PLEASE JUST ANSWER #3 and the correspdonding right under it   How many females are in your sample? How many males? What proportion of your sample is female? What proportion of your sample is male? - From the data it is clear that there are 50 persons in the sample. 33 are female and 17 are male. Thus, N(females ) =33 N(males ) =17 Proportion of females  = 33/50                                      =0.66 Proportion of males  = 17/50                                      =0.34   2)     Is the proportion of women at StatCrunchU equal to the proportion of men at StatCrunchU?   Show that the conditions are met for the use of a normal model for a hypothesis test (np > 10 and n(1 - p) >10, where p is from the null hypothesis). Test the claim that the proportion of females at StatCrunchU is equal to the proportion of men. Document your hypothesis test in hypothesis test format in your submission. Write a conclusion to your hypothesis test referring to females at StatCrunchU. Explain what the P-value means as a probability that refers to random samples of 50 StatCrunchU students. Assuming the students at the university can be classified as males or females only, if the proportion if females is indeed equal to that of males, then half or 0.5 proportion will be females, and the remaining half or 0.5 will be males. Thus, if the proportion if females is equal to that of males, we should expect to have, p = 0.5. The sample size considered here is, n = 50. Denote p̂ as the sample proportion of females. The sampling distribution of the sample proportion, p̂ can be assumed to have a normal model, if the individuals are selected normally, and if np > 10, n (1 – p) > 10, where p is the expected proportion. Here, the sampled individuals are mentioned to have been chosen randomly. Now, np = 50 × 0.5 = 25 > 10. n (1 – p) = 50 × (1 – 0.5) = 50 × 0.5 = 25 > 10. Thus, the conditions are met to use a normal model as the sampling distribution of the sample proportion, p̂, which will facilitate in the hypothesis test.   In order to test if the true proportion of females at the university is equal to the proportion of men, the null and alternative hypotheses are as follows: H0: p = 0.5; H1: p ≠ 0.5. The test is two-tailed, and the z-test for one proportion is suitable. The sample size considered is, n = 50. The sampled number of females obtained by counting the number of times “Female” appears in the column “Gender” is, x = 33. Thus, the sampled proportion of females is, p̂ = x/n = 33/50 = 0.66. The formula for the z-test statistic is, z = (p̂ – p)/ √ [p (1 – p)/n]. Thus, z = (p̂ – p)/ √ [p (1 – p)/n] = (0.66 – 0.5) / √ [(0.5) (1 – 0.5) / 50] = (0.16) / √ (0.005) ≈ 2.263. Thus, the value of the test statistic is 2.263. For the two-tailed test, the P-value would be: P-value = P (| Z | ≥ | z |) = P (| Z | ≥ | 2.263 |) = P (Z ≤ –2.263) + P (Z ≥ 2.263) = 2 P (Z ≤ –2.263) [Since the standard normal variable, Z is symmetric about 0] ≈ 2 × 0.0118 [Using the Excel formula: =NORM.S.DIST(-2.263,TRUE), P (Z ≤ –2.263) ≈ 0.0118] = 0.0536. Thus, the P-value of the test is 0.0236. The significance level, α is not specified, so it is assumed by default that, α = 0.05. The decision rule regarding a hypothesis testing problem using the P-value is: Reject H0 if P-value ≤ α. Otherwise fail to reject H0. Here, P-value (≈ 0.0236) < α (= 0.05), reject the null hypothesis H0.   Thus, it can be concluded that the proportion of females at the university is not equal to the proportion of males; in other words, the proportion of females at the university is significantly different from the proportion of males at the university.   The P-value is the probability of observing sample results at least as extreme as the observed results, given that the null hypothesis is true. In this case, the P-value of 0.0236 indicates that, if the proportion of females at the university is indeed equal to the proportion of males (as assumed in the null hypothesis), then the probability of observing at least as extreme as 33 females in a sample of 50 randomly selected students would be only 0.0236. In other words, if several samples of 50 students from the university are selected randomly, then only about 0.0236 proportion (or, 2.36%) of the sample will have at least as extreme as 33 female students.   3)     What are the proportions of females and males at StatCrunch U? Show that the conditions are met for the use of a normal model for a confidence interval (count of successes and failures are greater than 10, as in 2 a). Determine a range of plausible values for this proportion by using your choice of technology. Document your methods below, using screen short from the    calculator or other technology as needed. Interpret your interval referring to females at StatCrunchU. Explain what is meant by "95% confident."

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PLEASE JUST ANSWER #3 and the correspdonding right under it

 

  • How many females are in your sample? How many males? What proportion of your sample is female? What proportion of your sample is male?

- From the data it is clear that there are 50 persons in the sample. 33 are female and 17 are male. Thus,

N(females ) =33

N(males ) =17

Proportion of females  = 33/50

                                     =0.66

Proportion of males  = 17/50

                                     =0.34

 

2)     Is the proportion of women at StatCrunchU equal to the proportion of men at StatCrunchU?

 

  1. Show that the conditions are met for the use of a normal model for a hypothesis test (np > 10 and n(1 - p) >10, where p is from the null hypothesis).
  2. Test the claim that the proportion of females at StatCrunchU is equal to the proportion of men. Document your hypothesis test in hypothesis test format in your submission.
  3. Write a conclusion to your hypothesis test referring to females at StatCrunchU.
  4. Explain what the P-value means as a probability that refers to random samples of 50 StatCrunchU students.

Assuming the students at the university can be classified as males or females only, if the proportion if females is indeed equal to that of males, then half or 0.5 proportion will be females, and the remaining half or 0.5 will be males. Thus, if the proportion if females is equal to that of males, we should expect to have, p = 0.5.

The sample size considered here is, n = 50.

Denote  as the sample proportion of females.

The sampling distribution of the sample proportion,  can be assumed to have a normal model, if the individuals are selected normally, and if np > 10, n (1 – p) > 10, where p is the expected proportion.

Here, the sampled individuals are mentioned to have been chosen randomly. Now,

np = 50 × 0.5 = 25 > 10.

n (1 – p) = 50 × (1 – 0.5) = 50 × 0.5 = 25 > 10.

Thus, the conditions are met to use a normal model as the sampling distribution of the sample proportion, , which will facilitate in the hypothesis test.

 

In order to test if the true proportion of females at the university is equal to the proportion of men, the null and alternative hypotheses are as follows:

H0p = 0.5; H1p ≠ 0.5.

The test is two-tailed, and the z-test for one proportion is suitable.

The sample size considered is, n = 50.

The sampled number of females obtained by counting the number of times “Female” appears in the column “Gender” is, x = 33.

Thus, the sampled proportion of females is,  = x/n = 33/50 = 0.66.

The formula for the z-test statistic is, z = ( – p)/ √ [p (1 – p)/n].

Thus,

z

= ( – p)/ √ [p (1 – p)/n]

= (0.66 – 0.5) / √ [(0.5) (1 – 0.5) / 50]

= (0.16) / √ (0.005)

≈ 2.263.

Thus, the value of the test statistic is 2.263.

For the two-tailed test, the P-value would be:

P-value

P (| Z | ≥ | z |)

P (| Z | ≥ | 2.263 |)

P (Z ≤ –2.263) + P (Z ≥ 2.263)

= 2 P (Z ≤ –2.263)

[Since the standard normal variable, Z is symmetric about 0]

≈ 2 × 0.0118

[Using the Excel formula: =NORM.S.DIST(-2.263,TRUE), P (Z ≤ –2.263) ≈ 0.0118]

= 0.0536.

Thus, the P-value of the test is 0.0236.

The significance level, α is not specified, so it is assumed by default that, α = 0.05.

The decision rule regarding a hypothesis testing problem using the P-value is:

Reject H0 if P-value ≤ α. Otherwise fail to reject H0.

Here, P-value (≈ 0.0236) < α (= 0.05), reject the null hypothesis H0.

 

Thus, it can be concluded that the proportion of females at the university is not equal to the proportion of males; in other words, the proportion of females at the university is significantly different from the proportion of males at the university.

 

The P-value is the probability of observing sample results at least as extreme as the observed results, given that the null hypothesis is true.

In this case, the P-value of 0.0236 indicates that, if the proportion of females at the university is indeed equal to the proportion of males (as assumed in the null hypothesis), then the probability of observing at least as extreme as 33 females in a sample of 50 randomly selected students would be only 0.0236.

In other words, if several samples of 50 students from the university are selected randomly, then only about 0.0236 proportion (or, 2.36%) of the sample will have at least as extreme as 33 female students.

 

3)     What are the proportions of females and males at StatCrunch U?

    1. Show that the conditions are met for the use of a normal model for a confidence interval (count of successes and failures are greater than 10, as in 2 a).
    2. Determine a range of plausible values for this proportion by using your choice of technology. Document your methods below, using screen short from the    calculator or other technology as needed.
    3. Interpret your interval referring to females at StatCrunchU.
    4. Explain what is meant by "95% confident."

 

Gender
Class
Hours
Work
Loans
CC Debt
Male
1
15
1539
Female
1
15
1522
Male
1
18
7
2882
1516
Female
17
17
7514
2491
Female
1
15
1242
Male
4
33
17282
11155
Male
2
15
2477
Female
3
27.5
11757
3781
Female
12
11468
2594
Female
6
41
11949
3038
Female
3
15
13528
3657
Female
4
11
24
Female
14
Female
4
16
14.5
15590
4740
Female
4
15
3369
Female
2
15
7458
2184
O O
Transcribed Image Text:Gender Class Hours Work Loans CC Debt Male 1 15 1539 Female 1 15 1522 Male 1 18 7 2882 1516 Female 17 17 7514 2491 Female 1 15 1242 Male 4 33 17282 11155 Male 2 15 2477 Female 3 27.5 11757 3781 Female 12 11468 2594 Female 6 41 11949 3038 Female 3 15 13528 3657 Female 4 11 24 Female 14 Female 4 16 14.5 15590 4740 Female 4 15 3369 Female 2 15 7458 2184 O O
Sample of 50 Students from StatCrunchu
Gender
Class
Hours
Work
Loans
CC Debt
Female
2
11
22
7122
2202
Female
4
26.5
15367
4139
Female
16
14.5
8164
3205
Female
4
15
15
7611
Male
4
12
19
16014
8959
Male
1
16
1658
Female
3
24.5
11997
4418
Female
16
17.5
2543
Male
20
6.5
14118
6596
Female
1
17
1303
Male
4
14
15506
8273
Male
3
14
2530
Female
3
13
11371
2784
Male
1
15
20
4912
1720
Female
4
7
32
14494
8152
Female
2
13
21
9661
2008
Male
21
5173
Female
2
16
2787
Female
3474
Female
2
14
8433
2450
Male
18
7403
3003
Female
3
15
3101
Male
1
13
16.5
5475
1673
Female
4
12
18393
5113
Male
4
19
7604
Female
2
12
1663
Female
17
2874
1273
Female
4
12
4658
Male
2
14
20
7669
3144
Male
2
19
2531
Female
1
16
15.5
4723
1192
Female
4
20
12.5
15756
3300
Male
4
15
17188
3743
Female
2
18
2205
o o
Transcribed Image Text:Sample of 50 Students from StatCrunchu Gender Class Hours Work Loans CC Debt Female 2 11 22 7122 2202 Female 4 26.5 15367 4139 Female 16 14.5 8164 3205 Female 4 15 15 7611 Male 4 12 19 16014 8959 Male 1 16 1658 Female 3 24.5 11997 4418 Female 16 17.5 2543 Male 20 6.5 14118 6596 Female 1 17 1303 Male 4 14 15506 8273 Male 3 14 2530 Female 3 13 11371 2784 Male 1 15 20 4912 1720 Female 4 7 32 14494 8152 Female 2 13 21 9661 2008 Male 21 5173 Female 2 16 2787 Female 3474 Female 2 14 8433 2450 Male 18 7403 3003 Female 3 15 3101 Male 1 13 16.5 5475 1673 Female 4 12 18393 5113 Male 4 19 7604 Female 2 12 1663 Female 17 2874 1273 Female 4 12 4658 Male 2 14 20 7669 3144 Male 2 19 2531 Female 1 16 15.5 4723 1192 Female 4 20 12.5 15756 3300 Male 4 15 17188 3743 Female 2 18 2205 o o
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