2 Using regression, Strawstyle Company's accountant prepared the following computer printout to estimate overhead costs:ParameterInterceptDihR Square (R²)Standard Error (Se)ObservationsEstimate105.4515.05the slope is 15.05.the slope is significant.t for HoParameter 05.855.750.8529.0525Pr>t0.00050.0051Standard Errorof Parameter25.852.05During the last accounting period 18,000 direct labor hours were worked. The coefficient of determination in this model tells us thatthe intercept is 105.45.85% of the variation in the overhead variable is explained by direct labor hours.

Given: The output on estimation of overhead costs is provided. We need to interpret the coefficient…

Answered: Using regression, Strawstyle Company's…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 94E

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Water is being poured into a large cone shape cistern. The volume of water measured and centimeters cubed is reported at different time intervals measured in seconds. A regression analysis was completed and is displayed in the computer output. what is the equation of the lease squared regression line?
Systolic blood pressure (mm Hg), age (yrs), weight (kg), and height (cm) were measured. The results are summarized in the following table. Estimate Std. Error t value Pr(>lt|) (Intercept) 121.38821 32.46754 3.739 0.0201* 0.20645 0.0645 Age Weight Height 3.201 0.0329* 0.1495 0.12128 0.233 0.2852 -0.09856| 0.16826 -0.586 0.5895 What's the predicted Systolic blood pressure (SBP) of a person who is 50 years of age, weighs 75 kg, and has a height of 160 cm? Choose an option that is closest to your answer. Predicted SCB = 127.15 Predicted SCB = 118.22 Predicted SCB = 141.60 Predicted SCB = 95.64
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Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 Using a = 0.05, Louis should ________________.
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A photoconductor film is manufactured at a nominal thickness of 25 mils. The product engineer wishes to increase the mean speed of the film and believes that this can be achieved by reducing the thickness of the film to 20 mils. Eight samples of each film thickness are manufactured in a pilot production process, and the film speed (in microjoules per square inch) is measured. For the 25-mil film, the sample data result is.x₁ = 1.15 and S₁ = 0.11, while for the 20-mil film, the data yield 2 = 1.06 and s2 = 0.09. Note that an increase in film speed would lower the value of the observation in microjoules per square inch. Do the data support the claim that reducing the film thickness increases the mean speed of the film? Use a = 0.10 and assume that the two population variances are equal and the underlying population of film speed is normally distributed. The appropriate decision for the test is to reject the null hypothesis True False
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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.817 b=32.111 r²=0.877969 r=-0.937 Use this to predict the number of situps a person who watches 13.5 hour(s) of TV can do, and please round your answer to a whole number.
Create a residual plot and comment on the appropriateness of the model.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.953 b=25.096 r2-0.9409 r=-0.97 Use this to predict the number of situps a person who watches 13 hours of TV can do (to one decimal place)

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