1.Suppose X is binomial with p = .90. What does n have to be (at a minimum) to use the normal approximation for X?

1.Explain why the same n works for both of the previous problems.

The ones that are bolded are the ones i need, 13 and 15. I sent this picture as a reference!

LG Paste BI Uvab X x ALVA~ Styles Styles Dictate Sensitivity Pane a. Exactly 3 students in the sample have black cars. b. More than 3 students in the sample have black cars. c. Less than half of the students in the sample have black cars. d. At least one student in the sample has a black car. 10. There are two conditions that you have to check before using the normal approximation when X is binomial. a. Write down the two conditions. b. Why do we have to check BOTH conditions rather than just one, to be able to use the normal approximation? (Including an example with your explanation is OK.) 11. Suppose you flip a fair coin 7 times, and let X be the total number of tails. What is the probability that X is greater than 1? (Hint, what is p when you flip a fair coin?) A: 12. Suppose it is reported that 10% of OSU business students major in international marketing. You sample 200 OSU business students at random. Let X = the number of students in the sample who major in international marketing. a. How many students in your sample do you EXPECT to major in int'l marketing, according to the above information? b . What is the probability that your sample finds that at least 15 students major in international marketing? c. Are your findings from part (b) typical of what you EXPECTED to get in part (a)? Why or why not? 13. Suppose X is binomial with p = .90. What does n have to be (at a minimum) to use the normal approximation for X? A: 14. Suppose X is binomial with p = .10. What does n have to be (at a minimum) to use the normal approximation for X? 15. Explain why the same n works for both of the previous problems. A: 16. Suppose 20% of a bank's customers report being unsatisfied. You take a sample of 20 customers at random and count the number of unsatisfied customers