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Statistical Data Collection and Interpretation Assessment Item 3 Introduction

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Added on  2021-06-17

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Statistical Data Collection and Interpretation Assessment Item 3 Abstract 3 Introduction 3 Research Hypotheses 4 Data Collection 5 Descriptive Statistics 4 Graphical Analysis 5 Correlation and Regression Analysis 3 Independent Qualitative Samples t-tests 3 One way ANOVA 4 Chi square test 5 Results and Discussions 4 Conclusions 5 References 5 Assessment Item 3 Statistical Data Collection and Interpretation Abstract The correlation coefficient between the two variables monthly salary and monthly expense is given as 0.957, which indicate a strong positive linear relationship. Descriptive Ratio for

Statistical Data Collection and Interpretation Assessment Item 3 Introduction

   Added on 2021-06-17

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Statistical Data Collectionand InterpretationAssessment Item 3
Statistical Data Collection and Interpretation Assessment Item 3 Introduction_1
Table of ContentsAbstract................................................................................................................................3Introduction..........................................................................................................................3Research Hypotheses............................................................................................................4Data Collection.....................................................................................................................5Descriptive Statistics............................................................................................................4Graphical Analysis...............................................................................................................5Correlation and Regression Analysis...................................................................................3Independent Samples t-tests.................................................................................................3One way ANOVA................................................................................................................4Chi square test......................................................................................................................5Results and Discussions.......................................................................................................4Conclusions..........................................................................................................................5References............................................................................................................................52 | Page
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Assessment Item 3Statistical Data Collection and InterpretationAbstractThe correlation coefficient between the two variables monthly salary and monthly expense is given as 0.957, which indicate a strong positive linear relationship. We conclude that there is a statistically significant linear relationship exists between the two variables monthly salary and monthly expense. No any statistically significant difference is observed between the average monthly salary and expense for the female and male employees. A significant difference is observed in the average number of TV hours for the female and male employees. No any significant difference is observed in the average number of hours for exercise for male and female employees. A significant difference is observed in the average monthly salary and expense for the employees with different educations. Two categorical variables found statistically independent from each other. IntroductionStatistical data analysis plays an important role in the process of decision making in many sectors. It is important to use proper statistical tools and techniques for data analysis. Here, we have to analyse the data for the different variables regarding the employees. We have to draw theconclusions for the variables such as gender, age, education, monthly salary, monthly expense, etc. We have to use descriptive statistics, graphical analysis, correlation and regression, hypotheses tests such as independent samples t tests and one factor ANOVA tests for checking different claims regarding the variables. Let us see this statistical analysis in detail. Research HypothesesFor this study of statistical data collection and analysis, we consider the following research hypotheses: 1.H0: There is no any statistically significant linear relationship exists between the two variable monthly salary and monthly expense. 2.H0: There is no any significant difference exists between the average monthly salary for the female and male employees. 3.H0: There is no any significant difference in average number of TV hours for female and male employees. 3 | Page
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4.H0: There is no any significant difference in the average number of hours for exercise formale and female employees. 5.H0: There is no any significant difference in the average monthly salary for the employees with different educations. 6.H0: There is no any statistically significant difference in the average monthly expense forthe employees with different education levels. 7.H0: There is no any statistically significant difference in the average number of TV hoursfor the employees with different education levels. 8.H0: There is no any statistically significant difference in the average number of exercise hours for the employees with different education levels. 9.H0: Two categorical variables gender and education levels are independent from each other. Data Collection For this research study, the data is collected by using the random sampling methods and this datais archived from the government website Bureau of Labour Statistics. Data is collected for 100 employees including male and female employees. The list of the variables used for this research study is given as below:VariableType ScaleIDQualitative NominalGenderQualitative NominalAgeQuantitativeRatioEducationQualitative OrdinalMonthly Salary ($)QuantitativeRatioMonthly Expense ($)QuantitativeRatioMedi-claim InsuranceQualitative NominalPension PlanQualitative NominalExercise in hour per weekQuantitativeRatioTV hour per weekQuantitativeRatio4 | Page
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Descriptive Statistics First of all, we have to discuss the descriptive statistics for the variables included in the given data set. We know that the descriptive statistics gives us an idea about the nature of data for corresponding variable. Descriptive statistics for the variable age is summarised as below:Descriptive StatisticsNMinimumMaximumMeanStd.DeviationAge10027.0058.0043.92008.78553Valid N (listwise)100The average age of the all employees or participants in the given data is given as 43.92 years with the standard deviation of 8.79 years. The minimum age of the participant is observed as 27 year while the maximum age is observed as 58 years. Now, we have to see the descriptive statistics for the variable monthly salary which is summarised as below:Descriptive StatisticsNMinimumMaximumMeanStd.DeviationMonthly Salary ($)1004392.0010569.007481.73001378.83790Valid N (listwise)100The average salary for all employees is given as $7481.73 per month with the standard deviation of $1378.84. The minimum salary is observed as $4392 while the maximum salary is observed as $10569.Descriptive statistics for the variable monthly expense is given as below:5 | Page
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Descriptive StatisticsNMinimumMaximumMeanStd.DeviationMonthly Expense ($)1002081.009257.005643.88001478.30870Valid N (listwise)100From above table, it is observed that the average monthly expense for employees is given as $5643.88 with the standard deviation of $1478.31. Minimum monthly expense is given as $2081,while maximum monthly expense is given as $9257.00. Descriptive statistics for the variable exercise in hours per week are summarised in the followingtable:Descriptive StatisticsNMinimumMaximumMeanStd.DeviationExercise in hours per week100.005.002.34001.75361Valid N (listwise)100Average number of hours per week for all employees is given as 2.34 hour with the standard deviation of 1.75. The minimum number of hours per week is observed as 0, while the maximumnumber of hours per week for exercise is given as 5. Descriptive statistics for the variable TV hour per week is given as below:Descriptive StatisticsNMinimumMaximumMeanStd.DeviationTV hour per week1004.0020.0011.37005.02047Valid N (listwise)1006 | Page
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From above table, it is observed that the average TV hour per week for all employees is given as 11.37 hour with the standard deviation of 5.02 hours. The minimum number of TV hour is given as 4, while maximum number of TV hour is given as 20. Now, we have to see some frequency distributions for the variables included in the given study. The frequency distribution for the variable gender is given as below:GenderFrequencyPercentValid PercentCumulativePercentValidFemale5454.054.054.0Male4646.046.0100.0Total100100.0100.0The frequency distribution of the variable education is given as below:EducationFrequencyPercentValid PercentCumulativePercentValidLess than graduation3434.034.034.0Graduation3232.032.066.0Post-graduation or more3434.034.0100.0Total100100.0100.0Frequency distribution for the variable whether employee have a mediclaim policy or not is given as below:7 | Page
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Mediclaim InsuranceFrequencyPercentValid PercentCumulativePercentValidNo5555.055.055.0Yes4545.045.0100.0Total100100.0100.0Frequency distribution for the variable pension plan is summarised as below:Pension planFrequencyPercentValid PercentCumulativePercentValidNo5454.054.054.0Yes4646.046.0100.0Total100100.0100.0Graphical AnalysisIn this section, we have to see some graphical analysis for the different variables under this study. Graphical analysis plays an important role in easy understanding of the concepts of statistical analysis. For this graphical analysis, we have to use bar charts and box plots for comparison purpose. All graphical comparisons are provided in the appendix part at the end of this report. Correlation and Linear Regression The study of correlation gives the relationship between the two variables. The technique of linearregression is useful for the prediction of the response variable or dependent variable. Here, we have to check whether the two variables monthly salary and monthly expense are related to each other or not. We have to check whether the relationship between two variables is statistically significant or not. First of all we have to see the scatter plot for the given two variables monthly salary and monthly expense. By using scatter plot, we have to check the relationship between thetwo variables. Required scatter diagram for the given two variables is given as below:8 | Page
Statistical Data Collection and Interpretation Assessment Item 3 Introduction_8

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