This is the 3rd time posting the same Qs. l need answers to the table and not an explanation. I am providing answer screenshot from previous posted qs. Thanks

Assume that we are given a dataset that includes two attributes, neighbourhoods of residency and person's professional division. There is a society of 1000 residents with two neighbourhoods, P, and Q. A random sample of 310 residents of the society is taken whose occupations are doctors, engineers and teachers. The null hypothesis is that each person's neighbourhood of residency is independent of the person's professional division. The data are categorized as: X2 (chi-square) test: Let's tabulate the given information and calculate and fill the required values. To determine if you should accept or reject the null hypothesis, you need to calculate the p-value. The p-value is the probability of observing a test statistic as extreme or more extreme than the one calculated from the sample, assuming the null hypothesis is true. If the p-value is less than the significance level (e.g. 0.05), 1 you reject the null hypothesis, otherwise, you accept the null hypothesis. Step 2/2 Second step is here below Explanation In this case, without the calculation of the chi-square statistic, it is not possible to determine the pvalue and make a decision on accepting or rejecting the null hypothesis. Explanation The chi-square test is used to determine if there is a relationship between two categorical variables. In this case, the variables are the neighborhood of residency and the person's professional division. The null hypothesis is that there is no relationship between the two variables, meaning that a person's neighborhood of residency is independent of their professional division. The test is calculated by comparing the observed data with the expected data under the assumption of the null hypothesis. The difference between the observed and expected values is squared, divided by the expected value and the sum is calculated for each category. The final value is compared to the critical value from the chi- square distribution table to determine the p - value. The p-value helps to determine the strength of the evidence against the null hypothesis. If the p-value is less than a certain significance level, such as 0.05, the null hypothesis is rejected, otherwise it is accepted