Reference no: EM132216269

TRUE OR FALSE

1. An example of a quantitative variable is the number of telephone lines in a city.

2. An example of a ratio scale variable is the mileage of a car.

3. Credit score is an example of a ratio scale variable.

4. When establishing the classes for a frequency table it is generally agreed that the more classes you use the better your frequency table will be.

5. The cumulative distribution function is never decreasing.

6. A Histogram is a graphic that is used to depict qualitative data.

7. The income distribution is skewed to the right; therefore, the Mean Income must be greater than the Median Income.

8. The sample variance formula makes it an unbiased estimator.

9. The median is said to be more resistant to extreme values.

10. The probability of an event is a value which must be greater than 0 and less than 1.

11. Two events are independent if the probability of one event is influenced by whether or not the other event occurs.

12. Mutually exclusive events also independent.

13. A subjective probability is a probability assessment that is based on relative frequency.

14. The probability of an event is the sum of the probabilities of the sample space outcomes that correspond to the event.

15. If events A and B are independent, then P(A|B) is always equal to P(A) divided by P(B) .

16. Events that have no sample space outcomes in common and, therefore cannot occur simultaneously are referred to as mutually independent events.

17. The binomial experiment consists of n independent, identical trials, each of which results in either success or failure and the probability of success on any trial must be the same.

18. The standard deviation of the binomial distribution is np(1-p).

19. In a binomial distribution the random variable X is discrete.

20. The mean and variance are not the same for a standard normal distribution.

21. In a statistical study, the random variable X = 1, if the house is colonial and X = 0 if the house is not colonial, then it can be stated that the random variable is continuous.

22. For a continuous distribution, P(X ≤ 100) is the same as P(X<100).

23. The actual weight of hamburger patties is an example of a continuous random variable.

24. The number of defective pencils in a lot of 1000 is an example of a continuous random variable.

25. All continuous random variables are normally distributed.