BE279 Applied Statistics and Forecasting

1.      Introduction

In every organization, to expand the business and implement the job needs the support of the worker. For this the organization should move forward. Moreover the organization needs to [pay the salary on their time . For this the worker should didi their work on time . In this case study reader should find the salary protocall of the organization.

2.      Methodology and data

The study discusses this and other surveys involving information from the  Association for information study system Survey of salary ,rank, distribution information and comparable information related to recruitmentnews from MIS Faculty. This review had three main findings. For starters academic and personal variables have an impact on the position and salary offered but individual factors has more lasting effects. Second This research should look for placements presented by schools that to some extent interfere with the association between school ,  individual factors and initial salary.The last is the immediate effects of individual items are also influenced by certain field factors . In which, the distribution at the highest level is the main variable of the individual to determine the remuneration of the doctoral degree institutions.

Fascinatingly, scholars of various level are offered others see on the employee compensation of MIS,which  often. Employees who approach their school during recruitment are first asked to list institutional or external factors, for example, if the classes  is organized for research or focused on education, whether one or social , whether he has a “doctoral program” or not, whether he is a for affiliation reasons, and may this be a good year for MIS. By all accounts, agreement seems to be that single qualities, particularly a competitor’s track record in distributing A-grade articles, may be important, but those qualities are crucial. compensate them. When asked, employees generally agree that work-related items will also affect pay, but it is generally felt that single items (e.g. “experience, Level distribution”) will drive placement distribution. Similarly, argues that although collectivism is the main model when assessing a potential competitor in the workforce at the associate teacher level,

 

3.      Analysis and results (including robustness analysis)

 

Statistics
salary female
N Valid 786 753
“Missing” 0 33
“Mean” .08
“Median” .00
“Mode” 0
“Std. Deviation” .277
“Variance” .077

Table 1 Statistics

(Source : SPSS)

female
“Frequency” “Percent” “Valid Percent” “Cumulative Percent”
“Valid” 0 690 87.8 91.6 91.6
1 63 8.0 8.4 100.0
Total 753 95.8 100.0
“Missing” “System” 33 4.2
Total 786 100.0

BE279 Applied Statistics and Forecasting

Figure 1 Salary stat

(Source : SPSS)

BE279 Applied Statistics and Forecasting

Figure 2 Employ stat

(Source : SPSS)

 

 

Statistics
V1 salary female lsalary
N Valid 786 786 753 786
Missing 0 0 33 0
Mean 393.50 .08
Median 393.50 .00
Mode 1a 0
Std. Deviation 227.043 .277
Variance 51548.500 .077
“a. Multiple modes exist. The smallest value is shown”

 

 

Anova Test –

Table  4 Anova

(Source : SPSS)

 

“T-test”

“Group Statistics”
female N “Mean” “Std. Deviation” “Std. Error Mean”
V1 >= 3 0a . . .
< 3 753 380.01 221.802 8.083
id >= 3 0a . . .
< 3 753 527.78 253.154 9.225
pubindx >= 3 0a . . .
< 3 753 36.901806128511865 39.034539508677790 1.422497697972276
totpge >= 3 0a . . .
< 3 744 161.608488453331800 146.667395247874540 5.377087595199336
a. t “cannot be computed because at least one of the groups is empty.”

 

Table  5 T-test

(Source : SPSS)

 

Figure 3 Employe stat

(Source : SPSS)

“Regression”

 

“Descriptive Statistics”
“Mean” “Std. Deviation” N
id 527.78 253.154 753
female .08 .277 753

Table  6 Descriptive stat

(Source : SPSS)

 

“Correlations”
id female
“Pearson Correlation” id 1.000 -.182
female -.182 1.000
“Sig. (1-tailed)” id . .000
female .000 .
N id 753 753
female 753 753

Table  6 Correlation 

(Source : SPSS)

 

 

“Variables Entered/Removed
“Model” “Variables Entered” “Variables Removed” “Method”
1 female . “Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100)”.
a. “Dependent Variable”: id

Table 7: Variables

(Source : SPSS)

“Model Summary”
“Model” R “R Square” “Adjusted R Square” “Std. Error of the Estimate”
1 .182a .033 .032 249.109
a. Predictors: (Constant), female

Table 8: Variables

(Source : SPSS)

 

“ANOVA”a
Model “Sum of Squares” df “Mean Square” F Sig.
1 “Regression” 1589925.629 1 1589925.629 25.621 .000b
Residual 46603647.518 751 62055.456
Total 48193573.147 752
a. “Dependent Variable”: id
b. “Predictors: (Constant), female”

Table 9: Annova

(Source : SPSS)

“Coefficients”
Model “Unstandardized Coefficients” “Standardized Coefficients” t Sig.
B “Std. Error” “Beta”
1 (“Constant”) 541.670 9.483 57.117 .000
female -165.955 32.786 -.182 -5.062 .000
a. Dependent Variable: id

Table  10 : Coefficient

(Source : SPSS)

 

“T-Test”

 

“Paired Samples Statistics”
“Mean” N “Std. Deviation” “Std. Error Mean”
Pair 1 V1 380.01 753 221.802 8.083
female .08 753 .277 .010
Pair 2 id 542.85 786 258.603 9.224
y99 .33 786 .472 .017

Table 11 :”Paired sample test”

(Source : SPSS)

“Paired Samples Correlations”
N “Correlation” Sig.
Pair 1 V1 & female 753 -.182 .000
Pair 2 id & y99 786 .000 1.000

Table 12 :”Paired sample test”

(Source : SPSS)

“Paired Samples Test”
“Paired Differences” t df “Sig. (2-tailed)”
“Mean” “Std. Deviation” “Std. Error Mean” “95% Confidence Interval of the Difference”
“Lower” “Upper”
Pair 1 V1 – female 379.928 221.853 8.085 364.057 395.800 46.993 752 .000
Pair 2 id – y99 542.514 258.603 9.224 524.407 560.621 58.815 785 .000

Table 13 :”Paired sample test”

(Source : SPSS)

 

PPlot

 

“Model Description”
“Model Name” MOD_1
“Series or Sequence” 1 V1
2 id
3 pubindx
4 totpge
“Transformation” None
“Non-Seasonal Differencing” 0
“Seasonal Differencing” 0
“Length of Seasonal Period” No periodicity
“Standardization” Not applied
“Distribution” Type Normal
Location estimated
Scale estimated
“Fractional Rank Estimation Method” Blom’s
“Rank Assigned to Ties” “Mean rank of tied values”
“Applying the model specifications from MOD_1”

Table 14 :”P-plot”

(Source : SPSS)

 

“Case Processing Summary”
V1 id pubindx totpge
Series or Sequence Length 786 786 786 786
“Number of Missing Values in the Plot” “User-Missing 0 0 0 0
System-Missing” 0 0 0 39
“The cases are unweighted.”

Table 15:”P-plot”

(Source : SPSS)

 

“Estimated Distribution Parameters”
V1 id pubindx totpge
Normal Distribution Location 393.500000000000000 542.847328244274800 35.352493657467480 160.959458379222070
Scale 227.042947479105800 258.602776029033900 38.916367705071906 146.729089572200650
The cases are unweighted.

Table 16 :”P-plot”

(Source : SPSS)

V1

“Figure 4 P-plot”

“(Source : SPSS)”

“Figure 5 P-plot”

“(Source : SPSS)”

 

id

Figure 6  P-plot

(Source : SPSS)

Figure 7 P-plot

(Source : SPSS)

 

pubindx

“Figure 8 P-plot”

“(Source : SPSS)”

“Figure 9 P-plot”

“(Source : SPSS)”

 

totpge

 

“Figure 10  P-plot”

“(Source : SPSS)”

 

4.      Discussion and conclusions

Organizations could recruit a new workforce as learning partners (regardless of residency) to legitimize pay commensurate with their capabilities or, potentially, an organization could offer better compensation. rank (and place of residence) to balance a rather lower salary or a higher educational burden. Therefore, the ability to adapt to the idea of ​​a position can be an advantage for both the candidate and the organization, but just like the ability to reshape the position. Finally, institutionally, this is certainly one method by which platforms can attract attractive people. For example, find a central school looking to attract qualified and motivated junior staff to strengthen its exam.  for example, the widely held view in the field that “private organizations” pay more. In any case, if so, people suggest that “MIS professionals” could profit  from a reasonable investigation of the variables that affect their own compensation, and they could be quite shocked by the results. of such an examination.

In the end of the research some findings also suggest that employees who are looking for work and those trying to hire them may need to do their homework before engaging in interactions by visiting the AIS site and reading the Teacher’s report. monk Galleta. The doctoral project headquarters and facilitators may also want to encourage their PhD students to focus on reporting as well as completing research when the opportunity arises.

 

 

 

Topic 2

1.      Introduction

Contingency plans are knowledgeable in reiterating the testing conditions faced by the region . These constraints are monetary , political , administrative ,  legal , innovative and social . As a result of stress the company faces increasing competition ,  stunted growth  and abundant capital , and extended connections. The operation of gadgets in home and work environments, the improvement of autonomous vehicles and the growing threats of digital attacks are changing the way people live and take advantage of their opportunities. Contingency plans must support their action plans in the face of developments that may threaten the company’s growth. Progress is considered by many to be the single greatest success in a deeply global and fierce economy. The growth perspective paints a big picture of future opportunities which give some growth to the organization. The reason for the case study is to know the relation  between the possibility of promotion, the type of promotion and different parts of the business including the marketing , development and currency exposure. Focus of an observational assessment of teh protectionist industry in the UK .  Observational verification of these models and the evidence to confirm the relation between the development possibilities.

2.      Methodology and data

The exploration is oriented in the field of protection given teh fact that protection operations are growing and the information base is inactive.Invented devices are essential to ensure agents protection in difficult working environment.Protected cases can be classified as life. In additional articles on protection . Th commercial and non life insurance deals specifically current and long term theoretical bets. Many insurers are facing , the difficulties due to the large increase in the number of customers who are buying personalized and the lack of service . The gradual improvement of innovation which change in ;large scale. The economy and the industry are facing productive and innovative competitors inside and outside of teh company.Then given the importance of advance protection it is the focus of research in the UK. The research gives knowledge on this topic . For this the structures shou;ld make a clear view on the reader’s mind.

3.      Analysis and results (including robustness analysis)

Means

Table 17 : “Mean”

(Source : SPSS)

 

 

Innovation goods Innovation logistics Cooperation size of company (staff)  * Innovation services
Innovation services Innovation goods Innovation logistics Cooperation size of company (staff)
0 Mean .36 .08 .24 145.54
Std. Deviation .481 .269 .426 483.458
Median .00 .00 .00 44.00
Std. Error of Mean .007 .004 .006 7.309
1 Mean .82 .33 .54 329.92
Std. Deviation .384 .472 .498 979.729
Median 1.00 .00 1.00 92.00
Std. Error of Mean .016 .020 .021 41.890
Total Mean .42 .11 .27 166.03
Std. Deviation .493 .309 .445 563.569
Median .00 .00 .00 46.00
Std. Error of Mean .007 .004 .006 8.033

Table 18:”Mean”

(Source : SPSS)

 

Table 19:”Mean”

(Source : SPSS)

Innovation goods Innovation logistics Cooperation size of company (staff)  * Innovation support
Innovation support Innovation goods Innovation logistics Cooperation size of company (staff)
0 Mean .35 .04 .21 136.61
Std. Deviation .477 .198 .405 489.855
Median .00 .00 .00 39.00
Std. Error of Mean .008 .003 .006 7.823
1 Mean .67 .37 .53 281.30
Std. Deviation .472 .482 .500 778.220
Median 1.00 .00 1.00 93.00
Std. Error of Mean .015 .015 .016 24.597
Total Mean .42 .11 .27 166.03
Std. Deviation .493 .309 .445 563.569
Median .00 .00 .00 46.00
Std. Error of Mean .007 .004 .006 8.033

Table 20:”Mean”

(Source : SPSS)

 

“Descriptives”

 

“Descriptive Statistics”
N “Minimum” “Maximum” “Mean” “Std. Deviation”
Innovation goods 4922 0 1 .42 .493
Innovation services 4922 0 1 .11 .314
Innovation support 4922 0 1 .20 .403
Valid N (listwise) 4922

Table 21:“Descriptive Statistics”

(Source : SPSS)

T-Test

 

“One-Sample Statistics”
N “Mean” “Std. Deviation” “Std. Error Mean”
size of company (staff) 4922 166.03 563.569 8.033

Table 22:“One-Sample Statistics”

(Source : SPSS)

“One-Sample Test”
“Test Value = 0”
t df “Sig. (2-tailed)” “Mean Difference” “95% Confidence Interval of the Difference”
“Lower” “Upper”
size of company (staff) 20.669 4921 .000 166.033 150.29 181.78

Table 23:“One-Sample Statistics”

(Source : SPSS)

“Oneway”

 

 

Table 24:“One-way Statistics”

(Source : SPSS)

ANOVA
“Sum of Squares” df “Mean Square” F Sig.
Innovation goods “Between Groups” 174.502 1 174.502 841.166 .000
Within Groups 1020.669 4920 .207
Total 1195.171 4921
Innovation services Between Groups 29.109 1 29.109 313.317 .000
Within Groups 457.101 4920 .093
Total 486.210 4921
Innovation support Between Groups 105.744 1 105.744 752.170 .000
Within Groups 691.680 4920 .141
Total 797.424 4921
Cooperation Between Groups 132.035 1 132.035 771.288 .000
Within Groups 842.242 4920 .171
Total 974.277 4921

Table 25:“One-way Statistics”

(Source : SPSS)

 

Means Plots

Figure 11  Mean-plot

(Source : SPSS)

Figure 12  Mean-plot

(Source : SPSS)

Figure 13  Mean-plot

(Source : SPSS)

Figure 14  Mean-plot

(Source : SPSS)

“Regression”

 

“Descriptive Statistics”
Mean Std. Deviation N
size of company (staff) 166.03 563.569 4922
Innovation goods .42 .493 4922
Innovation services .11 .314 4922
Innovation manufacturing .35 .478 4922
Innovation logistics .11 .309 4922
Innovation support .20 .403 4922

Table 26: “Regression”

(Source : SPSS)

Table 27: “Regression”

(Source : SPSS)

“Variables Entered/Removeda”
Model “Variables Entered” “Variables Removed” “Method”
1 Innovation logistics . “Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100).”
2 Innovation services . “Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100)”.
3 Innovation support . “Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100).”
4 Innovation manufacturing . “Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100)”.
a. “Dependent Variable”: size of company (staff)

Table 27: “Regression”

(Source : SPSS)

Model Summarye
Model R “R Square” “Adjusted R Square” “Std. Error of the Estimate”
1 .114a .013 .013 559.962
2 .137b .019 .018 558.386
3 .145c .021 .020 557.808
4 .149d .022 .021 557.501
a.” Predictors: (Constant), Innovation logistics”
b. Predictors: (Constant), Innovation logistics, Innovation services
c. Predictors: (Constant), Innovation logistics, Innovation services, Innovation support
d. Predictors: (Constant), Innovation logistics, Innovation services, Innovation support, Innovation manufacturing
e. Dependent Variable: size of company (staff)

Table 28: “Regression”

(Source : SPSS)

“ANOVAa”
Model “Sum of Squares” df “Mean Square” F Sig.
1 “Regression” 20254653.523 1 20254653.523 64.596 .000b
“Residual” 1542704433.012 4920 313557.812
Total 1562959086.536 4921
2 Regression 29242024.723 2 14621012.362 46.893 .000c
Residual 1533717061.812 4919 311794.483
Total 1562959086.536 4921
3 Regression 32722619.079 3 10907539.693 35.056 .000d
Residual 1530236467.457 4918 311150.156
Total 1562959086.536 4921
4 Regression 34717824.501 4 8679456.125 27.925 .000e
Residual 1528241262.035 4917 310807.660
Total 1562959086.536 4921
a. “Dependent Variable”: size of company (staff)
b. “Predictors: (Constant), Innovation logistics”
c. “Predictors: (Constant), Innovation logistics”, Innovation services
d. Predictors: (Constant), Innovation logistics, Innovation services, Innovation support
e. Predictors: (Constant), Innovation logistics, Innovation services, Innovation support, Innovation manufacturing

Table 29: “Anova”

(Source : SPSS)

Coefficientsa
Model “Unstandardized Coefficients” “Standardized Coefficients” t Sig.
B Std. Error Beta
1 (Constant) 143.820 8.447 17.027 .000
Innovation logistics 207.467 25.813 .114 8.037 .000
2 (Constant) 132.158 8.698 15.193 .000
Innovation logistics 170.234 26.659 .093 6.386 .000
Innovation services 140.806 26.226 .079 5.369 .000
3 (Constant) 122.849 9.124 13.464 .000
Innovation logistics 133.708 28.783 .073 4.645 .000
Innovation services 123.746 26.691 .069 4.636 .000
Innovation support 74.324 22.222 .053 3.345 .001
4 (Constant) 111.594 10.144 11.001 .000
Innovation logistics 119.219 29.330 .065 4.065 .000
Innovation services 114.888 26.905 .064 4.270 .000
Innovation support 60.761 22.846 .043 2.660 .008
Innovation manufacturing 46.681 18.424 .040 2.534 .011
a. “Dependent Variable”: size of company (staff)

Table 30: “Coefficient”

(Source : SPSS)

“Excluded Variables”a
Model Beta In t Sig. “Partial Correlation” “Collinearity Statistics”
Tolerance
1 Innovation goods .059b 4.056 .000 .058 .959
Innovation services .079b 5.369 .000 .076 .932
Innovation manufacturing .062b 4.116 .000 .059 .887
Innovation support .067b 4.301 .000 .061 .820
2 Innovation goods .042c 2.799 .005 .040 .898
Innovation manufacturing .049c 3.245 .001 .046 .860
Innovation support .053c 3.345 .001 .048 .790
3 Innovation goods .035d 2.316 .021 .033 .877
Innovation manufacturing .040d 2.534 .011 .036 .813
4 Innovation goods .026e 1.659 .097 .024 .805
a. Dependent Variable: size of company (staff)
b. Predictors in the Model: (Constant), Innovation logistics
c. Predictors in the Model: (Constant), Innovation logistics, Innovation services
d. Predictors in the Model: (Constant), Innovation logistics, Innovation services, Innovation support
e. Predictors in the Model: (Constant), Innovation logistics, Innovation services, Innovation support, Innovation manufacturing

Table 31: “Excluded variables”

(Source : SPSS)

“Residuals Statistics”a
“Minimum” “Maximum” “Mean” “Std. Deviation” N
“Predicted Value” 111.59 453.14 166.03 83.994 4922
Residual -450.142 11445.406 .000 557.275 4922
Std. Predicted Value -.648 3.418 .000 1.000 4922
Std. Residual -.807 20.530 .000 1.000 4922
a. Dependent Variable: size of company (staff)

Table 32: “Residuals statitics”

(Source : SPSS)

Figure 15   Histogram

(Source : SPSS)

4.      Discussion and conclusions

In this review , the research describes the ability of a developed process and the assumption also leads quickly and the response should be accurate. Use hierarchical products including representations and external information and external information by promoting the culture of the organization which should make a step forward in the progress of the organization.Taking into  account of the customers needs for the future and assumptions are exceptional fundamental to the business which make all the business to make some growth in teh future. It will generate some new ideas in the business for some progress. The ability of the entire agency to innovate is largely based on the imagination of teh people ,the consequences of the examination show the innovation of employees and firmly align with the culture of the development support of teh organization.

Developing CreativeProtection’s corporate culture is an important way to further develop an insurance agent’s ability to advance. Social development platform insurance agency requires first agreement on leadership, on this premise,to adopt a sound strategy for planning and consolidation. In the new business environment, the usual construction of the alignment of the pyramids of defense efforts due to the inhibition of advancement and change. The guard change model of link is interactive; In adventure, the plan emphasizes the loyalty and solidarity of the cycles of the association. the consequence is it impacts the culture of the association that supports to advance, makes a difference to work on coordination between utility agencies in the department, it assumes an important part is having the right environment that promotes advancement by finding open doors for innovation from the outside to turn them into effective promotion .Along with the research the promotion ability is one of teh most desirable element that directors need to cultivate.Along with these lines, the promotion ability is one of the most desirable elements that directors need to cultivate. This accommodation controlling impulse empowers and certifies people for development. This will help link with super driving previous factors, administration and action plans.

 

    References list

 

 

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