Question 1The data in the chart below is for the distance (in cm) to the near point, the pointnearest the eye at which the eye can accurately focus, at a person's age. Use aspreadsheet application to create a linear regression model for the data in the form y =mx + b, where x is age and y is near point. Using your model, predict the near point ofsomeone who is 87 years old. Round your answer to 1 decimal place.Age (years)102030405060cmNear Point (cm)7.5911.517.252.583.3

From given data we have : Age Near Point 10 7.5 20 9 30 11.5 40 17.2 50 52.5 60 83.3…

Answered: The data in the chart below is for 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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Perform Linear Regression Analysis by Octave (or MatLab) software using formulas for calculation of required parameters. Make the graph y versus x values and ytted VS. X on the same plot. x Answers: 0.05 0.04 35.7210 23.3960 7 0.03 13.3970 X S 0.02 6.0310 0.01 1.5190 0 0.0000
What are the interpretations of the Y intercept and the slopes in a multiple regression model?
The attached table shows the level of water (y) measured in centimeters from the bottom of the container after (x) seconds. Create a table in DESMOS and graph the data. Attach a screenshot of your data plot from DESMOS. Does your data plot look linear or quadratic?
Use linear regression on your calculator to find the equation of the linear function that best fits this data. 1 3 4 5 6 y 84 110 139 170 180 222 = mx + b. Round all numbers to 2 decimal Hint: Write your final answer as an equation in the form y places.
Use linear regression to find the equation for the linear function that best fits this data. Round both numbers to two decimal places. Write your final answer in a form of an equation y= mx +b X y 1 92 2 110 4 5 3 139 160 171 6 196
The R2 from a regression of consumption on income is 0.75. Explain how the R2 is calculated and interpret this value.
Using the regression to model annual income, a variable for years of education has a slope = 2000. This means that the model predicts each additional year of education will yield an extra 2,000 for the average person's salary, regardless of how many total years of education a person has. A. True B. False
Use the data below for the PHONES questions that follow. Data compiled from the CIA World Factbook, https://www.cia.gov/. The number of cell phones in use in a country can be approximated from the number of main lines in use in that country by using the regression equation yhat=25+3x which had an R² of about 90%. (The numbers here are millions. For example, "1.2" would represent 1.2 million or 1,200,000.) PHONES 1 At the time the data was collected, the USA had about 314 million cell phones in use and about 150 million main lines in use. What was the residual for the USA? Enter a number of millions. For example, if your answer is 2.85 million enter "2.85". Do not enter anything else such as symbols or words.
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. 220- 210 200 190 180 170 160 150 Year 0 130- 120- 110- 100- 90 801 6 12 15 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. Retail Sales 85.851 108.426 141.781 169.256 202.297 222.266 3 6 9 12 15 Use the above scatter plot to decide whether the linear model fits the data well. The function is a good model for the data. The function is not a good model for the data Estimate the retails sales in the U. S. in 2015. billions of dollars.
Some pedometers have a default setting that assumes that the number of steps a person must take to walk 1 mile decreases by 32 steps per 1 inch increase in height. Suppose a person 72 inches tall makes 2,250 steps per mile. Find a linear model of the number of steps S per mile as a function of height h (in inches). S = Use the model to predict the number of steps per mile a person who is 64 inches tall will take. steps per mile

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