Assignment-1 MIS771 Descriptive Analytics and Visualisations Page 1 of 10 MIS771 Descriptive Analytics and Visualisation DEPARTMENT OF INFORMATION SYSTEMS AND BUSINESS ANALYTICS DEAKIN BUSINESS SCHOOL...

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Assignment-1 MIS771 Descriptive Analytics and Visualisations Page 1 of 10 MIS771 Descriptive Analytics and Visualisation DEPARTMENT OF INFORMATION SYSTEMS AND BUSINESS ANALYTICS DEAKIN BUSINESS SCHOOL FACULTY OF BUSINESS AND LAW, DEAKIN UNIVERSITY Assignment Two Background This is an individual assignment. You need to analyse a given dataset, and then interpret and draw conclusions from your analysis. You then need to convey your findings in a written report to an expert in Business Analytics. Percentage of the final grade 35% The Due Date and Time 8 pm Thursday 17th September 2020 Submission instructions The assignment must be submitted by the due date, electronically in CloudDeakin. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in CloudDeakin. Please note that we will NOT accept any paper or email copies, or part of the assignment submitted after the due date. Information for students seeking an extension BEFORE the due date If you wish to seek an extension for this assignment before the due date, you need to apply directly to the Unit Chair by completing the Assignment and Online Test Extension Application Form before Friday 5 pm 17th Thursday September 2020. Please make sure you attach all supporting documentation and a draft of your assignment. The request for extension needs to occur as soon as you become aware that you will have difficulty in meeting the due date. Please note: Unit Chairs can only grant extensions of up to two weeks beyond the original due date. If you require more than two weeks, or have already been provided with an extension by the Unit Chair and require additional time, you must apply for Special Consideration via StudentConnect within 3 business days of the due date. Conditions under which an extension will usually be considered include: • Medical – to cover medical conditions of a severe nature, e.g. hospitalisation, serious injury or chronic illness. Note: temporary minor ailments such as headaches, colds and minor gastric upsets are not serious medical conditions and are unlikely to be accepted. However, serious cases of these may be considered. • Compassionate – e.g. death of a close family member, significant family and relationship problems. • Hardship/Trauma – e.g. sudden loss or gain of employment, severe disruption to domestic arrangements, a victim of crime. Note: misreading the due date, assignment anxiety, or multiple assignments will not be accepted as grounds for consideration. https://www.deakin.edu.au/students/faculties/buslaw/student-support/assignment-extensions MIS771 Descriptive Analytics and Visualisations Page 2 of 10 Information for students seeking an extension AFTER the due date If the due date has passed; you require more than two weeks extension, or you have already been provided with an extension and require additional time, you must apply for Special Consideration via StudentConnect. Please be aware that applications are governed by University procedures and must be submitted within three business days of the due date or extension due date. Please be aware that in most instances the maximum amount of time that can be granted for an assignment extension is three weeks after the due date, as Unit Chairs are required to have all assignment submitted before results/feedback can be released back to students. Penalties for late submission The following marking penalties will apply if you submit an assessment task after the due date without an approved extension: • 5% will be deducted from available marks for each day, or part thereof, up to five days. • Work that is submitted more than five days after the due date will not be marked; you will receive 0% for the task. Note: 'Day' means calendar day. The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date. Additional information: For advice regarding academic misconduct, special consideration, extensions, and assessment feedback, please refer to the document "Rights and responsibilities as a student" in the "Unit Guide and Information" folder under the "Resources" section in the MIS771 CloudDeakin site. The assignment uses the dataset file T22020MIS771_A2Data.xlsx, which can be downloaded from CloudDeakin. Analysis of the data requires the use of techniques studied in Module-2. Assurance of Learning This assignment assesses the following Graduate Learning Outcomes and related Unit Learning Outcomes: Graduate Learning Outcome (GLO) Unit Learning Outcome (ULO) GLO1: Discipline-specific knowledge and capabilities - appropriate to the level of study related to a discipline or profession. GLO2: Communication - using oral, written and interpersonal communication to inform, motivate and effect change GLO5: Problem Solving - creating solutions to authentic (real world and ill-defined) problems. GLO6: Self-Management - working and learning independently, and taking responsibility for personal actions ULO 1: Apply quantitative reasoning skills to solve complex problems. ULO 2: Plan, monitor, and evaluate own learning as a data analyst. ULO 3: Deduce clear and unambiguous solutions in a form that they useful for decision making and research purposes and for communication to the wider public. MIS771 Descriptive Analytics and Visualisations Page 3 of 10 Feedback before submission You can seek assistance from the teaching staff to ascertain whether the assignment conforms to submission guidelines. Feedback after submission An overall mark together with feedback, will be released via CloudDeakin, usually within 15 working days. You are expected to refer and compare your answers to the feedback to understand any areas of improvement. The Case Study ANALYTICs7, a leading data analysis consulting company, has extensive experience in analysing data for both local and global, small to medium companies. By solving their business problems, ANALYTICs 7 helps these businesses to plan ahead and thrive. Your Role in ANALYTICS7 Dr Hugo Barra, the lead data scientist at ANALYTICs7 has engaged you to lead the modelling component for the TPM and AP projects and construct a report of your key findings and recommendations in response to the questions posed in the meeting minutes of the last team meeting on the next page. Datasets (accessible via T22020MIS771_A2Data.xlsx file) There are two datasets available for this assignment: TPM_Employee_Attrition and Monthly_EnergyCon_MW Employee Survey data (TPM_Employee_Attrition )– TassPaperMill (TPM), a subsidiary of Pinnon Paper Industries (PPI), is an Australian company with a long history of manufacturing paper rolls. To address numerous concerns raised in their recent employee survey TPM is currently reviewing how they calculate salary increments for their employees. TPM has hired ANALYTICs7 to extract a random sample of 1470 employee records from their HR database. Their ultimate goal is to adopt a more holistic rewarding system factoring the key relations between remuneration indicators and demographic characteristics, employment history and various other potential contributors to boost performance. In addition, human resource manager at TPM reported in her recent presentation to the company executive management team that the staff turnover rate at TPM is higher compared to their competitors. Thus, TMP wants to identify key contributing factors before they lose more talented, motivated and focused employees who contribute to the organisation's overall success. Energy consumption data (Monthly_EnergyCon_MW) – Australian Power (AP) is one of the largest generators of electricity in Australia, servicing for more than three million households in Victoria. AP operates an electric transmission system that covers much of Victoria and serves over 30% of the electricity demand in Victoria. This dataset consists of monthly power consumption data in megawatts (MW) comes from AP’s data warehouse during 2010-2019. AP wishes to review their current resources allocation strategy to plan and prioritise the provision of resources based on rapidly growing energy demand in Victoria. A complete listing of variables is provided in the T22020MIS771_A2Data.xlsx file. Note: All data, reports, people and scenarios in this assignment are either fictitious or have been modified from their original state. Any similarity to actual events is purely coincidental. It has been produced for the sole purpose of assessing performance of summative assessment task 2. MIS771 Descriptive Analytics and Visualisations Page 4 of 10 Form 210-3 ANALYTICS7 Team Meeting ANALYTICS7 727 Collins St, Docklands VIC 3008 Phone: (+61 3 212 66 000) [email protected] Reference AP-211 TPM Project Revised 27th August 2020 Level Expert Analysis Meeting Chair Dr Hugo Barra Date 24 August 2020 Time 10:00 AM Location ANALYTICS7 L4.320 Topic TPM and AP Research Projects – Analytics Details Meeting Purpose: Specifying and Allocating Data Analytics Tasks Discussion items: 1. Variable(s) description 2. Modelling PercentSalaryHike 3. Modelling the likelihood of an employee leaving the company 4. Forecasting monthly energy consumption in Megawatts 5. Producing a technical report Detailed Action Items Who: Modeller What: 1. Providing an overall summary of the following two variables: 1.1. Percentage increase in salary (PercentSalaryHike) 1.2. Attrition 2. Identify potential variables that may influence PercentSalaryHike: 2.1. Identify a list of possible variables that influence percentage increase in salary. Which three independent variables have the more impactful linear relationship with PercentSalaryHike? What form of relationship(s) exist between the independent variable(s) and PercentSalaryHike? Are there any potential multi-collinearity problems? If so, which variables are they? 2.2. Build a regression model to estimate percentage increase in salary. 2.3. Perform residual analysis. Based on your residual plots, does there appear to be any problems with the regression model? 3. Hugo has performed some preliminary analysis and discovered that the performance rating is a significant predictor of the Percentage increase in salary. Prior research shows that the strength of the relationship between performance rating and percentage increase in salary may vary according to satisfaction with the job. Generally speaking increased job satisfaction creates a more productive workforce as they are more motivated to improve their job performance. MIS771 Descriptive Analytics and Visualisations Page 5 of 10 Therefore, Hugo believes that the relationship between performance rating and percentage increase in salary should be stronger for employees who
Answered Same DaySep 14, 2021MIS771Deakin University

Answer To: Assignment-1 MIS771 Descriptive Analytics and Visualisations Page 1 of 10 MIS771 Descriptive...

Pooja answered on Sep 16 2021
132 Votes
Table of Contents
Introduction    2
Main Body    2
1)    Descriptive statistics of salary hike and attrition    2
2)    Regression model to predict the percent salary hike    2
2.1) Significant independent variables    2
2.2) Model    3
2.3) Residual Analysis    3
3) Model with interaction term of performance rating and job satisfaction    3
3.1) Model    3
3.2) Significance of interaction term    3
5) Prediction of Energy Consumption    3
Conclusion    4
Appendices    5
1)    Descriptive statistics of salary hike and attrition    5
2) Regression model to predict the percent salary hike    6
2.1) Significant independent variables    6
2.2) Model    7
2.3) Residual Analysis    8
3) Model with interaction term of performance rating and job satisfaction    9
5) Prediction of Energy Consumption    10
Introduction
Hugo wants to analyse the salary hike percentage on the basis of Age, Attrition, Education, Environment Satisfaction, Gender, Job Involvement, Job Level, Job Satisfaction, Marital Status, Number Companies Worked, Over Time, Performance Rating, Work-Life Balance, Years At Company, Years In Current Role, Years Since Last Promotion. The techniques of regression analysis and descriptive statistics are used to analyse the same.
Main Body
1) Descriptive statistics of salary hike and attrition
The average percent salary hike of a person is 15% with a low standard deviation of 3.6 %. The minimum and the maximum salary hike are 11% and 25% respectively. From the histogram of percent salary hike, it is evident that the distribution is positively skewed. This implies there are very few Employees with high percent salary hikes. 
Most of the employees do not have attrition. There are only 16% of the employee is with attrition and 84% of the employees without attrition.
2) Regression model to predict the percent salary hike
2.1) Significant independent variables
In an attempt to build the first, all 16 independent variables namely Age, Attrition, Education, Environment Satisfaction, Gender, Job Involvement, Job Level, Job Satisfaction, Marital Status, Number Companies Worked, Over Time, Performance Rating, Work-Life Balance, Years At Company, Years In Current Role, Years Since Last Promotion are considered. The insignificant independent variables are Attrition, Education, Environment Satisfaction, Job Level, Marital Status, Over Time, Work-Life Balance, Years At Company, Years In Current Role, Years Since Last Promotion with p>5%. The significant independent variables in the model are age gender job involvement and job satisfaction number of companies work and performance rating with the P-value of less than 5%. 
The three independent variables having the most powerful impact as a linear relationship with percent salary hike are performance rating coma job satisfaction and gender. This is because the magnitude of the coefficient of performance rating a job satisfaction for my and gender is the highest in comparison to the coefficient value of other significant independent variables.
There is a strong positive linear relationship between performance rating and percent salary hike with r = 0.76. This implies as the value of performance rating increases from up the value of salary hike also increases drastically.  There is a moderate negative linear relationship between the number of companies worked and percent salary hike. As the value of number of companies work increases the value of percent salary hike decreases. 
The problem of multicollinearity arises when any two independent variables are strongly correlated in the model. There appears to be a strong positive linear relationship between (years in current role, years at company), (years since last promotion, years at company), (years since last promotion, years in current role).  However, in my final model none of these independent variables are involved as they do not have a significant linear relationship with percent salary hike. 
2.2) Model
The regression equation to predict the percent salary hike ok is given by:  percent salary hike = -4.223 + 0.870*female + 0.655*Job Involvement + 0.879*Job Satisfaction + 0.009*Age -0.283*Num Companies Worked + 4.846*Performance Rating
For females the percent salary hike is 0.87% more than males. For one Level increase in job involvement the salary hike is expected to increase by 0.65%. With one Level increase in job satisfaction per the salary hike is expected to increase by 0.87%. With increase in age the the salary hike is expected to increase by 0.009%. With one company increased in working, the salary hike is decreased by 0.28%. For one Level increase in performance rating the salary hike is increased by 4.84%.
2.3) Residual Analysis
All points in the residential plot a randomly distributed indicating that their variance is constant. Thus, the assumption of homogeneity of error variance is satisfied. 
3) Model with interaction term of performance rating and job satisfaction

3.1) Model
The regression equation to predict the percent salary hike ok is given by:  percent salary hike = 1.818 + 0.853*female + 0.645*Job Involvement + -0.942*Job Satisfaction + 0.008*Age -0.284*Num Companies Worked + 2.863*Performance Rating + 0.598*Performance Rating*Job Satisfaction
For females the percent salary hike is 0.85% more than males. For one Level increase in job involvement the salary hike is expected to increase by 0.645%. With one Level increase in job satisfaction per the salary hike is expected to decrease by 0.94%. With increase in age the the salary hike is expected to increase by 0.008%. With one company increased in working, the salary hike is decreased by 0.28%. For one Level increase in performance rating the salary hike is increased by 2.86%. For 1 level increase in performance rating and job satisfaction, the percent salary hike is increased my 0.59%.
3.2) Significance of interaction term
Null hypothesis, ho: the coefficient of interaction term for performance rating and job satisfaction is not significant.
Alternative hypothesis, the coefficient of interaction term for performance rating and job satisfaction is significant.
With t=4.9, p<5% reject the null hypothesis at 5% level of significance and conclude that the coefficient of interaction term for performance rating and job satisfaction is significant.
With not much difference in the value of adjusted R2 for model in 2.2 (R2 Adj = 0.82) and 3 (R2 Adj = 0.824), model 2.2 is preferred as it has less number of independent variables.
5) Prediction of Energy Consumption
The predicted energy consumption for all months of year 2020 is:
    
    Predicted Energy Consumption
    Jan-20
    12342671
    Feb-20
    10833425
    Mar-20
    10591502
    Apr-20
    9126819
    May-20
    9593157
    Jun-20
    10431723
    Jul-20
    11362665
    Aug-20
    11207238
    Sep-20
    9693279
    Oct-20
    9452426
    Nov-20
    9897543
    Dec-20
    11345302
Conclusion
The average percent salary hike of a person is 15% with a low standard deviation of 3.6 %. The regression equation to predict the percent salary hike ok is given by: percent salary hike = -4.223 + 0.870*female + 0.655*Job Involvement + 0.879*Job Satisfaction + 0.009*Age -0.283*Num Companies Worked + 4.846*Performance Rating. For females the percent salary hike is 0.87% more than males. For one Level increase in job involvement the salary hike is expected to increase by 0.65%. With one Level increase in job satisfaction per the salary hike is expected to increase by 0.87%. With increase in age the the salary hike is expected to increase by 0.009%. With one company increased in working, the salary hike is decreased by 0.28%. For one Level increase in performance rating the salary hike is increased by 4.84%.
Appendices
1) Descriptive statistics of salary hike and attrition
    PercentSalaryHike
    
    
    Mean
    15.20952
    Standard Error
    0.095459
    Median
    14
    Mode
    11
    Standard Deviation
    3.659938
    Sample Variance
    13.39514
    Kurtosis
    -0.3006
    Skewness
    0.821128
    Range
    14
    Minimum
    11
    Maximum
    25
    Sum
    22358
    Count
    1470
    Confidence Level(95.0%)
    0.18725
    Row Labels
    Count of Attrition
    No
    1233
    Yes
    237
    Grand Total
    1470
2) Regression model to predict the percent salary hike
2.1) Significant independent variables
    SUMMARY OUTPUT
    
    
    Regression Statistics
    Multiple R
    0.907344
    R Square
    0.823273
    Adjusted R Square
    0.821327
    Standard Error
    1.547046
    Observations
    1470
    ANOVA
    
    
    
    
    
     
    df
    SS
    MS
    F
    Significance F
    Regression
    16
    16199.93
    1012.495
    423.0449
    0
    Residual
    1453
    3477.541
    2.393352
    
    
    Total
    1469
    19677.47
     
     
     
     
    Coefficients
    Standard Error
    t Stat
    P-value
    Lower 95%
    Upper 95%
    Lower 95.0%
    Upper 95.0%
    Intercept
    -4.08104
    0.473253
    -8.62338
    1.67E-17
    -5.00937
    -3.15271
    -5.00937
    -3.15271
    Age
    0.014356
    0.00522
    2.750383
    0.006026
    0.004117
    0.024595
    0.004117
    0.024595
    Attrition
    0.077437
    0.119728
    0.646777
    0.517878
    -0.15742
    0.312296
    -0.15742
    0.312296
    Education
    -0.00737
    0.051795
    -0.14224
    0.886912
    -0.10897
    0.094234
    -0.10897
    0.094234
    EnvironmentSatisfaction
    0.02372
    0.037405
    0.634141
    0.526089
    -0.04965
    0.097094
    -0.04965
    0.097094
    Gender
    0.872863
    0.093552
    9.330245
    3.81E-20
    0.689351
    1.056374
    0.689351
    1.056374
    JobInvolvement
    0.645458
    0.05035
    12.8193
    1.02E-35
    0.54669
    0.744225
    0.54669
    0.744225
    JobLevel
    -0.02674
    0.048002
    -0.55697
    0.577637
    -0.1209
    0.067426
    -0.1209
    0.067426
    JobSatisfaction
    0.875087
    0.041523
    21.07466
    3.03E-86
    0.793635
    0.956539
    0.793635
    0.956539
    MaritalStatus
    -0.07004
    0.056371
    -1.24246
    0.214267
    -0.18062
    0.040539
    -0.18062
    0.040539
    NumCompaniesWorked
    -0.28622
    0.018718
    -15.2916
    4.63E-49
    -0.32294
    -0.24951
    -0.32294
    -0.24951
    OverTime
    -0.08314
    0.09372
    -0.88709
    0.375177
    -0.26698
    0.100703
    -0.26698
    0.100703
    PerformanceRating
    4.854974
    0.122405
    39.66315
    9.5E-234
    4.614865
    5.095084
    4.614865
    5.095084
    WorkLifeBalance
    -0.03808
    0.057579
    -0.66135
    0.508494
    -0.15103
    0.074866
    -0.15103
    0.074866
    YearsAtCompany
    -0.01391
    0.011784
    -1.18077
    0.237887
    -0.03703
    0.009201
    -0.03703
    0.009201
    YearsInCurrentRole
    0.002931
    0.017564
    0.16686
    0.867503
    -0.03152
    0.037383
    -0.03152
    0.037383
    YearsSinceLastPromotion
    -0.00458
    0.016239
    -0.28204
    0.777956
    -0.03643
    0.027274
    -0.03643
    0.027274
     
    Age
    NumCompaniesWorked
    OverTime
    PerformanceRating
    WorkLifeBalance
    YearsAtCompany
    YearsInCurrentRole
    YearsSinceLastPromotion
    PercentSalaryHike
    Age
    1
    
    
    
    
    
    
    
    
    NumCompaniesWorked
    0.078664
    1
    
    
    
    
    
    
    
    OverTime
    0.028062
    -0.01547
    1
    
    
    
    
    
    
    PerformanceRating
    -0.00687
    -0.24505
    0.006249
    1
    
    
    
    
    
    WorkLifeBalance
    -0.02149
    0.014318
    -0.02709
    0.002899
    1
    
    
    
    
    YearsAtCompany
    0.311309
    -0.00183
    -0.01169
    0.003301
    0.012089
    1
    
    
    
    YearsInCurrentRole
    0.212901
    -0.00221
    -0.02976
    0.033066
    0.049856
    0.758754
    1
    
    
    YearsSinceLastPromotion
    0.216513
    0.009713
    -0.01224
    0.011995
    0.008941
    0.618409
    0.548056
    1
    
    PercentSalaryHike
    0.003634
    -0.51057
    -0.00543
    0.764766
    -0.00328
    -0.03599
    -0.00152
    -0.02215
    1
2.2) Model
    SUMMARY OUTPUT
    
    
    Regression Statistics
    Multiple R
    0.906689
    R Square
    0.822085
    Adjusted R Square
    0.821355
    Standard Error
    1.546924
    Observations
    1470
    ANOVA
    
    
    
    
    
     
    df
    SS
    MS
    F
    Significance F
    Regression
    6
    16176.54
    2696.091
    1126.669
    0
    Residual
    1463
    3500.922
    2.392975
    
    
    Total
    1469
    19677.47
     
     
     
     
    Coefficients
    Standard Error
    t Stat
    P-value
    Lower 95%
    Upper 95%
    Lower 95.0%
    Upper 95.0%
    Intercept
    -4.223
    0.398121
    -10.6081
    2.22E-25
    -5.00426
    -3.44236
    -5.00426
    -3.44236
    Gender
    0.870
    0.093229
    9.329228
    3.81E-20
    0.68688
    1.052634
    0.68688
    1.052634
    JobInvolvement
    0.655
    0.050018
    13.0898
    4.19E-37
    0.556614
    0.752844
    0.556614
    0.752844
    JobSatisfaction
    0.879
    0.041365
    21.2402
    1.75E-87
    0.797452
    0.959732
    0.797452
    0.959732
    Age
    0.009
    0.004436
    1.928885
    0.053938
    -0.00015
    0.017259
    -0.00015
    0.017259
    NumCompaniesWorked
    -0.283
    0.018652
    -15.1822
    1.87E-48
    -0.31977
    -0.24659
    -0.31977
    -0.24659
    PerformanceRating
    4.846
    0.122111
    39.68233
    2.3E-234
    4.606106
    5.085168
    4.606106
    5.085168
2.3) Residual Analysis
3) Model with interaction term of performance rating and job satisfaction
    SUMMARY OUTPUT
    
    
    Regression Statistics
    Multiple R
    0.9082975
    R Square
    0.8250044
    Adjusted R Square
    0.8241665
    Standard Error
    1.5347035
    Observations
    1470
    ANOVA
    
    
    
    
    
     
    df
    SS
    MS
    F
    Significance F
    Regression
    7
    16234
    2319.142
    984.6421
    0
    Residual
    1462
    3443.47
    2.355315
    
    
    Total
    1469
    19677.47
     
     
     
     
    Coefficients
    Standard Error
    t Stat
    P-value
    Lower 95%
    Upper 95%
    Lower 95.0%
    Upper 95.0%
    Intercept
    1.818
    1.28531
    1.414058
     0.1576
    -0.70375
    4.33875
    -0.70375
    4.33875
    Gender
    0.853
    0.092556
    9.215128
     0.0000
    0.671356
    1.034468
    0.671356
    1.034468
    JobInvolvement
    0.645
    0.04966
    12.9931
     0.0000
    0.547828
    0.742653
    0.547828
    0.742653
    JobSatisfaction
    -0.942
    0.37095
    -2.54006
     0.0112
    -1.66989
    -0.21458
    -1.66989
    -0.21458
    Age
    0.008
    0.004403
    1.818288
     0.0692
    -0.00063
    0.016641
    -0.00063
    0.016641
    NumCompaniesWorked
    -0.284
    0.018506
    -15.3579
     0.0000
    -0.32051
    -0.24791
    -0.32051
    -0.24791
    PerformanceRating
    2.863
    0.419252
    6.829642
     0.0000
    2.040939
    3.685737
    2.040939
    3.685737
    PerformanceRating*JobSatisfaction
    0.598
    0.121061
    4.938869
     0.0000
    0.360432
    0.835374
    0.360432
    0.835374
5) Prediction of Energy Consumption
    SUMMARY OUTPUT
    
    
    Regression Statistics
    Multiple R
    0.907472
    R Square
    0.823506
    Adjusted R Square
    0.803712
    Standard Error
    490220.7
    Observations
    120
    ANOVA
    
    
    
    
    
     
    df
    SS
    MS
    F
    Significance F
    Regression
    12
    1.2E+14
    9.99819E+12
    41.60427275
    9.27716E-35
    Residual
    107
    2.57E+13
    2.40316E+11
    
    
    Total
    119
    1.46E+14
     
     
     
     
    Coefficients
    Standard Error
    t Stat
    P-value
    Lower 95%
    Upper 95%
    Lower 95.0%
    Upper 95.0%
    Intercept
    12787329
    177128.9
    72.19224041
    1.35493E-92
    12436191.42
    13138466
    12436191.4
    13138466
    t
    -10924.4
    1298.353
    -8.41407967
    1.91191E-13
    -13498.279
    -8350.61
    -13498.279
    -8350.61
    Jan
    877199.9
    219698.1
    3.992752208
    0.000120034
    441674.1255
    1312726
    441674.125
    1312726
    Feb
    -621122
    219617.5
    -2.82819732
    0.005589505
    -1056487.59
    -185756
    -1056487.6
    -185756
    Mar
    -852120
    219544.5
    -3.8813067
    0.000179996
    -1287341.16
    -416898
    -1287341.2
    -416898
    Apr
    -2305878
    219479.3
    -10.5061333
    3.63518E-18
    -2740970.52
    -1870786
    -2740970.5
    -1870786
    May
    -1828616
    219421.7
    -8.33379775
    2.88569E-13
    -2263593.56
    -1393638
    -2263593.6
    -1393638
    jun
    -979126
    219371.7
    -4.46331913
    2.00453E-05
    -1414004.81
    -544247
    -1414004.8
    -544247
    July
    -37259.2
    219329.4
    -0.16987791
    0.865426928
    -472054.278
    397535.8
    -472054.28
    397535.8
    Aug
    -181762
    219294.9
    -0.82884698
    0.409036109
    -616488.364
    252964.6
    -616488.36
    252964.6
    Sept
    -1684796
    219268
    -7.68373087
    7.8435E-12
    -2119469.08
    -1250123
    -2119469.1
    -1250123
    Oct
    -1914725
    219248.7
    -8.7331182
    3.69966E-14
    -2349360.13
    -1480090
    -2349360.1
    -1480090
    Nov
    -1458683
    219237.2
    -6.65344637
    1.25723E-09
    -1893295.12
    -1024071
    -1893295.1
    -1024071
    
    Jan-20
    Feb-20
    Mar-20
    Apr-20
    May-20
    Jun-20
    Jul-20
    Aug-20
    Sep-20
    Oct-20
    Nov-20
    Dec-20
    t
    121.00
    122.00
    123.00
    124.00
    125.00
    126.00
    127.00
    128.00
    129.00
    130.00
    131.00
    132.00
    Jan.
    1
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    Feb.
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    Mar.
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    0
    Apr.
    0
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    May
    0
    0
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    Jun.
    0
    0
    0
    0
    0
    1
    0
    0
    0
    0
    0
    0
    Jul.
    0
    0
    0
    0
    0
    0
    1
    0
    0
    0
    0
    0
    Aug.
    0
    0
    0
    0
    0
    0
    0
    1
    0
    0
    0
    0
    Sep.
    0
    0
    0
    0
    0
    0
    0
    0
    1
    0
    0
    0
    Oct.
    0
    0
    0
    0
    0
    0
    0
    0
    0
    1
    0
    0
    Nov.
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    1
    0
    
    
    
    
    
    
    
    
    
    
    
    
    
    Predicted Energy Consumption
    12342671
    10833425
    10591502
    9126819
    9593157
    10431723
    11362665
    11207238
    9693279
    9452426
    9897543
    11345302
JobInvolvement Residual Plot
1    3    2    1    3    3    4    3    2    1    1    2    3    2    2    2    4    4    2    2    4    4    1    2    1    3    1    1    2    1    1    1    2    2    3    4    2    1    2    3    1    1    1    3    3    1    3    3    3    4    2    1    3    2    2    1    3    3    3    2    3    1    2    2    3    1    2    3    2    2    2    3    3    3    4    3    2    1    3    3    2    2    2    1    3    1    1    3    2    2    2    3    2    2    3    1    3    3    1    1    3    1    2    3    2    2    3    2    2    3    2    4    4    1    2    3    2    1    3    1    1    1    3    1    1    2    1    3    1    2    4    1    1    3    3    3    1    2    2    3    3    1    3    2    2    3    2    2    3    1    1    3    3    3    1    3    2    2    2    3    3    3    1    4    2    3    1    1    2    3    3    4    1    1    2    3    1    2    3    1    3    3    3    2    1    3    1    1    2    3    2    3    2    4    3    4    1    3    1    3    2    3    3    3    3    3    4    1    2    3    1    2    1    2    3    3    2    2    2    2    3    2    3    1    1    2    3    1    2    2    2    1    3    1    3    3    3    2    1    2    1    3    3    3    1    2    2    2    2    4    2    3    3    2    2    1    2    3    1    2    4    2    2    1    1    2    2    4    1    1    1    1    3    1    2    3    3    4    3    3    3    3    3    3    3    2    3    1    1    3    1    2    1    2    2    1    1    3    3    2    3    2    3    1    3    3    2    2    1    1    2    3    3    1    1    3    2    2    1    2    1    3    4    1    3    3    2    3    2    1    2    1    1    3    1    1    1    2    2    3    3    2    2    2    3    1    2    3    1    1    1    2    3    1    2    3    3    2    3    2    1    1    3    2    3    2    3    2    2    2    4    3    2    1    3    4    3    1    4    1    1    3    3    3    2    3    1    2    3    1    2    2    1    3    1    3    2    2    3    2    1    1    1    4    1    2    2    3    3    1    1    3    1    2    3    1    3    1    3    3    2    2    2    1    2    2    1    2    1    3    1    1    2    2    3    1    1    2    1    1    3    3    1    2    2    3    1    1    3    1    1    3    3    3    2    4    2    1    1    2    1    3    4    2    3    1    3    1    1    4    1    3    1    2    1    3    2    1    1    3    2    2    3    2    4    2    3    2    3    2    3    1    2    2    3    1    2    1    3    1    1    3    3    2    1    1    3    1    1    3    3    2    2    1    2    2    4    4    2    2    4    4    4    1    2    1    2    1    1    2    1    3    1    2    4    2    3    3    2    3    1    1    1    1    2    3    2    4    2    2    1    4    1    2    2    1    3    1    3    1    1    2    1    3    1    3    4    3    1    2    3    1    1    2    4    1    3    1    4    2    3    4    4    3    3    4    2    3    3    2    2    3    1    3    4    2    1    2    3    1    2    2    4    2    1    2    2    3    3    3    2    3    3    3    2    1    1    2    1    4    2    1    1    2    2    2    4    2    2    2    2    4    2    2    3    2    3    1    2    3    2    1    2    1    3    2    1    2    3    3    2    3    1    1    2    1    3    2    2    1    2    3    2    3    3    2    3    1    3    3    3    1    2    2    2    2    3    1    3    1    3    3    2    2    2    2    1    3    2    3    1    1    1    2    2    2    1    3    3    3    3    1    1    1    2    3    4    4    1    2    1    2    3    3    3    1    3    1    3    3    1    3    3    3    2    4    1    3    1    1    4    2    3    4    1    2    2    1    2    3    3    1    3    2    1    1    1    2    1    1    3    2    4    1    2    1    1    1    1    3    2    1    1    3    3    3    1    3    1    2    3    2    3    2    2    2    1    2    4    1    2    2    3    4    2    3    2    3    1    2    2    3    2    3    3    2    3    3    2    3    2    1    3    2    4    2    3    2    3    3    3    3    1    1    2    2    3    3    4    2    1    2    1    2    2    2    2    2    1    3    1    1    2    2    3    3    2    3    1    1    2    3    1    4    2    2    2    2    2    1    2    1    2    3    3    2    2    1    3    2    2    3    1    2    2    3    1    3    2    3    3    1    1    1    1    3    4    1    4    3    2    1    4    2    2    1    3    3    2    1    3    1    2    3    1    2    2    2    1    2    1    1    1    1    2    2    3    1    4    4    3    1    3    1    2    2    2    1    3    1    2    2    1    2    1    4    2    1    1    2    2    3    1    1    3    4    3    2    1    3    1    2    1    1    1    1    2    3    3    3    3    1    1    1    2    1    1    3    1    2    3    2    2    3    2    1    2    3    4    1    1    2    3    2    4    3    2    2    4    2    2    3    1    1    2    3    2    2    1    3    1    1    3    1    1    2    2    1    2    1    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