Employee Performance Analysis

Employee Performance AnalysisProject OutlineThis seek is nearly the Employee movement in an memorial tablet. info related to several factors such as Employee productiveness, guest gladnesss stigmatises, the true punctuates, baffle and eon of Employees is taken into consideration. statistical modes argon utilise to identify if there is any impact of ripen and get down of Employees on factors such as Productivity, client joy and Accuracy.Theoretical modellingXYZ Corporation operating let on of Illinois, US want to get word pop if the progress and be intimate of employees spend a penny an impact on his/her cognitive operation. They spend a penny hired an external adviser to dirty dogvass the impact of these two factors ( term and experience) on the performance metrics of the employees. According to the results of the research doed by this external consultant, XYZ Corporate lead design a strategy of recruiting the right talent which will have maximum per formance.Design and MethodologyDesign and Methodology use by the external consultant include identifying the various performance factors common across different businesses within XYZ Corporation. The performance invoices common for all businesses included guest atonement ScoresAccuracy ScoresProductivityThe consultants decided to study the impact of age of employees and their experience on the to a higher place factors by utilize statistical methods.Details on participants and take in methods take in MethodsSampling is the touch on of selecting a small chassis of elements from a larger defined stain group of elements. Population is the total group of elements we want to study. Sample is the subgroup of the world we actually study. Sample would lowly a group of n employees chosen randomly from organization of population N. Sampling is done in situations sameWe assay when the process involves destructive interrogation, e.g. taste tests, car calve tests, etc.We sample w hen there are constraints of time and costsWe sample when the populations cannot be easily capturedSampling is NOT done in situations likeWe cannot sample when the events and products are unique and cannot be replicableSampling can be done by using several methods including naive random sampling, bedded random sampling, Systematic sampling and Cluster sampling. These are Probability Sampling Methods. Sampling can similarly be done using methods such as Convenience sampling, Judgment sampling, Quota sampling and Snowball sampling. These are non- prospect methods of sampling.Simple random sampling is a method of sampling in which either unit has equal chance of being selected. Stratified random sampling is a method of sampling in which stratum/groups are created and accordingly units are picked randomly. Systematic sampling is a method of sampling in which every nth unit is selected from the population. Cluster sampling is a method of sampling in which clusters are sampled every tth time.For the non- fortune methods, Convenience sampling relies upon thingamajig and access. Judgment sampling relies upon belief that participants fit characteristics. Quota sampling emphasizes representation of precise characteristics. Snowball sampling relies upon respondent referrals of others with like characteristics.In our research, the consultant organization utilize a Simple Random Sampling method to conduct the study where they chose about 75 random employees and gathered data of age, experience, their customer Satisfaction scores, their Accuracy Scores and their Productivity scores.The employees were bifurcated into 3 age groups, namely, 20 30 age, 30 40 age and 40 50 eld. Similarly, they were also bifurcated into 3 experience groups, namely, 0 10 years, 10 20 years and 20 30 years.Data Analysis beneath are the different data abstract options apply by the consultant wallop of epoch on Accuracy clashing of be on AccuracyImpact of suppurate on guest Sa tisfactionImpact of Experience on Customer SatisfactionImpact of come along on ProductivityImpact of Experience on ProductivityFor each of the above statistical analysis, we will need to use scheme exam methods. surmise testing tells us whether there exists statistically significant difference betwixt the data sets for us to consider to represent different distribution. The difference that can be detected using meditation testing isContinuous DataDifference in AverageDifference in Variation discrete DataDifference in Proportion DefectiveWe follow the at a lower place steps for shot testingStep 1 Determine leave speculation testStep 2 State the Null Hypothesis Ho and Alternate Hypothesis HaStep 3 account Test Statistics / P-value against table value of test statisticStep 4 watch results Accept or spurn HoThe mechanism of Hypothesis testing involves the followingHo = Null Hypothesis There is No statistically significant difference between the two groupsHa = Alterna te Hypothesis There is statistically significant difference between the two groupsWe also have different types of errors that can be caused if we are using supposition testing. The errors are as noted belowType I illusion P (Reject Ho when Ho is true) = Type II delusion P (Accept Ho when Ho is false) = P Value Statistical Measure which indicates the probability of making an error. The value ranges between 0 and 1. We normally work with 5% alpha risk, a p value lower than 0.05 mingys that we reject the Null conjecture and accept alternate hypothesis.Lets blab a little about p-value. It is a Statistical Measure which indicates the probability of making an error. The value ranges between 0 and 1. We normally work with 5% alpha risk. should be specified before the hypothesis test is conducted. If the p-value is 0.05, and so Ho is true and there is no difference in the groups (Accept Ho). If the p-value is 0.05, then Ho is false and there is a statistically significant d ifference in the groups (Reject Ho).We will also discuss about the types of hypothesis testing1-Sample t-test Its used when we have Normal Continuous Y and distinguishable X. It is used for examine a population mean against a presumptuousness standard. For example Is the mean Turn Around Time of thread 15 minutes.2-Sample t-test Its used when we have Normal Continuous Y and separate X. It is used for comparing means of two different populations. For example Is the mean performance of morning shift = mean performance of night shift.analysis of variance Its used when we have Normal Continuous Y and decided X. It is used for comparing the means of more than two populations. For example Is the mean performance of stave A = mean performance of staff B = mean performance of staff C.Homogeneity Of Variance Its used when we have Normal Continuous Y and Discrete X. It is used for comparing the variance of two or more than two populations. For example Is the adaptation of staff A = vari ation of staff B = variation of staff C.Moods Median Test Its used when we have Non-normal Continuous Y and Discrete X. It is used for Comparing the medials of two or more than two populations. For example Is the median of staff A = median of staff B = median of staff C.Simple Linear lapse Its used when we have Continuous Y and Continuous X. It is used to see how issue (Y) changes as the input (X) changes. For example If we need to find out how staff As accuracy is related to his number of years spent in the process.Chi-square Test of Independence Its used when we have Discrete Y and Discrete X. It is used to see how output counts (Y) from two or more sub-groups (X) differ. For example If we want to find out whether defects from morning shift are significantly different from defects in the flush shift.Lets look at each of the analysis for our researchImpact of Age on Accuracyhard-nosed ProblemHypothesisStatistical animal Used windupIs Accuracy force by Age of EmployeesH0 Accur acy is independent of the Age of EmployeesH1 Accuracy is wedge by Age of Employeesone-way ANOVAp-value 0.05 indicates that performance stones throw of accuracy is force by age factor unidirectional ANOVA Accuracy versus Age place germ DF SS MS F PAge place 2 0.50616 0.25308 67.62 0.000Error 72 0.26946 0.00374 do 74 0.77562S = 0.06118 R-Sq = 65.26% R-Sq(adj) = 64.29% soulfulness 95% CIs For taut establish onPooled StDev take N specify StDev ++++20 30 years 26 0.75448 0.06376 (*)30 40 years 26 0.85078 0.07069 (*)40 50 years 23 0.95813 0.04416 (*)++++0.770 0.840 0.910 0.980Pooled StDev = 0.06118Boxplot of Accuracy by Age Bucket purpose P-value of the above analysis 0.05 which indicates that we reject the bootless hypothesis and thus, the performance flier of accuracy is impacted by age of employees. As the age increases, we cite that the accuracy of the employees also increases.Impact of Experience on AccuracyPractical ProblemHypothesisStatistical Tool Used shutdownIs Ac curacy impacted by Experience of EmployeesH0 Accuracy is independent of the Experience of EmployeesH1 Accuracy is impacted by Experience of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of accuracy is impacted by experience factorone-way ANOVA Accuracy versus Experience Bucket reference DF SS MS F PExperience Bucke 2 0.53371 0.26685 79.42 0.000Error 72 0.24191 0.00336Total 74 0.77562S = 0.05796 R-Sq = 68.81% R-Sq(adj) = 67.94%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev -++++0 10 years 24 0.74403 0.05069 (*)10 20 years 23 0.84357 0.05354 (*)20 30 years 28 0.94696 0.06660 (*)-++++0.770 0.840 0.910 0.980Pooled StDev = 0.05796Boxplot of Accuracy by Experience Bucket closedown P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of accuracy is impacted by experience of employees. As the experience increases, we observe that the accuracy of the employees also increases.Im pact of Age on Customer SatisfactionPractical ProblemHypothesisStatistical Tool Used closeIs Customer Satisfaction Score impacted by Age of EmployeesH0 Customer Satisfaction Score is independent of the Age of EmployeesH1 Customer Satisfaction Score is impacted by Age of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Customer Satisfaction score is impacted by age factorOne-way ANOVA Customer Satisfaction versus Age BucketSource DF SS MS F PAge Bucket 2 49.51 24.75 18.92 0.000Error 72 94.23 1.31Total 74 143.74S = 1.144 R-Sq = 34.44% R-Sq(adj) = 32.62%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++20 30 years 26 6.906 1.164 (-*)30 40 years 26 8.041 1.156 (*-)40 50 years 23 8.907 1.107 (*)++++7.20 8.00 8.80 9.60Pooled StDev = 1.144Boxplot of Customer Satisfaction by Age BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Customer Satisfaction Sco re is impacted by age of employees. As the age increases, we observe that the Customer Satisfaction Score of the employees also increases.Impact of Experience on Customer SatisfactionPractical ProblemHypothesisStatistical Tool UsedConclusionIs Customer Satisfaction Score impacted by Experience of EmployeesH0 Customer Satisfaction Score is independent of the Experience of EmployeesH1 Customer Satisfaction Score is impacted by Experience of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Customer Satisfaction score is impacted by experience factorOne-way ANOVA Customer Satisfaction versus Experience BucketSource DF SS MS F PExperience Bucke 2 51.20 25.60 19.92 0.000Error 72 92.54 1.29Total 74 143.74S = 1.134 R-Sq = 35.62% R-Sq(adj) = 33.83%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++-0 10 years 24 7.035 1.277 (*)10 20 years 23 7.570 0.922 (*)20 30 years 28 8.948 1.160 (-*-)++++-7.20 8.00 8.80 9.60Pooled StDev = 1.134Boxplot of Cu stomer Satisfaction by Experience BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Customer Satisfaction Score is impacted by experience of employees. As the experience increases, we observe that the Customer Satisfaction Score of the employees also increases.Impact of Age on ProductivityPractical ProblemHypothesisStatistical Tool UsedConclusionIs Productivity impacted by Age of EmployeesH0 Productivity is independent of the Age of EmployeesH1 Productivity is impacted by Age of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Productivity is impacted by experience factorOne-way ANOVA Productivity versus Age BucketSource DF SS MS F PAge Bucket 2 0.74389 0.37194 194.56 0.000Error 72 0.13765 0.00191Total 74 0.88153S = 0.04372 R-Sq = 84.39% R-Sq(adj) = 83.95%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++20 30 years 26 0.93959 0.04287 (-*)30 40 y ears 26 0.81511 0.05831 (-*-)40 50 years 23 0.69291 0.01747 (*-)++++0.720 0.800 0.880 0.960Pooled StDev = 0.04372Boxplot of Productivity by Age BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Productivity is impacted by age of employees. As the age increases, we observe that the Productivity of the employees decreases.Impact of Experience on ProductivityPractical ProblemHypothesisStatistical Tool UsedConclusionIs Productivity impacted by Experience of EmployeesH0 Productivity is independent of the Experience of EmployeesH1 Productivity is impacted by Experience of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Productivity is impacted by experience factorOne-way ANOVA Productivity versus Experience BucketSource DF SS MS F PExperience Bucke 2 0.74024 0.37012 188.61 0.000Error 72 0.14129 0.00196Total 74 0.88153S = 0.04430 R-Sq = 83.97% R-Sq(adj) = 83.53%Individual 9 5% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++-0 10 years 24 0.94474 0.03139 (*)10 20 years 23 0.83120 0.05754 (*-)20 30 years 28 0.70599 0.04118 (*-)++++-0.700 0.770 0.840 0.910Pooled StDev = 0.04430Boxplot of Productivity by Experience BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Productivity is impacted by experience of employees. As the experience increases, we observe that the Productivity of the employees decreases.Conclusion of the AnalysisAs Age and Experience increases, the Accuracy and Customer Satisfaction Scores of Employees increasesAs Age and Experience increases, the Productivity of Employees decreasesBibliographyThe data used in this analysis is self-created data using statistical software. Research enumeration (Gantt Chart) of the Project