A shoe store developed the following estimated regression equation relating sales to inventory investment and advertising expenditures.where9-21+ 10x+7x2X₁ inventory investment ($1,000s)x2advertising expenditures ($1,000s)y=sales ($1,000s).(a) Predict the sales (in dollars) resulting from a $14,000 investment in inventory and an advertising budget of $11,000.$(b) Interpret b, and by in this estimated regression equation.Sales can be expected to increase by $inventory investment is held constant.for every dollar increase in inventory Investment when advertising expenditure is held constant. Sales can be expected to increase by $ASK YOUR TEACHERPRACTICE ANfor every dollar increase in advertising expenditure when

The estimated regression equation is,Wherex1 = inventory investment ($1,000s)x2 = advertising…

Answered: A shoe store developed 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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A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y sales price (in thousands of dollars) x₁ = total floor area (in square feet) X₂= number of bedrooms x3² distance to nearest high school (in miles) The estimated model is as follows. y=121 +0.083x₁ +23x₂ - 4x3 Answer the questions below for the interpretation of the coefficient of x₁ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each 100-square-foot increase in floor area? dollars (b) Is this change an increase or a decrease? increase decrease X Ś
A real estate company wanted to see the relationship between home prices and square footage of homes for sale. The regression line is ŷ = –802,456 + 144x, where x is the square footage of a home. Complete the statement based on the information. For every -square-foot increase, the predicted home price is predicted to increase by $
Market researchers were interested in the relationship between the number of pieces in a building-brick set and the cost of the set. The researchers collected information from a survey and used it to obtain the regression equation ŷ = 0.08x + 1.20, where x represents the number of pieces in a set and ŷ is the predicted price (in dollars) of the set. What is the predicted price of a set that has 2,520 pieces? $202.80 $2,017.20 $3,024.08 $31,485
After interviewing salespersons at Harley Davidson dealerships, a researcher has created a linear regression line to explain the relationship between a Harley Davidson motorcycle's age (x) and price (y). The regression has an R² = 87.7%. Write a sentence summarizing what R² says about this regression. The age of the motorcycle explains 12.3% of the variation in price. The age of the motorcycle explains 9.36% of the variation in price. The age of the motorcycle explains 87.7% of the variation in price. The price of the motorcycle explains 12.3% of the variation in age. The price of the motorcycle explains 87.7% of the variation in age.
The Terre Haute Association of Realtors conducts a study to examine the association between the size of a home (in square feet) and the price the home sells for (in dollars). a regression model is then created to predict the price based on the size of the home and is given by the following equation: Price = - 43511+86.3(Size) a) Interpret the slope and intercept of the model in context using a complete sentence. b) Predict the selling price of a home with 2000 square feet. c) Suppose R2- 9225 Interpret its meaning in context using a complete sentence. d) Would it be better for a person to experience a positive or negative residual in this context? Please explain in a complete sentence.
A small business purchases a piece of equipment for $465. After 3 years the equipment will be outdated, having no value. (a) Write a linear equation giving the value y of the equipment in terms of the time x, 0 S x < 3. y = (b) Find the value of the equipment when x = 2. %$4 (c) Estimate (to two-decimal-place accuracy) the time when the value of the equipment is $200. yr
An ice cream truck owner collects data on the number of sales made each day and the average temperature that day. He computes a regression line for predicting the number of sales based on how far the daily temperature is from freezing (0 degrees Celsius) and finds sales = 3.22 - 1.8 (degrees over 0 Celsius). Identify the "y-intercept". A. -1.8 B. 1.8 C. 3.22 D. 0
The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. 220- 210 200 190 180 170 160 150 Year 0 130- 120- 110- 100- 90 801 6 12 15 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. Retail Sales 85.851 108.426 141.781 169.256 202.297 222.266 3 6 9 12 15 Use the above scatter plot to decide whether the linear model fits the data well. The function is a good model for the data. The function is not a good model for the data Estimate the retails sales in the U. S. in 2015. billions of dollars.
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 21.592 + 0.324x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 76 B 150 69 C 95 61 D 70 56 E 70 38 F 35 24 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) (c) What is the value of the sample correlation coefficient? (Round your answer to three decimal places.)

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