The following bivariate data set contains an outlier. Enter the data into StatCrunch. Using its graphing ability, draw a scatter plot of the data. Sketch the plot on your paper in your notebook.| x | y ||----|------|| 61 | -374 || 65 | -15 || 67 | -296 || 76 | 817 || 63 | -989 || 67 | -510 || 121| -6407|**Questions:**1. What is the correlation coefficient with the outlier? Use StatCrunch. - \( r_{\text{with}} = \_\_\_\_ \) {3 decimal places}2. What is the correlation coefficient without the outlier? Use StatCrunch. - \( r_{\text{without}} = \_\_\_\_ \) {3 decimal places}3. Would inclusion of the outlier change the evidence for or against a significant linear correlation? - ○ Yes. Including the outlier changes the evidence regarding a linear correlation. - ○ No. Including the outlier does not change the evidence regarding a linear correlation.

Answered: The following bivariate data set…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The data shown below consists of the price (in dollars) of 7 events at a local venue and the number of people who attended. Determine if there is significant linear correlation between ticket price and number of attendees. Use a significance level of 0.01 and round all values to 4 decimal places. Ticket Price 6 10 14 18 22 26 30 r= Ho: p= 0 Ha: p=0 Find the Linear Correlation Coefficient Find the p-value p-value= Attendence 151 146 146 145 The p-value is 138 137 137 O Less than (or equal to) a O Greater than a The p-value leads to a decision to Accept Ho Reject Ho Do Not Reject Ho The conclusion is There is insufficient evidence to make a conclusion about the linear correlation between ticket price and attendance. There is a significant negative linear correlation between ticket price and attendance. There is a significant positive linear correlation between ticket price and attendance. There is a significant linear correlation between ticket price and attendance.
Plz help asap, I need the sample correlation coefficient r, type of correlation, interpret the correlation, and use the table of crit values to make a conclusion.
Construct a scatter plot for the given data. Determine whether there is a positive correlation, negative correlation, or no linear correlation. x-5-34 1 |-1-202 3-4 y11 6 |-6-13 4 1-4-58
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A newspaper collected information on schools in its circulation area in order to compare their quality. Two Which is a true statement regarding Springside? measures the newspaper collected for each school, O Springside does not affect the correlation. mean class size and mean score on a statewide O Springside weakens the correlation shown in the scatterplot. reading exam, are shown in the scatterplot. One school in the report, Springside Elementary, is labeled in the graph. O Springside strengthens the correlation shown in the scatterplot. O Removing Springside would increase the value of the correlation coefficient. Comparing Schools 90 Springside 70 60 50 40 Save and Exit Next Submit Mark this and return ContentViewers/AssessmentViewer/Activity# ch hp Mean State Test Score
Weight (lbs.) 173 184 194 214 168 220 188 188 207 167 217 Systolic Blood Pressure 132 143 153 162 154 168 137 149 159 128 1 1. The sample correlation coefficient is _____________
(e)
A professor went to a website for rating professors and looked up the quality rating and also the "easiness" of the six full-time professors in one department. The ratings are 1 (lowest quality) to 5 (highest quality) and 1 (hardest) to 5 (easiest). The numbers given are averages for each professor. Assume the trend is linear, find the correlation, and comment on what it means. Calculate the correlation. r = (Round to three decimal places as needed.) Comment on the meaning of the correlation. Choose the correct interpretation below. OA. The professors that have high easiness scores tend to also have low quality scores.. OB. Being "easy" as a professor causes students to regard the professor as having a higher quality. OC. The professors that have high easiness scores tend to also have high quality scores. OD. Being "easy" as a professor causes students to regard the professor as having a lower quality. Quality Easiness D 4.7 3.7 4.7 3.1 4.2 3.5 4.2 2.6 3.9 1.9 3.5 1.8
The table lists weights (pounds) and highway mileage amounts (mpg) for seven automobiles. Use the sample data to construct a scatterplot. Use the first variable for the x-axis. Based on the scatterplot, what do you conclude about a linear correlation? Weight (lb) Highway (mpg) Which scatterplot below shows the data? A. 40- 20- 2000 5000 2795 3225 3470 32 34 28 Weight (lb) B. 40- 20+ 2000 D 5000 Weight (lb) 4085 4155 2225 25 23 38 C. ● 20+ 2000 ● ● ● ● 5000 Weight (lb) Is there a linear relationship between weight and highway mileage? A. No, there appears to be no relationship. B. Yes, as the weight increases the highway mileage decreases. C. No, there appears to be a relationship, but it is not linear. D. Yes, as the weight increases the highway mileage increases. 40- 20- 2000 3440 29 5000 Weight (lb)
Assume that you have paired values consisting of heights (in inches) and weights (in lb) from 40 randomly selected men. The linear correlation coefficient r is 0.567. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Question content area bottom Part 1 Choose the correct answer below. A. The coefficient of determination is 0.321. 32.1% of the variation is explained by the linear correlation, and 67.9% is explained by other factors. B. The coefficient of determination is 0.679. 67.9% of the variation is explained by the linear correlation, and 32.1% is explained by other factors. C. The coefficient of determination is 0.321. 67.9% of the variation is explained by the linear correlation, and 32.1% is explained by other factors. D. The coefficient of determination is 0.679. 32.1% of the variation is explained by the linear correlation, and 67.9% is…
Fifty-four wild bears were anesthetized, and their weights and chest sizes were measured. Results are showing in the graph below. IS there sufficient evidence to support the claim that there is a linear correlation between 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? Correlation Results Correlation coeff r:0.963141 Critical r ±0.2680855 P-value (two-tailed) 0.000
The following shows a linear correlation between weight and height. Image result for scatter plot scatter plot positive histogram negative O pie chart random frequency polygon biased

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