a. Choose the correct graph below.The following table compares fuel economy as calculated by car owners and bygovernment standards.O A.OB.CarOwner (x) Government (y) OAy60-60-A.21.925.036.4B29.529.726.9D24.228.234.416.029.815.3E36.7606014.424.315.532.6GOc.47.0Ay60-60-a. Construct a scatter diagram to investigate the relationship between ownercalculated mileage and government calculated mileage.b. Is there a relationship between the variables? Is it positive or negative?6060b. Is there a relationship between the variables? Is it positive or negative?O The relationship is negative because owner mileage is usuallybelow the government mileage.The relationship is positive because as one variable increases, theother variable also tends to increase.The relationship is positive because owner mileage is almostexactly equal to government mileage.O There is no relationship between the two variables.O O

Answered: Image /qna-images/answer/fea51e68-ec1d-47b7-b350-dd6fa0c5cf14.jpg

Answered: Choose the correct graph below. The…

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 table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.8 6.7 3.4 2.3 2.8 2.5 0.3 14.4 10.7 9.7 5.8 6.7 4.5 I = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y 0.86% + 4.03. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 4.9 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 2.4 thousand automatic weapons? se Answer = Round to 3 decimal places. Hint: Help Question Help: Message instructor Submit Question ere to search
Statistics Question
1. A researcher desires to know whether the typing speed of a secretary (in words per minute) is related to the time (in hours) that is takes the secretary to learn to use a new word processing program. The data are shown below. f. Calculate the coefficient of determination and explain this value.
Help me answer the following, thanks.
Identify the reasons the following statements are incorrect. Not all reasons will be used. Researchers found that the correlation between the number of pages in novels and Correlation should depend on units. the number of hours it takes to read them is r = 0.71 pages. The correlation between the advertised selling price of cars and the makes of the Both variables should be numerical. cars is r = -0.69. The amount of ice cream in a sundae and the total grams of fat are highly Correlation should depend on units. correlated (r = 1.15). The correlation between total daily snowfall in inches and daily low temperatures Both variables should be numerical. in degrees Fahrenheit differs from the correlation between daily snowfall in inches and daily low temperatures in degrees Celsius. Answer Bank One of the variables should be categorical. Correlation should not depend on units. Correlation should depend on units. Correlation should be greater than 0. Both variables should be numerical.…
The boxplot below represents annual salaries of attorneys in thousands of dollars in Los Angeles. About what percentage of the attorneys have salaries between $219,000 and $326,000? A. 90% B. 60% C. 15% D. 50% E. None of the Above 160 100 200 220 240 260 200 300 320 340 360 300
A Laspeyres price index for each year?
Nine students held their breath, once after breathing normally and relaxing for one minute and once after hyperventilating for one minute. The table indicates how long (in seconds) they were able to hold their breath. Is there an association between the two variables? Subject A B C D E F G H I NORMAL 56 56 65 65 50 25 87 44 35 Hyperventilating 87 91 85 91 75 28 122 66 58
A survey taken in a hotel shows the number of cups of coffee consumed with each meal.Find the relative frequency corresponding to the meal number 5. Frequency 5 Meal Number 3 3 2 а. 50% b. 20% 30% d. 40%
In order to advise students properly, a college advisor is interested in the relationship between a student's GPA and the number of hours the student an average weck. The data are shown in the table. Study Time (in hours) 33 Student GPA 3.2 19 16 2.4 31 3.1 5. 24 2.7 28 2.9 7 1.7 34 3.3 14 2.2 10 35 3.4 11 1.8 Part 1 of 5 (a) Let I represent study time and let y nepresent GPA. Graph the points. Grade Point Average vs. Weekly Study Time Weckly Study Time (hours) Part 2 of 5 (b) Does there appear to be a linear trend? Yes Part: 2/5 Part 3 of 5 (c) use the data points (16, 2.4) and (34, 3 3) to find an eguation of the line through these points. Express numbers as decimas or integers. Write answer in slope-intercept form (y = MI+ b). The equation is GPA
Christine is a sales manager for a car rental company. She has a scatterplot with data regarding the years of experience of sales representatives and percent of satisfied customers. What information would the scatterplot below indicate? Select all that apply. 100 90 80 70 50 40 30 20 10 1 3 5 6 7 Years of Experience of Salesperson O Satisfaction rate is the independent variable. O Satisfaction rate is the dependent variable. O Years of experience is the independent variable. O Years of experience is the dependent variable. O Christine should consider customer satisfaction training for her more novice representatives. O There is a positive correlation between years of experience and customer satisfaction. O There is a negative correlation between years of experience and customer satisfaction. O There is no correlation between years of experience and customer satisfaction. Percent of Satisfied Customers
Select the most appropriate response. If the correlation between a person’s age and annual income is 0.60, then the coefficient of determination tells us that: 36% of the variation in a person’s annual income can be explained by the predictor variable age. 36% of a person’s annual income can be explained by their age 60% of the variation in a person’s annual income can be explained by the predictor variable age 60% of a person’s annual income can be explained by their age

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