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31 . The domain of X = {1, 2, 3, 4, 5, 6, 7, 8., 9, 10, 11, 12…27}. Because you are drawing without replacement, and 26 of the 52 cards are red, you have to draw a red card within the first 17 draws.

32 . X ~ G (0.24)

33 . μ =   1 p =   1 0.27 = 3.70

34 . σ =   1 p p 2 =   1 0.27 0.27 2 = 3.16

4.5: hypergeometric distribution

35 . Yes, because you are sampling from a population composed of two groups (boys and girls), have a group of interest (boys), and are sampling without replacement (hence, the probabilities change with each pick, and you are not performing Bernoulli trials).

36 . The group of interest is the cards that are spades, the size of the group of interest is 13, and the sample size is five.

4.6: poisson distribution

37 . A Poisson distribution models the number of events occurring in a fixed interval of time or space, when the events are independent and the average rate of the events is known.

38 . X ~ P (4)

39 . The domain of X = {0, 1, 2, 3, …..) i.e., any integer from 0 upwards.

40 . μ = 4
σ = 4 = 2

5.1: continuous probability functions

41 . The discrete variables are the number of books purchased, and the number of books sold after the end of the semester. The continuous variables are the amount of money spent for the books, and the amount of money received when they were sold.

42 . Because for a continuous random variable, P ( x = c ) = 0, where c is any single value. Instead, we calculate P ( c < x < d ), i.e., the probability that the value of x is between the values c and d .

43 . Because P ( x = c ) = 0 for any continuous random variable.

44 . P ( x >5) = 1 – 0.35 = 0.65, because the total probability of a continuous probability function is always 1.

45 . This is a uniform probability distribution. You would draw it as a rectangle with the vertical sides at 0 and 20, and the horizontal sides at 1 10 and 0.

46 . P ( 0   < x < 4 ) = ( 4 0 ) ( 1 10 ) =   0.4

5.2: the uniform distribution

47 . P ( 2   < x < 5 ) = ( 5 2 ) ( 1 10 ) =   0.3

48 . X ~ U (0, 15)

49 . f ( x ) = 1 b a for ( a x b )  so  f ( x ) = 1 30 for ( 0 x 30 )

50 . μ =   a + b 2 =   0 + 30 5 = 15.0
σ =   ( b a ) 2 12 =   ( 30 0 ) 2 12 = 8.66

51 . P ( x < 10 ) = ( 10 ) ( 1 30 ) =   0.33

5.3: the exponential distribution

52 . X has an exponential distribution with decay parameter m and mean and standard deviation 1 m . In this distribution, there will be a relatively large numbers of small values, with values becoming less common as they become larger.

53 . μ = σ = 1 m = 1 10 = 0.1

54 . f ( x ) = 0.2 e –0.2 x where x ≥ 0.

6.1: the standard normal distribution

55 . The random variable X has a normal distribution with a mean of 100 and a standard deviation of 15.

56 . X ~ N (0,1)

57 . z = x μ σ so z = 112 109 4.5 = 0.67

58 . z = x μ σ so z = 100 109 4.5 = 2.00

59 .   z = 105 109 4.5 = −0.89
This girl is shorter than average for her age, by 0.89 standard deviations.

60 . 109 + (1.5)(4.5) = 115.75 cm

61 . We expect about 68 percent of the heights of girls of age five years and zero months to be between 104.5 cm and 113.5 cm.

62 . We expect 99.7 percent of the heights in this distribution to be between 95.5 cm and 122.5 cm, because that range represents the values three standard deviations above and below the mean.

6.2: using the normal distribution

63 . Yes, because both np and nq are greater than five.
np = (500)(0.20) = 100 and nq = 500(0.80) = 400

Questions & Answers

Write a short note on skewness
Saran Reply
and on kurtosis too
What is events
Manish Reply
who is a strong man?
Desmond Reply
Can you sir plz provide all the multiple choice questions related to Index numbers.?
Hiren Reply
about probabilty i have some questions and i want the solution
asad Reply
What is hypothesis?
Rosendo Reply
its a scientific guess
A hypothesis in a scientific context, is a testable statement about the relationship between two or more variables or a proposed explanation for some observed phenomenon. In a scientific experiment or study, the hypothesis is a brief summation of the researcher's prediction of the study's findings.
Which may be supported or not by the outcome. Hypothesis testing is the core of the scientific method.
statistics means interpretation analysis and representation of numerical data
To check the statment or assumption about population parameter is xalled hypothesis
hypothesis is simply an assumption
what is the d.f we know that how to find but basically my question is what is the d.f? any concept please
asad Reply
Degrees of freedom aren’t easy to explain. They come up in many different contexts in statistics—some advanced and complicated. In mathematics, they're technically defined as the dimension of the domain of a random vector.
But we won't get into that. Because degrees of freedom are generally not something you needto understand to perform a statistical analysis—unless you’re a research statistician, or someone studying statistical theory.
And yet, enquiring minds want to know. So for the adventurous and the curious, here are some examples that provide a basic gist of their meaning in statistics.
The Freedom to Vary First, forget about statistics. Imagine you’re a fun-loving person who loves to wear hats. You couldn't care less what a degree of freedom is. You believe that variety is the spice of life Unfortunately, you have constraints. You have only 7 hats. Yet you want to wear a different
hat every day of the week. On the first day, you can wear any of the 7 hats. On the second day, you can choose from the 6 remaining hats, on day 3 you can choose from 5 hats, and so on.
When day 6 rolls around, you still have a choice between 2 hats that you haven’t worn yet that week. But after you choose your hat for day 6, you have no choice for the hat that you wear on Day 7. You must wear the one remaining hat. You had 7-1 = 6 days of “hat” freedom—in which the hat you wore
That’s kind of the idea behind degrees of freedom in statistics. Degrees of freedom are often broadly defined as the number of "observations" (pieces of information) in the data that are free to vary when estimating statistical parameters.
please help me understand binomial distribution
Nnenna Reply
binomial distribution and poisson both are used to estimate the number of successes probable against the. probable failures. the difference is only that BINOMIAL dist. is for discrete data while POISSON is used for continuous data.
What do you need to understand?
poisson distribution is also for discrete data set. The difference is when the probability of occurring an event is very little and the sample size is extra large then we use poisson distribution.
Neil yes you got it and very interested answer you gave
How to know if the statement is 1 tail or 2 tail?
Coleen Reply
1 tail if greater than pr less than.2 tail if not equal.
in such a case there is no sufficient information provided to develop an alternative hypothesis and we can decide between only two states i.e either the statement is EQUAL TO or NOT EQUAL TO under given conditions
for 1tail there must be certain criteria like the greater than or less than or some probability value that must be achieved to accept or reject the original hypothesis.
for example if we have null hypothesis Ho:u=25 Ha:u#25(not equal to 25) it would be two tail if we say Ho:u=25 Ha:u>or Ha:u<25 it would be consider as one tail I hope you will be understand #Coleen
yes its true. now you have another problem. so share.
what is z score
Esperanza Reply
How to find z score through calculator
Different data sets will have different means and standard deviations, so values from one set cannot always be compared directly with those from another. The z-score standardizes normally distributed data sets, allowing for a proper comparison and a consistent definition of percentiles across data s
what are random number
Saif Reply
how to compute the mean with a long method
Fria Reply
there is a shortcut method for calculating mean long methid doesn't make any sense.
what are probability
probability mass function
probability density function
there are many definitions of probability. which one is, the ratio of favourable outcomes & total outcomes.
distribution used for modeling/(find probabilities) of discrete r.v. is called p.m.f
distribution used for modeling/(find probabilities) of continued r.v, called p.d.f
lets use short method using calculator.... store yo data n smply get your mean
if 1 calorie =4.12 kj, what is the total kj value of this dish
Jacqueline Reply
summation of values of x1 x2 x3 ,,,,xn divided by total number n if it is with frequency its like this summation of values of x1f1+x2f2+x3f3+xnfk divided by summation of frequencies like f1+f2+f3+fk
farah Reply
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QuizOver Reply

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