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 Correlation Results display. Is there sufficient evidence to support 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 a measured chest size can be used to predict the weight? Use a significance level of a=0.05. Correlation coeff, r. 0.961961 Critical r: +0.2680855 P-value (two tailed): 0.000 there is not a linear correlation between the two. OB. Yes, it is easier to measure a chest size than a weight because measuring weight would require lifting the bear onto the scale. The chest size could not be used to predict weight because there is too much variance in the weight of the bears. OC. Yes, it is easier to measure a chest size than a weight because measuring weight would require lifting the bear onto the scale. The chest size could be used to predict weight because there is a linear correlation between the two. On No it is easier to reasure weinht than chest size herause the chest is not a flat surface 4. X

Statistics Question

A study was undertaken to see how accurate food labeling for calories on food that is considered reduced calorie. The group measured the amount of calories for each item of food and then found the percent difference between the measured and labeled amount of calories. The group also looked at food that was nationally advertised, regionally distributed, or locally prepared. Do the data indicate that at least one of the mean percent differences between the three groups is different from the others? The data is summarized in the table below. National Mean Standard Deviation Sample Size Local -2.1 Regional 27.1667 143.125 Fill in the hypotheses below: Ho: Select an answer Ha: Select an answer 10.3257 20.797 53.5922 20 12 8

An experiment was conducted to determine if exposure to an advertisement would change attitude toward a product. Each subject’s attitude before and after exposure to the advertisement was recorded, using a valid ten point scale. The results indicated: Subject Attitude Post Exposure Attitude Pre Exposure   1 6 4 2 8 5 3 6 6 4 4 3 5 7 2 6 6 3 7 9 6 8 7 6 9 8 5 10 8 6   Set up hypotheses and test to determine if the mean attitude toward the product increased as a result of exposure to the advertisement. (Note: these samples must be regarded as dependent, as each subject served as both a control (pre-exposure) and a treatment (post exposure).   Fully interpret your result.

What is a confounding variable in research, and how can it affect the results of a study? Provide an example of a confounding variable in a hypothetical study.

What is the relative frequency? Give your answer to two decimal places.

The accompanying technology output was obtained by using the paired data consisting of foot lengths​ (cm) and heights​ (cm) of a sample of 40 people. Along with the paired sample​ data, the technology was also given a foot length of 23.5 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 23.5 ​cm, what is the single value that is the best predicted height for that​ person?   The single value that is the best predicted height is _____cm.     The regression equation is Height=50.8+3.20 Foot Length   Predictor Coef SE Coef T P Constant 50.82 11.65 4.36 0.000 Foot Length 3.1982 0.4903 6.52 0.000   S=5.50372 ​R-Sq=71.7​% ​R-Sq(adj)=71.0​%   Predicted Values for New Observations   New Obs Fit SE Fit ​95% CI ​95% PI 1 125.978 1.744 ​(121.356​, 130.600​) ​(114.681​, 137.275​)   Values of Predictors…

A professor was interested in how exam performance related to study time. Just before an exam began the professor asked students in his class to write down how many hours they had studied for the exam in the past two weeks. He then compared their exam scores to the reported study time. What type of study did the professor carry out? ex post facto correlational case experimental

A study was done to determine if food insecurity/hunger impacts psychological distress (this is fake data to make it simple, but it is based off of a 2020 study that examined the associations between food insecurity and psychological distress among older people in Ghana). Participants were broken into three groups based on their responses: no food insecurity, moderate food insecurity, and severe food insecurity. Psychological distress was measured on the Kessler Psychological Distress Scale (K10), where scores range from 0 to 50. Enter data and test to determine if there is a significant difference between the 3 groups in psychological distress Food insecurity:                      Psychological distress scores (higher = more distress) No food insecurity                  5          6          8          10        5          4          14        9 Moderate food insecurity        16        14        9          7          14        8          16        17 Severe food insecurity…

The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 17.8 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 17.8 cm, what is the single value that is the best predicted height for that person? Click the icon to view the technology output. The single value that is the best predicted height is (Round to the nearest whole number as needed.) cm. Technology Output The regression equation is Height = 53.3+4.25 Foot Length Predictor Constant Coef SE Coef 53.28 11.44 Foot Length 4.2537 0.4699 S = 5.50739 R-Sq=72.3% R-Sq (adj) = 71.6% T 4.66 9.05 Predicted Values for New Observations New Obs 1 Fit SE Fit 128.996 1.733 (124.309, 133.683) 95% CI Foot New Obs Length 1 17.8 0.000 0.000 Values of Predictors for New…