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# After considering the descriptive statistics the career accelerator team is interested in identifying the factors which affect the marks in the excel certification test

## 16 Nov After considering the descriptive statistics the career accelerator team is interested in identifying the factors which affect the marks in the excel certification test

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## 1. FixIt! ‘Statistical toolbox’

This section of the report is approximately 700 words (guide only, not a word limit).

· After considering the descriptive statistics the career accelerator team is interested in identifying the factors which affect the marks in the excel certification test (i.e. variable ECT_Score). They are more interested in identifying behavioural traits which could affect these scores. These include:

· Number of attempts ( ECT_Num_Attempt, Practice_Num_Attempt); and

· Score of the attempts ( Practice_Score_Max t, Practice_Score_Mean).

Further details of these variables can be found in “Note about the data” in your Assessment 2a Guide.

· To understand whether these factors play an important role, broadly speaking, you will need to (i) formulate a multiple linear regression model (refer to week explaining your choice of variables) and (ii) explain whether the relationship is statistically and/or economically significant. The use of a multiple linear regression (week 4-8), confidence intervals (week 7) and hypothesis testing would help you address this and thus provide evidence for your arguments.

· More specifically, you need to consider relevant factors to understand the relationship between ECT_Score and the behavioural variables incorporating the any control variables which are relevant.

· In addition to interpreting the results of your analysis you will also need to draw to attention issues of causality (Week 7) and confoundment (Week 4) which can impact the conclusions from the analysis.

· As a part of this assessment, assumptions and limitations need to be explicitly identified. e.g., What variable would you want to have in an ideal situation to measure ability in this analysis? Do you have this variable in the dataset? If not (which is often the case in practice: we often don’t have all the ideal data/variables that we need to perform an analysis, and must rely on the data available to us), what variable in the dataset do you have to use as an ability measure? What are the assumptions and limitations of using this variable?]

· Once, you have considered the above issues and analysis, you will need to understand its implications and consider the appropriate recommendations for the career accelerator team as they try to improve scores in the excel certification test (as above) as this is the purpose of this report.

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 ECT_Num_Attempt Practice_Num_Attempt Practice_Score_Max Practice_Score_Mean ECT_Score 1 4 640 490 863 1 6 1000 819 633 SUMMARY OUTPUT 1 3 923 859 438 1 2 914 886 807 Regression Statistics 1 6 914 666 914 Multiple R 0.2258 1 12 942 861 908 R Square 0.051 1 11 942 831 893 Adjusted R Square 0.0248 1 3 920 893 890 Standard Error 199.8337 1 3 800 790 420 Observations 150 1 5 257 108 433 1 4 960 930 890 ANOVA 1 1 857 857 166 df SS MS F Significance F 2 12 920 753 790 Regression 4 311217.6759 77804.419 1.9483 0.1056 1 5 840 552 376 Residual 145 5790360.964 39933.5239 2 9 971 457 914 Total 149 6101578.64 1 9 1000 936 871 1 4 971 657 893 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 1 9 1000 957 850 Intercept 527.6046 87.3713 6.0387 0 354.9189 700.2904 354.9189 700.2904 1 3 971 914 962 ECT_Num_Attempt -0.5768 52.3573 -0.011 0.9912 -104.0589 102.9053 -104.0589 102.9053 1 2 880 760 836 Practice_Num_Attempt 3.132 5.1642 0.6065 0.5451 -7.0749 13.3388 -7.0749 13.3388 1 7 971 355 871 Practice_Score_Max 0.2377 0.1535 1.5481 0.1238 -0.0658 0.5412 -0.0658 0.5412 1 8 1000 515 809 Practice_Score_Mean -0.0394 0.1366 -0.2885 0.7733 -0.3094 0.2306 -0.3094 0.2306 1 2 1000 914 829 1 5 923 869 646 1 2 885 771 764 1 4 960 450 376 1 18 884 579 538 1 9 961 743 481 1 3 1000 897 394 1 4 1000 928 700 1 1 846 846 485 1 3 971 923 742 1 3 1000 961 377 1 5 857 737 829 1 1 685 685 366 1 2 360 300 592 1 12 960 583 972 1 4 960 920 820 1 8 971 910 807 1 3 742 466 957 1 5 942 691 831 1 7 942 857 936 1 8 1000 493 742 1 3 1000 884 862 1 4 942 871 850 1 6 1000 809 914 1 1 571 571 167 1 2 800 780 550 4 4 1000 940 820 1 13 960 745 592 1 5 1000 994 943 1 5 914 497 807 1 1 85 85 807 1 7 971 787 935 1 5 960 912 754 1 16 961 709 746 1 2 942 942 636 1 6 828 776 700 1 3 1000 961 243 1 4 1000 798 769 1 1 920 920 650 1 2 771 728 566 1 12 1000 871 525 1