Q4. To determine the effectiveness of the advertising campaign for a new digital video recorder, management would like to know what proportion of the households is aware of the brand. The advertising agency thinks that this figure is close to .55. The management would like to have a margin of error of ±.025 at the 99% confidence level.

a) What sample size should be used?

b) A sample of the size calculated in a) has been taken. The management found the sample proportion to be .575. Construct a 99% CI for the true proportion.

c) If someone insists that the true proportion is .59. Based your answer to b), would you agree or disagree with this person? Why agree or why not agree?

In the following problem, check that it is appropriate to use the normal approximation to the binomial. Then use the normal distribution to estimate the requested probabilities.

It is estimated that 3.5% of the general population will live past their 90th birthday. In a graduating class of 741 high school seniors, find the following probabilities. (Round your answers to four decimal places.)

(a) 15 or more will live beyond their 90th birthday


(b) 30 or more will live beyond their 90th birthday


(c) between 25 and 35 will live beyond their 90th birthday


(d) more than 40 will live beyond their 90th birthday

Give and interpret the 95% confidence intervals for males and a second 95% confidence interval for females on the SLEEP variable. Which is wider and why?

Known values for Male and Female:

Males: Sample Size = 17; Sample Mean = 7.765; Standard Deviation = 1.855

Females: Sample Size = 18; Sample Mean = 7.667; Standard Deviation = 1.879

Using t-distribution considering sample sizes (Male/Female count) are less than 30

Suppose the Federal Aviation Administration (FAA) would like to compare the on-time performances of different airlines on domestic, nonstop flights. To determine if Airline and Status are dependent, what are the appropriate hypotheses?

A)H_O: Airline and Status are independent of each other.
H_A: Airline and Status display a positive correlation.

B)Two of the other options are both correct.

C)H_O: Airline and Status are independent of each other.
H_A: Airline and Status are dependent on one another.

D)H_O: Airline and Status are not related to each other.
H_A: Airline and Status display a negative correlation.

E)H_O: Airline and Status are related to one another.
H_A: Airline and Status are independent of one another.

2.A political poll asked potential voters if they felt the economy was going to get worse, stay the same, or get better during the next 12 months. The party affiliations of the respondents were also noted. To determine if Party Affiliation and Response are dependent, what are the appropriate hypotheses?

A)There is not enough information to choose the correct set of hypotheses.

B)H_O: Party Affiliation and Response are not related to one another.
H_A: Party Affiliation and Response display a negative correlation.

C)H_O: Party Affiliation and Response are independent of each other.
H_A: Party Affiliation and Response display a positive correlation.

D)H_O: Party Affiliation and Response are not related to each other.
H_A: Party Affiliation and Response are dependent on each other.

E)H_O: Party Affiliation and Response are associated with one another.
H_A: Party Affiliation and Response are not related to each other

3. Consider the first and second exam scores of the 10 students listed below. Calculate the Pearson's correlation coefficient for the dataset below and interpret what that means.

exam 1	exam 2
24	37
22	35
21	42
22	40
21	41
23	37
23	30
23	37
21	48
25	30

A)The correlation is -0.774 . There is a strong negative linear association between Exam 1 and Exam 2

B) The correlation is -0.774 . There is a weak negative linear association between Exam 1 and Exam 2 .

C)The correlation is 0.774 . There is a strong positive linear association between Exam 1 and Exam 2 .

D)The correlation is -0.774 . There is a strong positive linear association between Exam 1 and Exam 2 .

E)The correlation is 0.774 . There is a strong negative linear association between Exam 1 and Exam 2 .

4. Consider the first and second exam scores of the 10 students listed below. Calculate the Pearson's correlation coefficient for the dataset below and interpret what that means.

exam 1	exam 2
23	29
29	24
19	19
17	27
24	22
10	20
29	28
20	18
25	18
16	29

A)The correlation is 0.147 . There is a weak negative linear association between Exam 1 and Exam 2 .

B)The correlation is -0.147 . There is a weak positive linear association between Exam 1 and Exam 2

C)The correlation is 0.147 . There is a strong positive linear association between Exam 1 and Exam 2

D)The correlation is -0.147 . There is a weak negative linear association between Exam 1 and Exam 2

E)

The correlation is 0.147 . There is a weak positive linear association between Exam 1 and Exam 2 .

The World Bank collected data on the percentage of GDP that a country spends on health expenditures ("Health expenditure," 2013) and also the percentage of woman receiving prenatal care ("Pregnant woman receiving," 2013). The data for the countries where this information are available for the year 2011 is in table #10.1.8. Create a scatter plot of the data and find a regression equation between percentage spent on health expenditure and the percentage of woman receiving prenatal care. Then use the regression equation to find the percent of woman receiving prenatal care for a country that spends 5.0% of GDP on health expenditure and for a country that spends 12.0% of GDP. Which prenatal care percentage that you calculated do you think is closer to the true percentage? Why?

Table #10.1.8: Data of Heath Expenditure versus Prenatal Care

HEALTH EXPENDITURE (% of GDP)	Prenatal Care (%)
9.6	47.9
3.7	54.6
5.2	93.7
5.2	84.7
10.0	100.0
4.7	42.5
4.8	96.4
6.0	77.1
5.4	58.3
4.8	95.4
4.1	78.0
6.0	93.3
9.5	93.3
6.8	93.7
6.1	89.8

If a random variable has a uniform distribution over the range 10 ≤X≤ 20, what is the probability that the random variable takes a value in the range [13.75, 17.25]?

Hello, I have been trying to answer this question for the last hour and I am still struggling could someone help me? The deadline is in 1hour!

Perform an analysis of variance on the following data set. Do this by answering the questions below.

Group 1	Group 2	Group 3
82	87	97
91	90	99
93	91	104
94	99	105
94	101	106
95	115	109
99	118	110
101	114
103	117
105	121
106	121
106	129
113
127

Link to spreadsheet.

1. What is SST?

2. What is the test statistic from ANOVA?

3. What is the p-value from ANOVA?

4. Consider the null hypothesis that there are no differences between the means of the three populations from which the three columns were sampled. Should this hypothesis be rejected at the 5% level?

According to the Centers for Disease Control, the mean number of cigarettes smoked per day by individuals who are daily smokers is 18.1. A researcher claims that retired adults smoke less than the general population of daily smokers. To test this claim, she obtains a random sample of 25 retired adults who are current smokers, and records the number of cigarettes smoked on a randomly selected day. The data result in a sample mean of 16.8 cigarettes and a standard deviation of 4.8 cigarettes. Do the data support the claim that retired adults who are daily smokers smoke less than the general population of daily smokers? Conduct a hypothesis test at α = 0.10. Assume the population is normally distributed. Hint: σ is unknown, and this is a one-tailed test. (5 points) State the hypotheses 〖 H〗_0: H_1: b. Compute test statistic (Round to the nearest 100th) c. Find critical value (Round to the nearest 100th) d. State decision rule: e. State your conclusion. First, state either “Reject the null hypothesis” or “Fail to reject it.” Then, interpret your conclusion:

A government official is in charge of allocating social programs throughout the city of Vancouver. He will decide where these social outreach programs should be located based on the percentage of residents living below the poverty line in each region of the city. He takes a simple random sample of 120 people living in Gastown and finds that 21 have an annual income that is below the poverty line.

Part i) The proportion of the 120 people who are living below the poverty line, 21/120, is a:

A. variable of interest.
B. parameter.
C. statistic.

Part ii) Use the sample data to compute a 95% confidence interval for the true proportion of Gastown residents living below the poverty line.

(Please carry answers to at least six decimal places in intermediate steps. Give your final answer to the nearest three decimal places).

95% confidence interval = ( , )

(a) Find the margin of error for the given values of​ c, σ​, and n. c = 0.90​, σ = 3.8​, n = 100

E= _ (Round to three decimal places as​ needed.)

(b) Construct the confidence interval for the population mean μ.

c = 0.90 ​, x=9.1​, σ = 0.3 ​, and n = 47

A 90​% confidence interval for μ is _, _ (Round to two decimal places as​ needed.)

(c)  Construct the confidence interval for the population mean μ.

c=0.95 , x=16.2, σ =2.0, and n =35

A 95​% confidence interval for μ is _, _ (Round to two decimal places as​ needed.)

A

• A real estate agency wants to compare the appraised values of single-family homes in two Black Hawks County communities. A random sample of 60 listings in Cedar Falls and 95 listings in Waterloo yields the following results (in thousands of dollars):

	Appraised value	Standard deviation
Cedar Falls	154.12	27.5
Waterloo	138.74	21.8

• What is the 95% margin of error when estimating the mean appraised value of all single-family homes for Waterloo? _____

• A 95% confidence interval for the difference in mean appraised value of all single-family homes between Cedar Falls and Waterloo is ___________________

• A 99% confidence interval for the mean appraised value of all single-family homes for Cedar Falls is _______________

We assume that our wages will increase as we gain experience and become more valuable to our employers. Wages also increase because of inflation. By examining a sample of employees at a given point in time, we can look at part of the picture. How does length of service (LOS) relate to wages? The data here (data426.dat) (see below) is the LOS in months and wages for 60 women who work in Indiana banks. Wages are yearly total income divided by the number of weeks worked. We have multiplied wages by a constant for reasons of confidentiality.

(a) Plot wages versus LOS. Consider the relationship and whether or not linear regression might be appropriate. (Do this on paper. Your instructor may ask you to turn in this graph.)

(b) Find the least-squares line. Summarize the significance test for the slope. What do you conclude?

Wages = _________ + __________ LOS

t = _________

P = _________

(c) State carefully what the slope tells you about the relationship between wages and length of service. This answer has not been graded yet.

(d) Give a 95% confidence interval for the slope.

(______ , _______)

worker  wages   los     size
1       55.0977 28      Large
2       60.3942 54      Small
3       55.5375 35      Small
4       48.6244 27      Small
5       56.5636 188     Large
6       38.237  156     Small
7       43.5632 30      Large
8       42.7156 61      Large
9       39.143  65      Large
10      46.1205 23      Small
11      49.5348 68      Large
12      63.0939 76      Small
13      37.3613 57      Small
14      86.4907 44      Large
15      62.1521 103     Large
16      49.2244 51      Large
17      61.2332 63      Large
18      38.775  14      Small
19      47.1923 127     Large
20      38.5997 39      Large
21      38.8533 105     Large
22      46.0433 164     Small
23      64.581  70      Large
24      41.4075 17      Small
25      55.9129 143     Large
26      47.352  107     Small
27      43.1829 22      Small
28      51.886  197     Large
29      51.3497 46      Large
30      60.591  40      Large
31      55.6434 77      Small
32      37.9994 34      Large
33      50.3993 85      Large
34      39.2409 88      Small
35      51.1068 118     Large
36      44.8436 58      Large
37      39.4066 78      Large
38      64.675  47      Small
39      59.4471 142     Large
40      70.2038 93      Small
41      47.4302 168     Small
42      44.8665 33      Small
43      39.4258 27      Large
44      71.8007 69      Small
45      38.5246 46      Large
46      71.9274 68      Small
47      51.5816 22      Large
48      65.4135 18      Large
49      64.9034 76      Small
50      73.0817 97      Large
51      45.4468 35      Large
52      44.2239 56      Large
53      68.4574 87      Large
54      37.7713 60      Small
55      46.0706 86      Small
56      45.3591 62      Large
57      53.7606 21      Small
58      104.9657        74      Large
59      40.4731 71      Small
60      60.6301 97      Large

Observed Frequencies	Remedial English	Not in Remedial English	Total
Normal	22	187	209
ADD	19	74	93
Total of the two categories	41	261	302

My Question: How do you run the appropriate chi square test on this data in SPSS? I need to know how to set it up in SPSS and the step by step procedures. What goes in the data view/variable view and how do I run the test?

Which of the following statements about the general exponential equation y = 600 (1.05)t is true? (Assume t is time in years, with t = 0 in 1950.) Check all that apply.

A)After 1950, each year the y-value is 1.05 times greater than the previous year.

B)The initial amount of 600 is increasing at a rate of 1.05% each year after 1950.

C)When t = 1, y is 105% of its original value, 600.

D)The initial amount of 600 is increasing at a rate of 5% each year after 1950.

First National Bank employs three real estate appraisers whose job is to establish a property’s market value before the bank offers a mortgage to a prospective buyer. It is imperative that each appraiser values a property with no bias. Suppose First National Bank wishes to check the consistency of the recent values that its appraisers have established. The bank asked the three appraisers to value (in $1,000s) three different types of homes: a cape, a colonial, and a ranch. The results are shown in the accompanying table. (You may find it useful to reference the q table.)

	Appraiser
House Type	1	2	3
Cape	425	415	430
Colonial	530	550	540
Ranch	390	400	380 If average values differ by house type, use Tukey’s HSD method at the 5% significance level to determine which averages differ. (If the exact value for nT − c is not found in the table, use the average of corresponding upper & lower studentized range values. Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.)