A hypergeometric function is a special function that arises in many areas of mathematics, including complex analysis, number theory, and mathematical physics. It is defined as a solution to a certain type of differential equation known as the hypergeometric differential equation. The hypergeometric function is denoted by ${}_2F_1(a,b;c;z)$, where the parameters $a$, $b$, and $c$ are complex numbers and $z$ is a complex variable. It is a powerful tool for solving various mathematical problems and has many interesting properties and applications. **

To expand the sample in a hypergeometric distribution, you can increase the number of items in the population from which the sample is drawn. This will provide more opportunities for different combinations of items to be selected in the sample. Additionally, increasing the sample size will also help in expanding the sample in a hypergeometric distribution, as a larger sample will provide more data points to analyze and make more accurate inferences about the population. Finally, increasing the number of categories or characteristics being studied in the population can also help expand the sample in a hypergeometric distribution, as it allows for a more diverse range of items to be included in the sample. **

The numerator in the hypergeometric distribution is multiplied to account for the number of ways to choose the desired items from the population. This multiplication is necessary because the hypergeometric distribution calculates the probability of getting a specific number of desired items in a sample without replacement from a finite population. By multiplying the numerator, we are accounting for the different ways the desired items can be chosen from the population, which affects the overall probability of obtaining the desired items in the sample. **

To expand the sample in a hypergeometric distribution, you would increase the number of items drawn from the population without replacement. This means increasing the sample size, which would result in a larger number of items being selected from the population. As the sample size increases, the distribution of the hypergeometric random variable becomes more closely approximated by the binomial distribution. This expansion allows for a more accurate representation of the population and can lead to more reliable statistical inference. **

Lotto is typically calculated using the hypergeometric distribution. The hypergeometric distribution is used when the outcome of each trial is dependent on the outcomes of previous trials, which is the case in lotto where the numbers are drawn without replacement. This distribution is used to calculate the probability of getting a certain combination of numbers out of a specific set. **

The binomial distribution is used instead of the hypergeometric distribution when the sample size is relatively small compared to the population size, or when the population size is very large. In these cases, the hypergeometric distribution becomes computationally complex and approaches the binomial distribution. Therefore, it is more practical to use the binomial distribution in such scenarios. Additionally, the binomial distribution assumes sampling with replacement, which is often a reasonable approximation in real-world situations. **

To calculate the recursion formula for the hypergeometric distribution using a calculator, you can use the following formula: P(X = x) = (choose(m, x) * choose(N-m, n-x)) / choose(N, n), where choose(n, k) represents the binomial coefficient. You can input the values of N, m, n, and x into the formula and use the calculator to calculate the binomial coefficients and then multiply and divide accordingly to obtain the probability of the hypergeometric distribution for a specific value of x. This process can be repeated for different values of x to obtain the entire probability distribution. **

To calculate the result for the hypergeometric distribution with a calculator, you would need to use the formula: P(X = k) = (C(n, k) * C(N - n, n - k)) / C(N, n), where C(n, k) represents the combination of n items taken k at a time. You would input the values for N (total number of items), n (number of items in the sample), and k (number of successful outcomes in the sample) into the formula. Then, use the calculator to calculate the combinations and perform the necessary arithmetic to find the probability of getting exactly k successful outcomes in the sample. **