A1A1 Summated_Rating23456789LO111213141516Ō600691718192021~ 3 4 5222324252627282955686765636052655348544974655348574454546365537269XfxBCost_($_per_person)38515957443842433331393684543837332243466360284648Summated_RatingсDEF(5)G A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost perperson. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three ratings. The magazine has compiledthe accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below.a. Construct a scatter plot. Choose the correct graph below.O A.90-0-01Cost ($)Rating90QO B.90+0-0Cost ($)30Rating90O C.A Cost ($)90-0-0Rating90b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b₁.bo =and b₁ =(Round to two decimal places as needed.)O D.90-0-0Cost ($)સ્થાની ના ૧૧માRating90

Given data: x y 55 38 68 51 67 59 65 57 63 44 60 38 52 42 65 43 53 33 48 31…

Answered: A magazine publishes restaurant ratings…

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 least squares regression model performed to predict the selling price of houses found the following equation: Pricê = 169.328+35.3Area + 0.718Lotsize - 6543Age where Price is in dollars, Area is in square feet, Lotsize is in square feet, and Age is in years. The R2 is 0.92. One of the following interpretations is correct. Which is it? Explain why. a.) Each year a house Ages, it is worth $6543 less. b.) Every extra square foot of Area is associated with an additional $35.30 in average price, for houses with a given Lotsize and Age. c.) Every dollar in price means Lotsize increases 0.718 square feet. d.) This model fits 92% of the data points exactly.
The output of a another regression analysis on the effect of radio advertising spending, television advertising spending and online advertising spending on sales is given below. For this study they explored 3 different types of advertising spending (radio, television and online). They modelled it as y = ao + a1¤1+a2x2 + a3X3. The table below gives the summary output of this regression analysis. Regression Analysis: Sales versus Spend Analysis of Variance Source DF s MS F-Value P-Value Regression d freg 23.897.37 MSR F-val p-val Error dferr 552.44 MSE Total 36 Find the F-value in the above table. Give your answer to 3 decimal places.
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $7 million. Is the result close to the actual number of viewers, 5.2 million? Use a significance level of 0.05. Salary (millions of $) 108 14 5 8 5.1 4.3 7.8 2.5 Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to three decimal places as needed.) X 1 1 5 8 5.7 8.2 10.9 4.4
5. The table below shows varying prices of music sold on a streaming service. Determine the linear regression to estimate the weekly sales of albums sold, use your regression to estimate the weekly sales if the price is $9 per album, and state the correlation coefficient, r, and interpret its meaning in the context of this problem. Price ($) Weekly sales (in thousands) $8 440 $8.50 380 $10 $11.50 250 180
How do you figure this out using conjoint analysis in a linear regression? Determine how various attributes impact the purchase of a car. There are four attributes, each with three levels: Brand: Ford = 1, Chrysler = 2, GM = 3 MPG: 15 MPG = 1, 20 MPG = 2, 25 MPG = 3 Horsepower (HP): 100 HP = 1, 150 HP = 2, 200 HP = 3 Price: $18,000 = 1, $21,000 = 2, $24,000 = 3 The nine product profiles ranked in the data file were evaluated by a consumer. (a) For this market segment, rank the product attributes from most important to least important. Below is the data provided. Brand MPG Power Price Rank 1 1 1 1 4 1 2 2 3 7 1 3 3 2 3 2 1 2 2 6 2 2 3 1 2 2 3 1 3 9 3 1 3 3 8 3 2 1 2 5 3 3 2 1 1
$339,380 $41,768 A national homebuilder builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price, lot cost, and type of home for closings during one month. Complete parts a through c. E Click the icon to view the house sales data a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "Townhouse", where it is equal to 1 when Type = "Townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sales Price = 108726 + (3.68 ) • Lot Cost + ( - 75063 )• Townhouse (Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places as needed.) b. Determine if an interaction exists between lot cost and type of home and find the best model. Use a = 0.1 as the level of significance. First determine whether an interaction exists. Select the…
Roller coasters get their speed as a result of dropping down a steep incline. The table below gives height of a drop and the speed achieved for different roller coasters around the world. Drop in Meters (m 25 30 28 58 55 40 90 100 Speed of the coaster (k.p.h) 80 90 78 93 90 93 105 128 Have your calculator find the linear regression for the roller coaster data above. Write the linear model, clearly define the variables and state correlation coefficient and clearly interpret the slope. Use the model to find the drop height that will result in a coaster speed of 150 kph. Have your calculator find the exponential regression for this data. Write the exponential model, clearly define the variables and state correlation coefficient. Use the model to find the drop height that will result in a…
The table contains price-supply data and price-demand data for corn. Find a linear regression model for the price-supply data where x is supply (in billions of bushels) and y is price (in dollars). Do the same for the price-demand data. Find the equilibrium price for corn. Price ($/bu) Supply (billion bu) Price ($/bu) Demand (billion bu) 2.14 6.41 2.06 9.96 2.25 7.38 2.15 9.42 2.34 7.51 2.26 8.33 2.41 7.78 2.32 8.08 2.46 8.13 2.36 7.86 2.57 8.35 2.48 6.96 ... Question content area right Part 1 Find a linear regression model for the price-supply data where x is supply (in billions ofbushels) and y is price (in dollars). y=enter your response here (Type an equation using x as the variable. Round to two decimal places as needed.)
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.6 8.1 7.1 3.9 2.9 2.6 2.1 0.7 13.7 10.7 10.2 7 6.5 6.5 5.7 4.7 thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the linear equation 0.83x + 4.08 a. How many murders per 100,000 residents can be expected in a state with 10.3 thousand automatic weapons? Round to 3 decimal places. murders per 100,000 residents b. How many murders per 100,000 residents can be expected in a state with 2.5 thousand automatic weapons? Round to 3 decimal places. murders per 100,000 residents
Used cars 2010 Vehix.com offered several used ToyotaCorollas for sale. The following table displays the ages ofthe cars and the advertised prices. a) Make a scatterplot for these data.b) Do you think a linear model is appropriate? Explain.c) Find the equation of the regression line. d) Check the residuals to see if the conditions for infer-ence are met. Age (yr) Price ($) Age (yr) Price ($)1 15988 6 99951 13988 6 119882 14488 7 89903 10995 8 94883 13998 8 89954 13622 9 59904 12810 10 41005 9988 12 2995
Is there a relationship between the weight and price of a mountain bike? The following data set gives the weights and prices for ten mountain bikes. Let the expanatory variable x be the weight in pounds and the response variable y be the bike's price. Weight (LB) 32 33 29 29 34 37 28 30 34 30 Price ($) 980 350 430 710 930 160 590 530 180 1090 a. Construct a scatterplot. Interpret. b. Find the regression equation. Interpret the slope in context. Does the y-intercept have contextual meaning? c. You decide to purchase a mountain bike that weighs 33 pounds. What is the predicted price for the bike? a. Which scatterplot below correctly shows the data? A. 254001200xy A coordinate system has a horizontal x-axis labeled from 25 to 40 in increments of 1 and a vertical y-axis labeled from 0 to 1200 in increments of 50. A cluster of plotted points that form a line that falls from left to…
The table contains price-supply data and price-demand data for corn. Find a linear regression model for the price-supply data where x is supply (in billions of bushels) and y is price (in dollars). Do the same for the price-demand data Find the equilibrium price for corn. Price Supply ($/bu) (billion bu) 2.14 2.24 2.38 2.43 2.45 2.54 6.29 7.33 7.69 7.83 8.12 8.23 Price ($/bu) 2.02 2.14 2.24 2.35 2.35 2.42 Demand (billion bu) 9.76 9.47 8.48 8.18 7.72 6.93 XX Find a linear regression model for the price-supply data where x is supply (in billions of bushels) and y is price (in dollars). y=0 (Type an equation using x as the variable. Round to two decimal places as needed.)

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