Determine whether each of the following variables would best be modeled as continuous or discrete 6.1 a. The height of a high rise apartment building b. The number of floors in a high rise apartment building

Given data of height, x and stories, y is of six notable buildings in a city. height numbers: 768, 628, 518, 511, 491, 478 and stories numbers: 52, 48, 46, 43, 37, 35.  What is the scope and y intercept of this? Round the scope to three decimal places as needed. Round the y-intercept to two decimal places as needed.

Consider that the relation between the cost of production, the lot of size production and the number of labours if for a particular point the slop is change to another point where Xp=26 for X1, Xp=22 for X2, find the linear model for this relation and write it.

A gaming system purchased for $2,750 is to be depreciated linearly over 4 years and has a scrap value of $100. a).What is the linear function that represents this information? b).What is the value of the entertainment system at the end of 2 years?

The mtcars data set contains data on 32 cars from the 1974 Motor Trend US magazine. The data set has 11 variables that measure different aspects of the cars, such as miles per gallon (mpg), horsepower (hp), weight (wt), and number of cylinders (cyl). The data set can be used to study the relationship between fuel consumption and car performance, as well as other factors that affect the efficiency and design of the cars. What is the maximum and minimum number of the range regarding the explanatory variable for which the regression model can make valid predictions?

The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1�1) is the square footage, the second independent variable (x2�2) is the number of bedrooms, and the third independent variable (x3�3) is the age of the house. Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 11191119 33 33 100900100900 13081308 33 44 119800119800 14521452 33 66 189800189800 16931693 33 66 190500190500 20052005 33 66 196900196900 22102210 33 99 216600216600 23012301 44 99 250900250900 23452345 44 1010 267800267800 28532853 55 1313 270000270000 Copy Data Step 1 of 2 :   Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places

Identify whether the variable is continuous or discrete: Occupation.

Assume a cars depreciation value can be modeled by a linear function where y represents the cars value after t years. If the car was worth $1200 after 2 years and worth $300 after 5 years:  a. How much did the car depreciate each year? b. How much was the car worth when it first debuted? c. Write an equation that models the cars value as a function of time d. When will the car have no value?

Fill in the blanks outside the table showing the changes in the x- and y-values. Then, answer the questions. 1) a. Is it a linear function? b. Why/Why not? -3 1. -2 -1 3 4 1 5 2) a. Is it a linear function? b. Why/Why not? -5 2 1. -2 4 -1 6 1. 2 8 1. 10 3) -10 a. Is it a linear function? 1. -5 b. Why/Why not? 4 2 5 1. 10 -3 4) y a. Is it a linear function? b. Why/Why not? -1 -9 ]. -4 1. 1 1. 3 7 11 1. 11 15