For item 1 to 5, referring to Table 3.5.Consider the ff. data set, in which a maker of asphalt shingles is interested in the relationship between sales for aparticular year and factors that influence sales. Y- sales(in thou $); X1- promotional accounts; X2- active accounts;X3- competing brands; X4- potentialTable 3.5YX1 X2 X3X4291.9 9 5610160 4 424339.4 6.5 7316159.6 5.5 60 11 786.3 5 44 12 12237.5 650 66107.2 5394680510 Correlating the active accounts to promotional accounts, competing brands and potential in 2 decimal places.Let the active accounts be the dependent variable.Statement A. Active account has a moderate positive relationship to promotional accounts.Statement B. Active account has a moderate negative relationship to competing brands.Statement C. Active account has a high positive relationship to potential.Statement D. Active account has a high positive relationship to promotional accounts and competing brands.Which of the following choices is correct?O Statement D is true.Statements B and D are true.Statements C and D are true.Statements A, B and C are true.Statements A and B are false, D is true.

Solution: No. of variables=5Variable=1. Sales (Y)2. Promotional accounts (X1)3. Active accounts…

Answered: Correlating the active accounts to…

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 accompanying table shows the height and weight of five football players in training camp. The body mass index (BMI) is used to assess the relative amount of fat in a person's body. If w is weight in pounds and h is height in inches, then BMI = 703wh. Some physicians consider your body fat abnormal if your BMI satisfies BMI-23>3. Which of the five players does not have abnormal body fat? does not have abnormal body fat. Player D Player B Player C Player E Player A .. Player A B C E Ht. 6-8 5-8 5-10 5-9 6-6 Wt. 359 240 227 164 342
The data set Pain contains hypothetical data for a clinical trial of a drug therapy to control pain. The clinical trial investigates whether adverse responses increase with larger drug doses. Subjects receive either a placebo or one of four drug doses (1, 2, 3, or 4 units). An adverse response is recorded as Adverse = 'Yes'; otherwise, it is recorded as Adverse = 'No'. The number of subjects for each drug dose and response combination is contained in the variable Count. (a) Construct a contingency table that corresponds to the data set created above. What type of variable is Dose? (B) Compute the sample proportions of adverse responses at each dose level. Do you observe any trend in the proportion of adverse responses with respect to dose level? (C) Conduct a Cochran-Armitage trend test at the 5% significance level to address the interests of the trial.
15 wheelchair users were randomly assigned to three groups with 5 in each group. These participants navigated in virtual-reality settings. Group 1 participants were in the virtual-reality setting (a building) as wheelchair users. Group 2 participants were in the virtual-reality setting in a wheelchair pushed by a walking person. Group 3 participants walked without aid in the virtual-reality setting. Joan measured the time each participant needed to complete the navigation of the virtual-reality setting. What is the independent variable(s)?
How to find independent and defendant variables
USE MANUAL SOLUTIONS NO EXCEL
Atlantic hurricanes form off the western coast of Africa. As warm moist air rises, it begins to rotate around a low- pressure system. This rotation is sustained by warm ocean temperatures and a central low pressure. Once a sustained wind speed of 74 miles per hour is reached, the storm is considered a hurricane. As man-made climate change continues to increase the temperature of the Earth's oceans, it is expected that the strength of hurricanes will Typically, a lower central pressure within a hurricane's eye is associated with a higher wind speed. Here, statistical software (R/RStudio) will be used to explore this relationship by applying linear regression methods. Using a database of landfalling hurricanes in the United States, a linear regression will be used to correlate the central pressure of each hurricane to its maximum sustained wind speed. increase. Regression Plot y=942.196-0.884*x R^2=0.768 ----- ----------- -. •... •. 980 1000 940 960 900 920 Independent Variable (x)…
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A professor in a university is trying to conduct a research study. His study is trying to determine if his 8 am lecture is more interactive with him than his 2 pm lecture. What is the independent variable? How many levels are there for the independent variable? What is the dependent variable?
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Please help me better understand word problem. A researcher was investigating variables that might be associated with the academic performance of high school students. The data included the average Math SAS score of all high school seniors in the city that took the exam (labeled as the variable SAT-M), the average number of dollars per pupil spent on education by the city (labeled as the variable $Per Pupil), and the percentage of high school seniors in the city that took the exam (labeled as the variable %Taking). As part of the researcher's investigation, he ran the following multiple linear regression model as SAT-M=Beta0 + Beta1($Per Pupil) + Beta2(%Taking). This model was fit to the data using the method of least-squares, results shown inside of table within photo. Question: What's the 95% confidence interval for Beta1, (coefficient of variable $PerPupil). Then determine the interpretation for Beta2 (coefficient of %Taking variable).
Identify which of the variables is most likely to be the explanatory variable, and which is most likely to be the response variable. Explain. Identify whether each variable is categorical or quantitative, and classify the relationship as C-Q, C>C, or Q-›Q, and explain how you know. Determine what type of data display and description is appropriate for the relationship, and explain why. Create an appropriate data display and describe the relationship using vocabulary appropriate to the type of data. Describe any potential lurking (confounding) variables and describe how this impacts association (correlation) versus causation.
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