For the house sales data in Table 9.4, Table 9.12 shows a regression analysis relating selling price to number of bedrooms.What is the linear relationship between selling price and number of bedrooms?VariablepricebedroomsSum ofSource DFSquaresModel1.575534E+111Error 98 8.575962E+11Total 99 1.01515E+12VariableInterceptbedroomsO positive4O negativeNMean100 155331100 3.000O no relationshipTABLE 9.12Root MSE 93547 R-Square 0.1552ParameterEstimate-2841261248Std Dev101262.20.651339MeanSquare1.575534E+118750981211StandardError4430314435t-0.644.24Sig.0.5228<.0001

The regression analysis for the house sell data related to the number of bedrooms is given.

Answered: For the house sales data in Table 9.4,…

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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While performing searches on the Internet to find accommodation, Jasmine realized that the cost of a housing unit seems to be related to the number of inhabitants of the municipality where the unit is located. She noted the following data. She represented the data in a scatter plot, where she drew a line of best fit passing through the mean point of the distribution. If Jasmine considers that a linear correlation exists between these two variables, what should be the mean monthly rent of a 4½-room unit in a municipality with a population of approximately 250 000? Justify your answer algebraically.
A linear model was fit to predict weekly Sales of frozen pizza (in pounds) from the average Price ($/unit) charged by a sample of stores in a city in 39 weeks over a three -year period. The average Sales were 63, 200 pounds (SD = 10, 670 pounds), and the correlation between Price and Sales was -0.529. If the Price in a particular week was two SD higher than the mean Price, how much pizza would you predict was sold that week?
In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.7 -0.4 -0.79 0.4
Q9 The explanatory variable in linear correlation/regression model is recorded on which of thesemeasurement scales: A. categorical B. ordinal C. quantitative D. none of the above
In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.79 -0.4 -0.79 0.4
Please answer... I'm needed only 1 hours time to dd .... Thank u
1945 is paired with cancer rates for 1975. The cigarette consumption data is from 1945 to 1975 in 5-year increments. There is a strong positive linear association between annual cigarette consumption and lung cancer rates. The correlation is 0.81. If we use the line to make predictions of the number of lung cancer deaths per 100,000 people, which of the following is an example of extrapolation? a. Predict Y when cigarette consumption is 3,500 cigarettes per person. b. Predict Y when cigarette consumption is 3,800 cigarettes per person. c. Predict Y when cigarette consumption is 4,600 cigarettes per person.
From the graphs displayed, is there evidence to suggest that there is a positive linear relationship between Square Feet and Bathrooms, for the population of real estate represented by this sample? Be sure to provide numeric support in your answer.

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