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Question: Find the regression​ equation, letting the first variable be the predictor​ (x) variable. Using t...

Find the regression​ equation, letting the first variable be the predictor​ (x) variable. Using the listed​ actress/actor ages in various​ years, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 27 years. Is the result within 5 years of the actual Best Actor​ winner, whose age was 45 ​years?

Best Actress: 27, 31, 29, 59, 34, 32, 44, 28, 65, 21, 45, 54

Best Actor: 45, 39, 40, 43, 52, 50, 59, 52, 41, 56, 44, 33

a. Find the equation of the regression line.

b. The best predicted age of the best actor winner given that the age of the best actress winner that is 27 years is ___years old.

Free will is an important concept to individuals and societies. In the context of cognitive neuroscience, mounting evidence suggests that free will is an illusion, constructed by processes in the brain, similar to visual illusions. Recent research has demonstrated that people who believe in free will tend to also believe in the paranormal (Mogi, 2014). A psychologist at Arizona State University (ASU) wanted to replicate this study with students in the psychology major. Following the procedure of Mogi (2014), an anonymous survey was sent to students with a call to participate in a “study of free will.” There was no mention of paranormal beliefs at this stage. One thousand and thirty-eight (1038) students completed the following survey. First, subjects were asked a series of questions regarding their belief in free will. After the free will questions, subjects were asked a series of questions regarding their belief in the paranormal. Care was taken so that the questions related to the paranormal was not phrased in a way that suggested a correlation between free will and the paranormal. Below are the findings:

1. (Test of Independence) The researcher now wants to look at just the psychology students at ASU. Does this data suggest a significant relationship between belief in free will and belief in the paranormal amongst the budding psychologists at ASU?
   
   1. Which variables are being tested in this Test of Independence?
      
   2. What are the hypothesis (H₁), the null hypothesis (H₀), and alpha level?
      

3. What is the df value for the chi-square statistic?
4. What is the population size?
5. Using SPSS and the dataset, ParaFW.csv, conduct a Test of Independence. What is the chi-square statistic and p-value?
   

1. 6. BY HAND! Calculate the chi-square statistic for this test of independence with α = .05.
      
2. 7. Conduct a measure the Effect Size. Is this effect size small, medium, or large?
      

State your decision: Based on your statistical analysis, does this data suggest a significant relationship between belief in free will and belief in the paranormal amongst the budding psychologists at ASU?

17. The lengths of a population of certain HULU shows I watch are normally distributed with a mean running time of 38 minutes and a standard deviation of 11.5.

1. Find the percentage of shows with running times between 26 and 42 minutes.

2.Between what values would you expect to find the middle 80 %

3. Find the percentage of shows with running times below 47.5 minutes

4.Above what value would you expect to find the top 25 %?

5.Find the percentage of shows with running times above 18 minutes.

Absenteeism is a major problem for some companies and in some industries. Suppose a study was conducted on absenteeism in the warehousing industry. Observations on several variables that might be related to absenteeism were collected on 35 major warehouses in the Pacific Northwest.

Absent: The average number of absences per employee for the year (does not include vacation days or confirmed sick days)

Wage: The average annual wage paid to the warehouse employees (does not include manager salaries)

Pct U: Union membership. The percentage of employees who belong to a union at the warehouse.

Good R: 1 if the employee group self-reported a “good” relationship with management; 0 if the employee group self-reported otherwise.

Perform a complete multiple regression analysis to find a model that might be useful for predicting the average number of absences per employee for the year. Perform ALL steps as outlined in class. Use Minitab and show all your work. Use alpha = .10 for any required tests (and show all steps for any required test). STAPLE MULTIPLE PAGES and include all required computer output.

A researcher hypothesizes that caffeine will affect the speed with which people read. To test this, the researcher randomly assigns 8 people into one of two groups: 50mg Caffeine (n1=4) or no Caffeine (n2=4). An hour after the treatment, the 8 participants in the study are asked to read from a book for 1 minute; the researcher counts the number of words each participant finished reading. The following are the data for each group:

50mg Caffeine (group 1)

450 400 500 450

No Caffeine (group 2)

400 410 430 440

Use α-level of .05 to answer the questions below:

i. Draw the sampling distribution of the difference between independent sample means, and locate M1-M2, μ(m1-M2) on the x-axis. What is the value of μ(m1-M2 under the assumption of the null hypothesis; indicate this on the x-axis as well.

j. Given the total df for this problem, what is the critical value of t? Indicate the critical value of t (and its value) in your drawing on (i). Also, indicate what the area is in the tail beyond the critical value of t.

k. Can you reject the null hypothesis?

l. Can you accept the research hypothesis?

1)

A report says that 82% of British Columbians over the age of 25 are high school graduates. A survey of randomly selected British Columbians included 1290 who were over the age of 25, and 1135 of them were high school graduates. Does the city’s survey result provide sufficient evidence to contradict the reported value, 82%?

Part i) What is the parameter of interest?

A. The proportion of all British Columbians (aged above 25) who are high school graduates.
B. Whether a British Columbian is a high school graduate.
C. All British Columbians aged above 25.
D. The proportion of 1290 British Columbians (aged above 25) who are high school graduates.

Part ii) Let pp be the population proportion of British Columbians aged above 25 who are high school graduates. What are the null and alternative hypotheses?

A. Null: p=0.88p=0.88. Alternative: p≠0.88p≠0.88.
B. Null: p=0.82p=0.82. Alternative: p=0.88p=0.88.
C. Null: p=0.82p=0.82. Alternative: p>0.82p>0.82.
D. Null: p=0.88p=0.88. Alternative: p>0.88p>0.88.
E. Null: p=0.82p=0.82. Alternative: p≠0.82p≠0.82 .
F. Null: p=0.88p=0.88. Alternative: p≠0.82p≠0.82.

Part iii) The PP-value is less than 0.0001. Using all the information available to you, which of the following is/are correct? (check all that apply)

A. The reported value 82% must be false.
B. Assuming the reported value 82% is correct, it is nearly impossible that in a random sample of 1290 British Columbians aged above 25, 1135 or more are high school graduates.
C. The observed proportion of British Columbians who are high school graduates is unusually high if the reported value 82% is correct.
D. The observed proportion of British Columbians who are high school graduates is unusually low if the reported value 82% is correct.
E. The observed proportion of British Columbians who are high school graduates is unusually high if the reported value 82% is incorrect.
F. The observed proportion of British Columbians who are high school graduates is unusually low if the reported value 82% is incorrect.
G. Assuming the reported value 82% is incorrect, it is nearly impossible that in a random sample of 1290 British Columbians aged above 25, 1135 or more are high school graduates.

Part iv) What is an appropriate conclusion for the hypothesis test at the 5% significance level?

A. There is sufficient evidence to contradict the reported value 82%.
B. There is insufficient evidence to contradict the reported value 82%.
C. There is a 5% probability that the reported value 82% is true.
D. Both A. and C.
E. Both B. and C.

Part v) Which of the following scenarios describe the Type II error of the test?

A. The data suggest that reported value is correct when in fact the value is incorrect.
B. The data suggest that reported value is correct when in fact the value is correct.
C. The data suggest that reported value is incorrect when in fact the value is correct.
D. The data suggest that reported value is incorrect when in fact the value is incorrect.

Part vi) Based on the result of the hypothesis test, which of the following types of errors are we in a position of committing?

A. Type I error only.
B. Type II error only.
C. Both Type I and Type II errors.
D. Neither Type I nor Type II errors.

2)

(1 point) McBeans magazine recently published a news article about caffeine consumption in universities that claims that 80% of people at universities drink coffee regularly. Moonbucks, a popular coffee chain, is interested in opening a new store on UBC campus. After reading McBeans' article, they will consider opening a store in UBC if more than 80% of the people in UBC drink coffee regularly. A random sample of people from UBC was taken, and it was found that 680 out of 810 survey participants considered themselves as regular coffee drinkers. Does Moonbucks' survey result provide sufficient evidence to support opening a store at UBC?

Part i) What is the parameter of interest?

A. Whether a person at UBC drinks coffee regularly.
B. The proportion of all people at UBC that drink coffee regularly.
C. The proportion of people at UBC that drink coffee regularly out of the 810 surveyed.
D. All people at UBC that drinks coffee regularly.

Part ii) Let pp be the population proportion of people at UBC that drink coffee regularly. What are the null and alternative hypotheses?

A. Null: p=0.84p=0.84. Alternative: p≠0.84p≠0.84.
B. Null: p=0.84p=0.84. Alternative: p>0.80p>0.80.
C. Null: p=0.80p=0.80. Alternative: p>0.80p>0.80 .
D. Null: p=0.84p=0.84. Alternative: p>0.84p>0.84.
E. Null: p=0.80p=0.80. Alternative: p=0.84p=0.84.
F. Null: p=0.80p=0.80. Alternative: p≠0.80p≠0.80.

Part iii) The PP-value is found to be about 0.0025. Using all the information available to you, which of the following is/are correct? (check all that apply)

A. The observed proportion of people at UBC that drink coffee regularly is unusually low if the reported value 80% is correct.
B. Assuming the reported value 80% is incorrect, there is a 0.0025 probability that in a random sample of 810, at least 680 of the people at UBC regularly drink coffee
C. Assuming the reported value 80% is correct, there is a 0.0025 probability that in a random sample of 810, at least 680 of the people at UBC regularly drink coffee.
D. The observed proportion of people at UBC that drink coffee regularly is unusually low if the reported value 80% is incorrect.
E. The observed proportion of people at UBC that drink coffee regularly is unusually high if the reported value 80% is correct.
F. The observed proportion of people at UBC that drink coffee regularly is unusually high if the reported value 80% is incorrect.
G. The reported value 80% must be false.

Part iv) What is an appropriate conclusion for the hypothesis test at the 5% significance level?

A. There is sufficient evidence to support opening a store at UBC.
B. There is insufficient evidence to support opening a store at UBC.
C. There is a 5% probability that the reported value 80% is true.
D. Both A. and C.
E. Both B. and C.

Part v) Which of the following scenarios describe the Type II error of the test?

A. The data do not provide sufficient evidence to support opening a store at UBC when in fact the true proportion of UBC people who drink coffee regularly exceeds the reported value 80%.
B. The data provide sufficient evidence to support opening a store at UBC when in fact the true proportion of UBC people who drink coffee regularly is equal to the reported value 80%.
C. The data provide sufficient evidence to support opening a store at UBC when in fact the true proportion of UBC people who drink coffee regularly exceeds the reported value 80%.
D. The data do not provide sufficient evidence to support opening a store at UBC when in fact the true proportion of UBC people who drink coffee regularly is equal to the reported value 80%.

Part vi) Based on the result of the hypothesis test, which of the following types of errors are we in a position of committing?

A. Type II error only.
B. Both Type I and Type II errors.
C. Type I error only.
D. Neither Type I nor Type II errors.

3)Suppose some researchers wanted to test the hypothesis that living in the country is better for your lungs than living in a city. To eliminate the possible variation due to genetic differences, suppose they located five pairs of identical twins with one member of each twin living in the country, the other in a city. For each person, suppose they measured the percentage of inhaled tracer particles remaining in the lungs after one hour: the higher the percentage, the less healthy the lungs. Suppose they found that for four of the five twin pairs the one living in the country had healthier lungs.Is the alternative hypothesis one-sided or two-sided?one-sided
one-sided or two-sided
two-sided
none of these answersHere are the probabilities for the number of heads in five tosses of a fair coin:

Compute the p-value and state your conclusion.p-value = 0.15625 + 0.03125 = 0.1875 and we have little evidence that individuals living in the country have healthier lungs than those individuals living in cities.
p-value = 0.03125 and we have little evidence that individuals living in the country have healthier lungs than those individuals living in cities.
p-value = 0.15625 and we have little evidence that individuals living in the country have healthier lungs than those individuals living in cities.
p-value = 0.15625 - 0.03125 = 0.125 and we have little evidence that individuals living in the country have healthier lungs than those individuals living in cities.

An elevator has a placard stating that the maximum capacity is 2520 lblong dash15 passengers.​ So, 15 adult male passengers can have a mean weight of up to 2520 divided by 15 equals 168 pounds. If the elevator is loaded with 15 adult male​ passengers, find the probability that it is overloaded because they have a mean weight greater than 168 lb.​ (Assume that weights of males are normally distributed with a mean of 175 lb and a standard deviation of 31 lb​.) Does this elevator appear to be​ safe?

The probability the elevator is overloaded is?

Does this elevator appear to be​ safe?

The frequency distribution shows the average seasonal rainfall in California measured in inches

Please complete the chart (frequency and cumulative relative frequency) CRF to two decimal points

Rainfall                                 Frequency                          Cumulative Relative Frequency

0 -9.99                                   2                                                             

10 – 19.99                            25                                          

20 - 29.99                                                                             0.88       

30 -30.99 0.98

40 – 40.99                                                                            1.00

Total                               50

I would like a clear understanding of where the values come from to complete this chart. I have seen a couple of answers that do not clearly explain how the missing values are generated (Formulas).

Independent random samples of professional football and basketball players gave the following information. Assume that the weight distributions are mound-shaped and symmetric.

Weights (in lb) of pro football players: x₁; n₁ = 21

Weights (in lb) of pro basketball players: x₂; n₂ = 19

(a) Use a calculator with mean and standard deviation keys to calculate x₁, s₁, x₂, and s₂. (Round your answers to one decimal place.)

(b) Let μ₁ be the population mean for x₁ and let μ₂ be the population mean for x₂. Find a 99% confidence interval for μ₁ − μ₂. (Round your answers to one decimal place.)

Weatherwise is a magazine published by the American Meteorological Society. One issue gives a rating system used to classify Nor'easter storms that frequently hit New England and can cause much damage near the ocean. A severe storm has an average peak wave height of μ = 16.4 feet for waves hitting the shore. Suppose that a Nor'easter is in progress at the severe storm class rating. Peak wave heights are usually measured from land (using binoculars) off fixed cement piers. Suppose that a reading of 39 waves showed an average wave height of x = 17.2 feet. Previous studies of severe storms indicate that σ = 3.8 feet. Does this information suggest that the storm is (perhaps temporarily) increasing above the severe rating? Use α = 0.01. Solve the problem using the critical region method of testing (i.e., traditional method). (Round your answers to two decimal places.) test statistic = critical value = State your conclusion in the context of the application. Reject the null hypothesis, there is sufficient evidence that the average storm level is increasing. Reject the null hypothesis, there is insufficient evidence that the average storm level is increasing. Fail to reject the null hypothesis, there is sufficient evidence that the average storm level is increasing. Fail to reject the null hypothesis, there is insufficient evidence that the average storm level is increasing. Compare your conclusion with the conclusion obtained by using the P-value method. Are they the same? The conclusions obtained by using both methods are the same. We reject the null hypothesis using the traditional method, but fail to reject using the P-value method. We reject the null hypothesis using the P-value method, but fail to reject using the traditional method.

Consider the following time series data:

Month 1 2 3 4 5 6 7

Value 25 14 19 11 18 22 16

(a) Compute MSE using the most recent value as the forecast for the next period. If required, round your answer to one decimal place. What is the forecast for month 8? If required, round your answer to one decimal place. Do not round intermediate calculation.

(b) Compute MSE using the average of all the data available as the forecast for the next period. If required, round your answer to one decimal place. Do not round intermediate calculation. What is the forecast for month 8? If required, round your answer to one decimal place.

Going back to problem 1, in real life you can, without much difficulty, get the mean grades of Prof. Lax’s classes but that is about it; meaning you will have no idea how his grades would be distributed, nor would you have any idea about the standard deviation of these grades. (I doubt Prof. Lax would advertise his laxness on his website. Contrary what you might believe that is academically bad form and might negatively affect his students’ hireability in the job market). However, you have access to Miss Z’s data (which she swears is obtained by a random selection process) and the grades she obtained in her random sample of nine were:

79, 75, 84, 63, 98, 52, 87, 99, 83

a .to help Miss Z with her decision to take this course with Prof. Lax or not, create a 97% confidence interval (CI) for the mean using Miss Z.’s data. Make sure that you do the necessary checks.

b. Does your interval capture the rumored population mean of 85?

c. Calculate the margin of error (ME or simply E) of your confidence interval.

d. Miss Z thinks a margin of error (or E) of 7 points or more will have a significant negative effect on her GPA. How does the ME (or E) of your 97% CI from part (c) compare to what she says her GPA can afford? If your CI’s ME (or E) is different than 7 points she can afford what are the ways you can use to reduce the margin of error down to 7 or smaller. Discuss all that can be done. 3

When evaluating research, what factors should be considered? Why are these factors important? Provide some examples to illustrate the importance of each factor.

The lengths of a particular​ animal's pregnancies are approximately normally​ distributed, with mean mu equals280 days and standard deviation sigma equals12 days. ​(a) What proportion of pregnancies lasts more than 295 ​days? ​(b) What proportion of pregnancies lasts between 259 and 283 ​days? ​(c) What is the probability that a randomly selected pregnancy lasts no more than 262 ​days? ​(d) A​ "very preterm" baby is one whose gestation period is less than 250 days. Are very preterm babies​ unusual?

For each of the following cases, assume a sample of n observations is taken from a normally distributed population with unknown mean μ and unknown variance σ2. Complete the following: i) Give the form of the test statistic. ii) State and sketch the shape of the prob. distribution of the test statistic when the null hypothesis is true. iii) Give the range of values of the test statistic which comprises the rejection region. iv) Sketch in the area(s) associated with α on the probability distribution of the test statistic. v) Compute the observed value of the test statistic. Give the approximate size of the p-value. vi) Using the observed value of the test statistic, state your conclusions with the appropriate probability statement. a. H0: σ2< 15; HA: σ2> 15, α = .05, n = 50, s2= 19.5

b. H0: σ2= 20 ; HA: σ2≠20, α = .05, n = 50, s2= 22.1