The expected values are computed in terms of row and column totals. In fact, the formula is
where "R_i" corresponds to the total sum of elements in row "i," "C_j" corresponds to the total sum of elements in column "j," and "T" is the grand total.
The table below shows the calculations to obtain the table with expected values:
Based on the observed and expected values, the squared distances can be computed according to the following formula: "\\dfrac{(E-O)^2}{E}." The table with squared distances is shown below:
The following null and alternative hypotheses need to be tested:
"H_0:" The two variables are independent.
"H_1:" The two variables are dependent.
This corresponds to a Chi-Square test of independence.
Based on the information provided, the significance level is "\\alpha=0.05," the number of degrees of freedom is "df=(2-1)\\times(3-1)=2," so then the rejection region for this test is "R=\\{\\chi^2:\\chi^2>5.991\\}."
The Chi-Squared statistic is computed as follows:
Since it is observed that "\\chi^2=58.661>5.991=\\chi_c^2," it is then concluded that the null hypothesis is rejected.Therefore, there is enough evidence to claim that the two variables are dependent, at the 0.05 significance level.
1. Only A statement is correct.
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