Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephonehandset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is thetotal cost of the lot (in $100's). Regression analysis of the data yielded the following tables.InterceptSourceCoefficientsTotaldf113.996SSFRegression 1 9.858769 9.858769 12.22345Residual8.872 0.8065450.358Save for LaterStandard ErrorUsing a=0.05, Louis should12 18.73077O suspend judgmentO not reject Ho: Po=0O reject Ho: 1₁=0O increase the sample sizeO not reject Ho: 1-0p-value1.161268 3.441065 0.0048850.102397 3.496205 0.004413t StatisticMSSe = 0.898r² = 0.526341

In this case, the independent variable is the production lot size (x) and the dependent variable is…

Answered: Louis Katz, a cost accountant at…

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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The following multiple linear regression depicts the sales of Unilever Corporation: S = 10- 0.20P + 0.06TA + 0.08RA Where:S: Sales (Unit);P: Price ($/Unit);TA: Television advertising ($)RA: Radio advertising ($). 1. Interpret the slopes;2. Is television advertising more effective than radio advertising? Why or why not?3. What is the effect of a $1 increase in price, a $3 increase in TV advertising, and a $2decrease in radio advertising on sales?4. What is the effect of a $2 decrease in price, a $2 decrease in TV advertising, and a $4increase in radio advertising on sales?
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Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of company speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow: YEAR 1 2 3 4 5 6 7 B 9 10 11 12 UNIT SALES (THOUSANDS) 398 698 898 y bar 1,304 1,163 1,195 898 1,200 982 1,235 875 811 PRICE $ PER UNIT 283 209 213 220 209 200 220 209 230 213 216 243 ADVERTISING ($000) 621 821 1,200 1,405 1,210 1,304 875 1,200 691 875 691 691 a. Perform a regression analysis based on these data using Excel. Note: Negative values should be indicated by a minus sign. Round your answers to 4 decimal places. Price advertising
The following data show the brand, price ($), 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 = 23.462 + 0.315x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 74 B 150 73 C 95 59 D 70 58 E 70 42 F 35 24 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST=SSR=SSE= (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line.The least squares line provided a good fit as a large proportion of…
A real estate analyst believes that the three main factors that influence an apartment's rent in a college town are the number of bedrooms, the number of bathrooms, and the apartment's square footage. For 40 apartments, she collects data on the rent (y, in $), the number of bedrooms (x1), the number of bathrooms (x2), and its square footage (X3). The following table shows a portion of the regression results. ANOVA Significance df SS MS F F gression Residual 3 5694717 1898239 50.88 4.99E-13 36 1343176 37310 Total 39 7037893 Standard Upper 95% Coefficients Error t Stat p-value_Lower 95% Intercept 300 84.0 3.57 0.0010 130.03 470.79 Bed 226 60.3 3.75 0.0006 103.45 348.17 Bath 89 55.9 1.59 0.1195 -24.24 202.77 Sqft 0.2 0.09 2.22 0.0276 0.024 0.39 What would be the rent for a 1000-square-foot apartment that has 2 bedrooms and 2 bathrooms? $840 $1,335 $1,130 $1,260
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.8 8.2 6.8 3.5 2.8 2.8 2.3 0.5 y 13.9 10.8 10.1 7.2 6.5 6.5 6.3 4.8 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.81x + 4.35. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 7.8 thousand automatic weapons? Answer = Answer = Round to 3 decimal places.
A. Write the equation of the regression line. B. Interpret each one of the slopes in this context. C. Calculate the estimated number of days absent and the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school.
We want to predict the selling price of a house in Newburg Park, Florida, based on the distance the house lies from the beach. Suppose that we're given the data in the table below. These data detail the distance from the beach (x, in miles) and the selling price (y, in thousands of dollars) for each of a sample of sixteen homes sold in Newburg Park in the past year. The data are plotted in the scatter plot in Figure 1. Also given is the product of the distance from the beach and the house price for each of the sixteen houses. (These products, written in the column labelled "xy", may aid in calculations.) Distance from the beach, x (in miles) 7.7 18.5 6.3 11.9 11.0 14.6 11.3 3.0 Selling price, y (in thousands of dollars) 290.8 215.0 304.4 269.9 283.2 203.4 212.4 263.2 197.3 272.2 215.1 241.6 312.3 221.5 279.1 236.1 10.9 4.9 12.5 5.8 3.6 5.7 9.4 8.1 Send data to calculator xy 2239.16 3977.5 1917.72 3211.81 3115.2 2969.64 2400.12 789.6 2150.57 1333.78 2688.75 1401.28 1124.28 1262.55…
Please provide a detailed explanation for the questions attached.
Please answer parts d, e and f.
Please help me better understand this problem. Researchers want to determine if heavier cars use more gasoline? To answer this question, a researcher randomly selected 15 cars. He collected their weight (in hundreds of pounds) and the mileage (MPG) for each car. From a scatterplot made with the data, a linear model seemed appropriate and the following output was obtained in photo below. Question: Determine the proportion of the variation in mileage is accounted for by the linear relationship with the weight of the car and also determine the estimate of the regression standard deviation.
The following data show the brand, price (S), 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 ý = 22.522 + 0.335x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 78 B 150 73 95 63 70 58 E 70 38 35 26 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination rt. (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a small proportion of the variability in y…

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