Among a random sample of the stat's census deciles (a small geographic unit), there is a -0.90 correlation (p=.50) between school truancy rates and the rates of alcohol sales to minors.  How would you characterize the relationship between truancy and alcohol sales to minors? A.  We cannot be confident that there is a relationship between the two variables. B.  We have evidence that there is a strong, positive correlation between the two variables. C.  We have evidence that there is a strong, negative correlation between the two variables. D.  We have evidence that there is a weak, positive correlation between the two variables. E.  We have evidence that there is a weak, negative correlation between the two variables.

even though we may observe a strong relationship between the explanatory and response variables, a lurking variable may also influence the response vatiabke. This is reffered to as A. correlation  B. confounding C.association  D. causation

An Economics instructor assigns a class to investigate factors associated with the gross domestic product (GDP) of nations. Each student examines a different factor (such as life expectancy, literacy rate, etc.) for a few countries and reports to the class. Apparently, some of the classmates do not understand Statistics very well because several of their conclusions are incorrect. Explain the mistakes in comments a and b below. a) Explain the mistake in the statement "There was a very strong correlation of 1.22 between Life Expectancy and GDP" Choose the correct answer below O A. A correlation cannot be greater than 1. O B. A correlation that is greater than 1 implies that the variables are not quantitative, so the correlation cannot be interpreted. OC. A correlation that is close to 1 implies a very weak correlation. The correlation must be close to 10 for it to be interpreted as a very strong correlation. O D. A correlation that is greater than 1 implies a weak correlation, not a…

. Dr. Hafen is doing research on the effects of study time (how many minutes students study) on test performance (their score on an exam).  He hypothesizes that the amount of study time will impact an individual’s performance on the exam.  Then Dr. Hafen collects data on 100 students.  He collects data on their ‘study time’ and their ‘exam performance’, then runs a Pearson correlation.  In your own words, please describe what Dr. Hafen’s null hypothesis would be and what Dr. Hafen’s alternative hypothesis would be.  If Dr. Hafen makes a Type I error, what would that mean in the context of this study?  If he makes a Type II error, what would that mean in the context of this study?

A personnel manager wants to find out if a test carried out during an employee’s interview and a skills assessment at the end of basis training is a guide to performance after working for the company for one year. The table below shows the results of the interview test of 10 employees and their performance after one year. Please provide the Pearson r correlation coefficient for the indicated data. How would you rate the correlation coefficient (strong, moderate, weak)? Employee A B C D E F G H I J Interview test,  x % 65 71 79 77 85 78 85 90 81 62 Performance after one year,  y % 65 74 82 64 87 78 61 65 79 69

The following table shows the selling​ prices, in thousands of​ dollars, and the square footages of five randomly selected houses recently sold by a real estate company. The data have a sample correlation​ coefficient, rounded to three decimal​ places, of 0.652. Using alpha equals 0.01 and the data given​ below, test the significance of the population correlation coefficient between a​ house's selling price and its square footage. What conclusions can you​ draw? Selling_Price Square_Footage 253 2725 191 1825 258 2193 163 2105 242 2358 What is the​ p-value?

What would you conclude if 8 pairs of data produce a linear correlation coefficient of r = – 0.608 if the significance level is 0.01. Must show critical value and decision before writing the conclusion.

Fifty-four wild bears were anesthetized, and then their weights and chest sizes were measured and listed in a data set. Results are shown in the accompanying display. Is there sufficient evidence to support Correlation Results the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuring an anesthetized bear, is it easier to measure chest size than weight? If so, does it appear that Correlation coeff, r: 0.959614 a measured chest size can be used to predict the weight? Use a significance level of = 0.05. Critical r: +0.2680855 P-value (two tailed): 0.000 Но: Р H1:P (Type integers or decimals. Do not round.) Identify the correlation coefficient, r. (Round to three decimal places as needed.) Identify the critical value(s). (Round to three decimal places as needed.) O A. There is one critical value at r= . B. There are two critical values at r= ± Is there sufficient evidence to support the claim that there is a linear correlation between…

What are the reasons why causal conclusions cannot be made from a correlation?