In a study of variables that affect productivity in the retail grocery trade, W.S. Gooduses value added per work-hour to measure the productivity of retail grocery outlets.¹He defines "value added" as "the surplus [money generated by the business] availableto pay for labor, furniture and fixtures, and equipment." Data consistent with the re-lationship between value added per work-hour y and the size x of a grocery outlet de-scribed in Good's article are shown in Table 13.2 for 10 fictitious grocery outlets.Choose a model to relate y to x.Data on Store Size and Value AddedStoreValue Added per Work-Hour, y (S)4.083.403.513.092.921.944.113.16123789456103.753.60Size of Store (thousand square feet), x21.012.025.210.430.96.819.614.525.019.1

Regression equation is used to predict the future value. It explains the relationship between the…

Answered: In a study of variables that affect…

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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If you want to use the build-down method to test a model using age (in number of years) and sex (male or female) to predict income, how large a data set would you need to collect: 8 8 8 8 20 50 40
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An important application of regression analysis is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year 2018. Month Production Volume (units) Total Costs ($) January 2018 February 2018 500 6,000 350 4,000 March 2018 450 5,000 April 2018 550 5,400 May 2018 600 5,900 June 2018 400 4,000 July 2018 August 2018 September 2018 400 4,200 350 3,900 400 4,300 October 2018 600 6,000 November 2018 700 6,400 December 2018 750 7,000
Pesticide danger: One of the factors that determines the degree of risk a pesticide poses to human health is the rate at which a pesticide is absorbed into skin after contact. An important question is whether the amount in the skin continues to increase with the length of the contact, or whether it increases for only a short time before leveling off. To investigate this, measured amounts of a certain pesticide were applied to 20 samples of rat skin. Four skins were analyzed at each of the time intervals 1, 2, 4, 10, and 24 hours. The amounts of the chemical (in micrograms) that were in the skin are given in the following table.
Machine hours and electricity costs for AMK Industries for the past year were as follows: Month January February March April May June July August Machine hours 72 73 77 69 66 71 80 63 Electricity costs $750 $750 $790 $730 $690 $720 $800 $730 Required: Using regression analysis determine the cost function for AMK Industries
what do part b and part f say? I'm unsure what all the words say.
Consider the following simple linear regression model between the dependent variable, SALES (reflecting monthly sales, in thousands of dollars) and independent variable, ADVERTISING (representing the monthly advertising expenditure, in thousands of dollars): Expected (SALES) = 4.2 - 2.6 ADVERTISING. The value 2.6 in the above regression model represents: A. the value of SALES, on average, when ADVERTISING is zero. B. the change in SALES following a unit decrease in ADVERTISING. C. a ratio of SALES over ADVERTISING as a percentage. D. the proportion of the variation in SALES explained by using ADVERTISING in the regression equation. E. a ratio of ADVERTISING over SALES as a percentage.
Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPI) and the cost of a slice of tuna. We would like to build a model using the CPI to predict the cost of a slice of pizza in a given year. Year 1960 1973 1986 1995 2002 2003 CPI (x) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of a slice of tuna(y) 0.15 0.35 1.00 1.25 1.75 2.00 Plot the data. Are we justified in creating a Linear Regression model to predict the price of a pizza slice using the CPI? Why or why not? Calculate the descriptive statistics. Calculate the sums of squares Calculate the slope, intercept, and correlation. What is this correlation value telling us? Assemble the prediction equation, and interpret the slope within the context of the problem. State the hypotheses for the hypothesis test for regression.
Income (in 1960 dollars/person) for European countries and the percent of the labor force (in %) that works in agriculture in 1960 are in the table below ('OECD economic development,' 2013). X, percent of labor in agriculture (in %) Y, income (in 1960 dollars/person) 4 1105 44 238 15 1242 23 681 27 504 14 1644 25 839 11 1361 36 529 18 1049 11 810 79 177 6 1005 20 977 42 290 20 1013 15 1035 56 324 a) State the random variables. rv X = of rv Y = of b) Make a scatterplot of X versus Y in StatCrunch (optional). Why do we wish to sketch a scatterplot? c) Find the equation of the best-fitting line (the least squares regression equation). Round values to 2 decimal places. Include the restricted domain. equation: = + * X restricted domain: % <= X <= % d) Interpret the slope from part c in the context of this problem. (Pay attention to the units) Every…
3.3 An electronics company is looking to develop a regression model to predict the number of units sold for a special running watch. Data is provided below Sales (units) Price ($) Advertising ($) Holiday 500 100 50 Yes 480 120 40 Yes 485 110 45 No 510 103 55 Yes 490 108 40 No 488 109 30 No 496 106 45 Yes (a) Determine the relationship between price and sales i.e. what is the effect of a unit change in price on sales and is it statistically significant? (b) Determine the relationship between advertising and sales i.e. what is the effect of a unit change in advertising on sales and is it statistically significant? (c) Determine the relationship between Holiday and sales i.e. what is the difference in sales between a holiday and a non-holiday and is it statistically significant? (c) Determine the relationship between Holiday and sales i.e. what is the difference in sales between a holiday and a non-holiday and is it statistically significant
The following table shows the annual expenditures, in dollars, per customer unit for residential landline phone services and cellular phone services in the United States in the given year.† Year Landline Cell 2004 592 378 2006 542 524 2008 467 643 2010 401 760 Calculate the regression line for each type of service. (Let t be the time in years since 2004, L be the operating revenue of landline phone services and C be the expenditure of cellular services. Round your regression parameters to two decimal places.) L(t) = C(t) = Determine the expenditure level at which the two lines cross. Round your answer for the expenditure level to one decimal place. million dollars
Income (in 1960 dollars/person) for European countries and the percent of the labor force (in %) that works in agriculture in 1960 are in the table below ('OECD economic development,' 2013). X, percent of labor in agriculture (in %) Y, income (in 1960 dollars/person) 15 1242 56 324 20 977 42 290 11 1361 27 504 6 1005 79 177 23 681 11 810 18 1049 14 1644 20 1013 15 1035 25 839

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