Pattern of Data from the Percentages – Hire Academic Expert
Insert your data here – this is the table shown in the instructions document labelled with your student number.
2107630  Quarter of Year  
Position  Q1  Q2  Q3  Q4 
Goalkeeper  104  91  73  66 
Defender  308  250  241  180 
Midfielder  238  180  169  124 
Forward  141  127  119  102 
Identify the dependent variable
Identify the Independent Variables
Positions of player
Birth quarter 

Assess if your data and variables meet the assumptions of Chisquare (Approx. 125words)
We see the χ2 (sub with symbol) is applicable as the data is nominal, the data is categorised from the two independent variables into groups, no data set can be in more than one group set or lie outside a group set as all participants play football with a certain position and they can only have one birth day so there put into quarters of the years simply. The two variables both have more than the minimum of two needed independent categorial groups. These data are accepted in the contingency table so a fair RAE test can be carried out looking at if the quarter someone is born in effects the position in football.  
Insert the Contingency Table here.
Contingency Tables  
Quarter of year  
Position  Q1  Q2  Q3  Q4  Total  
Defender  Count  308  250  241  180  979  
Expected count  308.153  252.444  234.524  183.879  979  
% within row  31.461 %  25.536 %  24.617 %  18.386 %  100.000 %  
% within column  38.938 %  38.580 %  40.033 %  38.136 %  38.957 %  
% of total  12.256 %  9.948 %  9.590 %  7.163 %  38.957 %  
Forward  Count  141  127  119  102  489  
Expected count  153.919  126.093  117.142  91.846  489  
% within row  28.834 %  25.971 %  24.335 %  20.859 %  100.000 %  
% within column  17.826 %  19.599 %  19.767 %  21.610 %  19.459 %  
% of total  5.611 %  5.054 %  4.735 %  4.059 %  19.459 %  
Goalkeeper  Count  104  91  73  66  334  
Expected count  105.131  86.125  80.011  62.733  334  
% within row  31.138 %  27.246 %  21.856 %  19.760 %  100.000 %  
% within column  13.148 %  14.043 %  12.126 %  13.983 %  13.291 %  
% of total  4.138 %  3.621 %  2.905 %  2.626 %  13.291 %  
Midfielder  Count  238  180  169  124  711  
Expected count  223.797  183.338  170.323  133.542  711  
% within row  33.474 %  25.316 %  23.769 %  17.440 %  100.000 %  
% within column  30.088 %  27.778 %  28.073 %  26.271 %  28.293 %  
% of total  9.471 %  7.163 %  6.725 %  4.934 %  28.293 %  
Total  Count  791  648  602  472  2513  
Expected count  791  648  602  472  2513  
% within row  31.476 %  25.786 %  23.955 %  18.782 %  100.000 %  
% within column  100.000 %  100.000 %  100.000 %  100.000 %  100.000 %  
% of total  31.476 %  25.786 %  23.955 %  18.782 %  100.000 %  
Describe the Pattern of Data from the Percentages (App125 words)
We see from the percentage of total count most data coming from defenders with all these percentages being greater then 38% in total and compared to goalkeepers with all their quarters it was lower than 14.1% to all other positions. Midfielders there was between 30.09%26.27% and for forwards 21.6117.83%, so a downward frequency pattern was highlighted from Defenders to midfielders, forwards and goalkeepers.
The percentages show no significant differences with a person birth quarter and their position in football they showed the same pattern whereas the quarters went from left to right there was a decrease of cells in each position shown as for defenders for Q1 to Q4 the variance (highestlowest column percentage) was only 1.897% for midfielders 3.817% forwards being 3.784 and goalkeepers 1.917%. 

Quarter of year  
Position  Q1  Q2  Q3  Q4  Total  
Defender  Count  308  250  241  180  979  
Expected count  308.153  252.444  234.524  183.879  979  
SR  0.00872  0.15382  0.422876  0.28606  
Forward  Count  141  127  119  102  489  
Expected count  153.919  126.093  117.142  91.846  489  
SR  1.04132  0.080772  0.171668  1.059515  
Goalkeeper  Count  104  91  73  66  334  
Expected count  105.131  86.125  80.011  62.733  334  
SR  0.11031  0.525303  0.7838  0.412478  
Midfielder  Count  238  180  169  124  711  
Expected count  223.797  183.338  170.323  133.542  711  
SR  0.949408  0.24652  0.10137  0.82572  
Total  Count  791  648  602  472  2513  
Expected count  791  648  602  472  2513 
Describe and interpret the pattern of the data from the Observed Values, Expected Values and Standardised Residuals within this table (App125 words)
From all results there was no significant standardised residuals, as there were no standard residuals having a value greater than 1.96 or lower then 1.96 so little association detected.
The results swing from positive to negative in the expected count with no correlation to certain positions or quarters of the years, the standardised residuals vary from 1.0413 for defenders in Q1 to 1.0595 for forwards in Q4 showing there’s no correlations with the variables, all quarters have positive and negative SR’s and all positions having negative and positive values. 

Insert ChiSquare Test table here
ChiSquared Tests  
Value  df  p  
χ2  5.254  9  0.812  
N  2513 
Describe and interpret the ChiSquare statistics within the table (App100 words)
From the degrees of freedom 9 pieces of information can vary without breaking the constraints, we see the probability of belonging to Q1 is statistically significant but that the players position and when they were born didn’t have a significant baring shown in the p value being 0.812 which is higher than 0.05, the chi squared reading (χ2 )is 5.25, so we can reject the null hypothesis if the observed value is equal or larger then 5.25 a sample size of 2513 male professional footballers were collected.


Insert Nominal Measures Here.
Nominal  
Valueᵃ  
Phicoefficient  NaN  
Cramer’s V (φc) 
0026  
ᵃ Phi coefficient is only available for 2 by 2 contingency Tables 
Describe and interpret the Effect Size statistics within the table (App75 words)
With the value of φc being 0.026 which is a lot closer to 0 then 1 a nonsignificant association between the variables is highlighted.  
Report the full result of the ChiSquare test below
For this data there was a nonsignificant association between the players positions and there birth quartile χ2 =5.254, φc=0.026, p=0.812, p>0.05 this is shown in figure 1 where the correlation on averages shows little difference, the correlation to quarters and number of players had negative gradient shown in figure 1 from Q1 down to Q4 looking at the study from Delorme (2010) it found the same pattern with a greater number of players born in the first quarter of year (Q1), with these they discussed that the coaches selections were reached attending primarily to the players “anthropometric, physical and physiological variables”, which linked with RAE, as “Q1 players will have these qualities more developed” further evidence in a study from Kearney (2017) on French rugby players showed from 2135 players there tests with the same quarters of the year found here was a overrepresentation of players born in Q1 (SR=2.66) and an underrepresentation of players in Q4(2.88) it also found a link between RAE within forward and back row players, and the RAE is depended on how physically demanding the sport is and this can cause bias in the French rugby system and also for are study on professional football players but that the positions aren’t too depended on RAE as footballers have similar skills in each position. We see the frequencies in are percentages go from the highest for defenders to midfielders then forwards then goalkeepers this was expected with most teams playing in a 442 or 433 and some instances 451 or 532, there’s always 1 goalkeeper and in most cases more midfielders then forwards and more change of more defenders than midfielders, so the pattern isn’t out of the ordinary. 
References
Delorme, N., Boiché, J. and Raspaud M. (2010) Relative age effect in elite sports: Methodological bias or real discrimination? European Journal of Sport Science 10(2), 9196. Page 95
Kearney, P. (2017). Playing position influences the relative age effect in senior rugby union. Science & Sports, 32(2), 114–116. https://doi.org/10.1016/j.scispo.2016.06.009 Page 115
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