A researcher wants to test the relationship between the number of years of formal education received (X;) and the average weekly earnings (Y;) (measured in hundred dollars). A report released by a government agency suggests that the average weekly earnings of individuals with no formal education is equal to $429. The researcher wants to test whether the average weekly earnings with no formal education is $429 or greater than that. He collects data from a sample of 100 individuals and estimates the following regression function: Ŷ₁ = 7.14+3.14X;, (3.24) (2.18) where ŷ, is the predicted value of the weekly earnings for the 1th individual and the standard errors for the coefficients appear in parenthesis. The t-statistic for the test the researcher wants to conduct will be (Round your answer to two decimal places.) At the 5% significance level, the researcher will the null hypothesis that the average weekly earnings of individuals with no formal education is equal to $429.

7. A television station wishes to study the relationship between viewership of its 11 p.m. news program and viewer age (18 years or less, 19 to 35, 36 to 54, 55 or older). A sample of 250 television viewers in each age group is randomly selected, and the number who watch the station’s 11 p.m. news is found for each sample. The results are given in the table below.

We are interested in analysing the effect of the lockout laws introduced in some areas of the Sydney CBD and nearby surrounds on the number of alcohol-related violent incidents.  Suppose we have two samples of data on the number of violent incidents in a number of local areas of the Sydney CBD and nearby surrounds. Pubs, hotels and clubs located in a subset of these areas became subject to the lockout laws when they were introduced in 2014. The first sample is from 2010 before the introduction of the lockout laws, and the second is from 2015 after the introduction of the law. The hypothesis we wish to test is that the introduction of the lockout laws reduces violent incidents in the areas in which the lockout laws were put in place.  We use a difference-in-difference model on the pooled data from 2010 and 2015. We find the following results: viol_inc hat = 33.21 (5.29) + 12.43lockout (7.01) – 4.19Yr2015 (2.98) – 3.32 (lockout x Yr2015) (1.85) N = 181, R^2 = 0.128 Where: viol_incis the…

Textbook authors must be careful that the reading level of their book is appropriate for the target audience. Some methods of assessing reading level require estimating the average word length. We've randomly chosen 20 words from a randomly selected page in Intro Stats and counted the number of letters in each word: 5, 5, 2, 11, 3, 5, 6, 8, 5, 4, 7, 2, 9, 4, 8, 10, 4, 7, 6, 9 Suppose that our editor was hoping that the book would have a mean word length of 6.8 letters. Does this sample indicate that the authors failed to meet this goal? With a significance level of 0.05, test an appropriate hypothesis and state your conclusion. (i.e state the appropriate null and alternative hypotheses, calculate the test statistic, conclude and interpret it).

PLEASE PROVIDE ME THE ACCURATE ANSWER MAKE SURE THE ANSWER IS 100 % RIGHT  DOUBLE CHECK IF YOU HAVE TO MAKE SURE THE ANSWER IS RIGHT PLEASE DO NOT GIVE ME THE WRONG ANSWER.

A salesman for a new manufacturer of cellular phones claims not only that they cost the retailer less but also that the percentage of defective cellular phones found among his products, ( p1 ), will be no higher than the percentage of defectives found in a competitor's line, ( p2 ).  To test this statement, the retailer took a random sample of 130 of the salesman's cellular phones and 110 of the competitor's cellular phones.  The retailer found that 10 of the salesman's cellular phones and 5 of the competitor's cellular phones were defective.  Does the retailer have enough evidence to reject the salesman's claim?  Use a significance level of α=0.05 for the test. Step 5 of 6 :  Determine the decision rule for rejecting the null hypothesis H0 . Round the numerical portion of your answer to three decimal places.

We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings.  We use the following model to estimate the effect of a training program on annual earnings in 2018: ln⁡(earn2018)=β0+β1train+β2ln⁡(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education    exper = the individual's years of experience   We find: ln⁡(earn2018)^= 11.672 + 0.041train + 0.821ln⁡(earn2016) + 0.037educ + 0.012exper                          (5.864)   (0.019)         (0.258)                       (0.013)         (0.007) n=1278, R2= 0.048 Which of the following is the correct interpretation of the…

We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings.  We use the following model to estimate the effect of a training program on annual earnings in 2018: ln⁡(earn2018)=β0+β1train+β2ln⁡(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education    exper = the individual's years of experience     Now, I want to test whether the effect of an additional year of education increases earnings by twice as much as an additional year of experience. My null hypothesis is H0:β3=2β4. To get the standard error I need to conduct this hypothesis test, I rearrange or re-parameterise…

A salesman for a new manufacturer of cellular phones claims not only that they cost the retailer less but also that the percentage of defective cellular phones found among his products, ( p1 ), will be no higher than the percentage of defectives found in a competitor's line, ( p2 ).  To test this statement, the retailer took a random sample of 185 of the salesman's cellular phones and 150 of the competitor's cellular phones.  The retailer found that 22 of the salesman's cellular phones and 11 of the competitor's cellular phones were defective.  Does the retailer have enough evidence to reject the salesman's claim?  Use a significance level of α=0.05 for the test. Step 1 of 6 :  State the null and alternative hypotheses for the test.

A manufacturer of cat food was planning to survey households in the US to determine purchasing habits of cat owners. Among the questions to be included are those that relate to Where cat fat is primarily purchased. Whether dry or moist cat food is purchased. The number of cats living in the household. Whether the cat is pedigreed. Describe the population. For each of the four items listed, indicate whether the variable is categorical or numerical.