Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data,which show the numbers of these coffees sold over six different price values:Price$2.70$3.40$1.90$4.10$3.00$4.00Using simple linear regression, and given that the price per cup is $1.80, the forecasted demand for mocha latte coffees will be cups (enter your response rounded to one decimal place).Number Sold765510980245320480

The given data set is:PriceNumber Sold$2.70765$3.40510$1.90980$4.10245$3.00320$4.00480In order to…

Answered: Café Michigan's manager, Gary Stark,…

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 candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: River Falls Hudson Ellsworth Prescott Rock Elm Stillwater Select one: a. 78.39% b. 88.54% O c. 161.39% O d. 100% City What percentage of the total variation in the candy bar sales is explained by prices? e. 48.19% Price ($) 1.30 1.60 1.80 2.00 2.40 2.90 Sales 100 90 90 40 38 32
Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
Hiroshi Sato, an owner of a sushi restaurant in San Francisco, has been following an aggressive marketing campaign to thwart the effect of rising unemployment rates on business. He used monthly data on sales ($1,000s), advertising costs ($), and the unemployment rate (%) fromJanuary 2008 to May 2009 to estimate the following sample regression equation: Sales(t) = 17.51 +0.05 Advertising Costs(t-1) – 0.70 Unemployment Rate t-1 Requirement: a. Hiroshi had budgeted $620 toward advertising costs in May 2009. Make a forecast in June2009, if the unemployment rate in May 2009 was 9.1%b. What will be the forecast if he raises his advertisement budget to $700?c. Reevaluate the above forecast if the unemployment rate in May 2009 was 9.5%. Please, if possible, can you do the answer in Excel/Spreadsheet
2. Following data is amount of Assessed Value for an office building for a given floor area, number of office space, number of entrances, and the age of the offic building. Use excel to …. Obtain a Scatter plot of Floor Area as the independent and Assessed value as the dependent variable. Note the linear regression Model Predict the assessed value for 6000 sq ft. FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433…
The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers): A.What would be the typical linear regression equation for the number of sales? B.Make the sales forecast for period 15 of next year. C. Make the sales forecast for period 17 of next year. D. What is the standard error for the data?
15. Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $115 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions %3D 39 23 117 70 70 49 122 62 64 122 20 11 149 111 18 104 68 129 116 98 95 60 113 208 40 22 288 48 8. Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = (Round to one decimal place as needed.) х. The best predicted gross for a movie with a $115 million budget is $ million. (Round to one decimal place as needed.)
$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…
Suppose a firm has had the following historic sales figures. Year: 2016 2017 2018 2019 2020 Sales $2,570,000 $3,750,000 $4,380,000 $5,010,000 $5,520,000 What would be the forecast for next year’s sales using regression to estimate a trend?
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
The registrar at State University believes that decreases in thenumber of freshman applications that have been experiencedare directly related to tuition increases. They have collectedthe following enrollment and tuition data for the past decade: a. Develop a linear regression model for these data andforecast the number of applications for State Universityif tuition increases to $10,000 per year and if tuition islowered to $7000 per year.b. Determine the strength of the linear relationship betweenfreshman applications and tuition using correlation. c. Describe the various planning decisions for State Univer-sity that would be affected by the forecast for freshman applications.
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