First we find the sum of each column and each row. Then we find the grand sum which is equal to 500.
"\\text{Table 1:}\\\\\nu_i=\\frac{1}{500}\\sum_{j=1}^c O_{ij}\\text{ where } c\\text{ --- number of columns},\\\\\nO_{ij} \\text{ --- observed values of the contingency table},\\\\\ni=1, \\ldots, r, r\\text{ --- number of rows}.\\\\\n\\text{In our case } r=c=3.\\\\\nv_j=\\frac{1}{500}\\sum_{i=1}^r O_{ij}\\\\\n\\text{Table 2:}\\\\\nE_{ij}=500u_iv_j\\text{ --- values of the table (expected values)}\\\\\n\\text{Table 3:}\\\\\na_{ij}=\\frac{(E_{ij}-O_{ij})^2}{E_{ij}}\\\\\nchi^2_o=\\sum_{i=1}^r\\sum_{j=1}^c a_{ij}=28.75\\text{ --- observed value of } \\chi^2\\\\\nchi^2_{cr}\\approx 9.49\\text{ --- critical value of } \\chi^2\\\\\nchi^2_{cr}=chi^2_{cr}(alpha;df)\\\\\nalpha=0.05 \\text{ --- significance level}\\\\\ndf=(c-1)(r-1)=4\\text{ --- degrees of freedom}\\\\\nchi^2_o>chi^2_{cr}.\\text{ So we can say that age and grade points are}\\\\\n\\text{not independent}.\\\\\n\\text{Using CHISQ.TEST() we find p-value. We can see}\\\\ \n\\text{that p-value<alpha. So we can say that age and grade points are}\\\\\n\\text{not independent}."
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50 students selected at random from 500 students enrolled in a computer crash programme were classified according to the age and grade points giving the following data: Age (in years) Grade Point Below 20 21-30 Above 30 Up to 5 - 3 5 2 5.1 to 7.5 - 8 7 5 7.6 to 10.0 - 4 8 8 Test at 5% level of significance that age and grade points are independent
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