation of dyslipidaemia information respectively. The demonstration of detailed calculations and graphical representations can be associated in the respective section based on which the overall findings of the research work can be concluded successfully.

Key issues related to the chosen statistical methods

The effective issues that are associated with the respective chosen statistical method of SPSS have demonstrated that it is unable to determine a level of measurements and calculations of outputs and thus the demonstration of standard deviation evaluation is being concluded manually. Following this, the probability segmentation of the given distribution of the data set is also unable to fulfil through statistical analysis of SPSS and thus it is a major drawback and promotes issues in the analytical procedure. The identification of complete analysis is not developed through the SPSS method and thus major output and findings a developed through manual calculations and procedures which demonstrate manual findings thus the respective segmentation of the analysis are not evaluated in statistical procedure and it is unable to promote mathematics active tracking and analysis of the calculations is not attend in the respective method efficiently and promotes lack of opportunity in measurements.


The discussion of the analysis focuses on evaluating the context of the questions in the assignment based on which the hypothesis and the objective of the study are being evaluated efficiently for determining an insight valuation of effective categories based on which graphical representation and mathematical calculations are being conducted for demonstrating the calculations and evaluating the impact of calculations efficiently.

Question 1 Output

The respective analysis focuses on evaluating the variables of the data set into categories of nominal ordinal and discrete based on which the further progress of analysis can be objectified. Respective segments of the data set on demonstrated as follows:

  1. Age_gr: Ordinal
  2. Gender: Nominal
  3. Occupation: Ordinal
  4. BP_diastolic: Discrete
  5. HTN: Nominal
  6. Duration diabetes: Discrete
  7. HbA1c_gr: Nominal
  8. T_Chol_gr: Nominal

The following are the 8 variables that are the monster rated in nominal ordinal and discrete segmentation as per the evaluation of the dataset respectively. However, the evaluation of nominal data is determined as a label variable which is demonstrated without any quantitative value and thus it can’t be compared with other segmentations. Following that the ordinal data is evaluated as a natural ordering based on which the presence of the positions in the scale is determined as a natural order the observations that are associated with ordinal data segmentation focus on the arithmetical task and determine qualitative data valuation and relative positions of the data attributes in and proper sequence. Lastly, the discrete data focuses on determining and separate or distinct segmentation of data that valuation comes under an integer value which promotes countable functionality and data representation can be subdivided into bar graphs or frequency tables as per the requirement of the data set and calculation respectively.

Question 2 Output

The respective significance of the question focuses on addressing the analysis of the relationship between the presence of dyslipidaemia and the other variables such as duration of diabetes groups, body mass index category group and occupation type group respectively. Therefore the evaluation of the respective analysis has been demonstrated through a stacked bar graph for determining the finding attendance from the respective variables and categories efficiently.

The solution evaluated in the respective graph has determined that the highest number of duration categories is wind determined in both non-dyslipidaemia and dyslipidaemia segments. Following this, the lower number of attributes attended is the body mass index category in both non-dyslipidaemia and dyslipidaemia categories efficiently.

Question 3 output

The third solution of the analysis focuses on promoting and comparative segments based on three appropriate graphs that promote the relationship between HbA1c and the following factors namely age group mode of treatment and occupation type respectively.

The first analysis has promoted the level of age categories in the respective segment based on which it is determined that the highest number of the age groups of greater than 40 years have determined the highest level of HbA1c category efficiently.

The second generated graph has determined a relationship between HbA1c and occupation type segmentation and based on the analysis it is evaluated that sedentary workers have attained the highest number of HbA1c categories respectively.

The last relationship graph promotes the evaluation of visualisation of the HbA1c category and mode of treatment which attains insulin is being demonstrated in the respective bar graph. Therefore as per the evaluation, it is generated that the maximum number of HbA1c categories is determined in the segment of insulin-taking category.

The final graph which is generated is focused on evaluating and comparative analysis of each category’s occupation type and taking insulin as a mode of treatment is evaluated in the category of HbA1c respectively. Therefore the analysis has demonstrated that there are two segments namely greater than 7 controlled HbA1c categories and lower than equal to 7 uncontrolled HbA1c categories respectively. Therefore the visualization determines that the highest number of attainment in the HbA1c category level is determined in age categories in both controlled and uncontrolled segments and following which the occupation type is determined in both control and uncontrolled variations. And the lowest determined in both controlled and uncontrolled segments are taking insulin patients in both categories of HbA1c level respectively.

Question 4 output

The valuation of the question focuses on determining and summarizing statistical analysis comparing different variables based on the status of dyslipidaemia respectively. Therefore different variables such as age, gender, HbA1c, hypertension, duration of insulin and physical activity level are being promoted in a graphical representation efficiently.

As per the analysis generated from the respective graphical representation, it is determined that the age of the patient has been attended to in the highest level of the graph in both the categories of non-dyslipidaemia and dyslipidaemia segments respectively. Therefore following which the second segment that is determined in the respective analysis is evaluated as fasting HbA1c level determined in the patient in the second segment in both non-dyslipidaemia and dyslipidaemia categories respectively. Following the respective pattern the next segment is determined as a patient of gender then physical activity level duration of insulin and hypertension in the respective patient based on the categories of dyslipidaemia.

Question 5 output

The determination of the fifth solution focuses on evaluating mathematical calculations based on the lifetime mechanical aortic valve and determining the chances that the replacement can be determined efficiently. The second segment of the solution focuses on evaluating the standard deviation and mean age of the data based on which probability distribution and mean segment have been calculated in all the sections.

The respective solutions are determined in manual calculations in three different sections A, B and C based on which the calculations are evaluated efficiently.

Question 6 output

The determination of the respective solution promotes the population distribution based on which the effective attainment of a sampling distribution and sample main length has been signified based on 2500 lung cancer patients from the respective population. Therefore the evaluation determines that the sampling distribution of the graphical analysis will be determined as positive and promotes up and down graphs in the respective segment. The demonstration of the central limit theorem determines the distribution of sample means that is attended to in an approximate segmentation in normal distribution based on which the analysis has been approximated. The considered efficiency of the central limit theorem helps in analysing the sample mean and the size of the sample based on waste the population distribution can be calculated successfully. The further demonstration of the respective analysis has evaluated mathematical calculations based on which the possibility determination of given valves and the changes have been calculated efficiently based on which the estimated value has been calculated. The following segment of the analysis has focused on determining the time spent by the employees based on which the mean is when calculated for determining the value of standardized deviation respectively. Lastly, the calculation focuses on determining and probability testing in which the normal distribution and standard deviation are when calculated for analysing the mean score respectively.

Question 7 output

The consideration of the objective determined in the module evaluates the calculations and summarisation determined through statistical analysis in dyslipidaemia study efficiently. The aspects of the findings and solutions determined effective considerations on the visualisations of the findings and sampling of the mean score and standard deviation effectively.

Impact of the findings

The respective impact determined through the findings has evaluated that the sampling distribution and analysis have been assessed based on which the analysis of dyslipidaemia study has been evaluated in corresponding variables and statistical measurements efficiently. The respective segment is developed through demographic questionnaires based on which the representations of the respective solutions have developed an insight analysis of variable distribution in nominal ordinal and discrete segments. Therefore the determination is focused on developing a relationship graphical segment of the duration of diabetes groups and occupation type and BMI group which helps in evaluating the overall concept of connectivity and dependency of variables on each other for determining the flow of information in the respective study. The following section focuses on determining and demonstrating three comparative graphical representations of HbA1c level categories and different variables such as age group mode of treatment in insulin and occupation type based on which the mean evaluation of the variables are considered to determine the analysis.


The report focuses on obtaining various statistical graphical representations based on the given questions along with that given dataset. In the report question, 2 – 4 are conducted based on the knowledge which is acquired from module assessment 1 where a graphical representation is done and conducted for the betterment of the study. On the contrary, the obtained bar plotting graph assisted this study work to understand and acquire in-depth knowledge from the above-conducted analysis. Moreover, there are various benefits which are obtained from conducting analysis using SPSS software such as data from surveys being transferred to the specialised SPSS application. With the SAV format, data extraction, processing, and analysis are easy and uncomplicated. SPSS uses the. SAV format to automatically build up and import the selected variable names, variable types, titles, and value labels, substantially simplifying the process for researchers. The illustration of the results has determined the attribute of analysing the suggestion on the determining of the dyslipidaemia analysis of the study respectively. The evaluation of the solutions evaluated through the objectives and aspects helps in evaluating the suggestions of mathematical calculations and the solutions of signified attributes.

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