Determine whether the following bivariate data are correlated or not. If they are correlated, tell the direction of theassociation. Evaluate whether the correlation is most likely strong or week.a. Annual income of the family and floor area of the residence house.b. Age and price of a car.c. Gross national product ad level of technology of a country.d. Age and reaction time of persons over 18 years of age.e. Yearly income and number of years of schooling of company owners.

Given that We have to find the direction of association positive or negative.... a. Annual…

Answered: Determine whether the following…

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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Let x be the average number of employees in a group health insurance plan, and let y be the average administrative cost as a percentage of claims.
An electronics retailer would like to investigate the relationship between the selling price of a certain digital camera model and the demand for it. The accompanying table shows the weekly demand for the camera in one particular market along with the corresponding price. Determine the sample correlation coefficient between the selling price and the demand for this camera. Click the icon to view the demand and price data. r = (Round to three decimal places as needed.) Demand and Price Demand Price 18 320 19 330 15 340 15 350 12 360 12 370 8 380 Print Done
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A group of Economics students wish to know whether a student's spending is related to his/her parents' monthly income. Which measure must be used? Group of answer choices A. regression analysis B. correlation analysis C. descriptive statistics D. partial correlation analysis
Data relating to age of the students and their games. Calculate correlation between the age of the students and their playing habits. Age 15 16 17 18 19 20 No. of Students 250 200 150 120 100 80 Regular Players 200 150 90 48 30 12
Jim and Larry run a soup stand. The data below represents the sales of soup that they had on 8 randomly selected days, along with the high temperature on that day. a) Create a scatter diagram for this data set and comment on whether there is a positive or negative relationship between these variables.b) Calculate the correlation coefficient between temperature and sales.c) Does a linear relationship exist between these variables? Explain to me why or why not this is the case.d) Using excel, calculate the least squares regression equation for this data set. e) Using your equation, predict the soup sales on a day where the temperature is 29 degrees.f) Interpret the slope of this equation.g) Can we use this data set and regression equation to predict the sales on a day when the average temperature is 102 degrees? Why or why not?h) What is the R-squared for this equation? How do you interpret that R-squared?
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Use the table to find the following, unit price being your (x) and units sold (y). a. Find linear correlation coefficient. Years of Experience Salary in 1000$ 2 3. 15 28 42 13 64 50 16 90 11 58 8 54 b. Find the line of best fit. c. How many units will sell if the unit price was 0.85.
An economist wants to determine whether there is a linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. The data are shown in the table below. c. Compute and interpret the correlation coefficient. d. Compute and interpret the coefficient of determination. e. Test for the significance of the linear relationship. Use a 0.05 level of significance. State your conclusion. Hint: Your conclusion is either of the following. • There is a significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. • There is no significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. GDP 1.6 3.6 4.9 1.1 0.9 2.9 2.7 2.3 1.6 1.5 (trillion dollars) Carbon Dioxide Emissions 428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5 (millions of metric tons)
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
Calculate the value of coefficient of correlation petween price and supply. What is its probable error ? Interpret the value of coefficient of correlation. Price 8. 10 15 17 20 22 24 25 Supply : 25 30 32 35 37 40 42 45
Construct a scatter plot from the given points and describe the correlation.

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