Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the followinginformation (note: only a portion of the regression analysis results is presented here)SUMMARY OUTPUTRegression StatisticsMultiple R 0.998041188R Square 0.996086212Adjusted R Square 0.994781616Standard Error 917.1714273Observations 5ANOVAdf SS MS F Significance FRegression 1 642276389.7 642276389.7 763.5208906 0.000104039Residual 3 2523610.281 841203 427Total 4 644800000Coefficients Standard Errort StatP-valueIntercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876What is variable cost per machine hour (rounded to the nearest cent)?OA. $6,679.62OB. $917.17OC. $9.80OD. $.99Lower 95% Upper 95.0%9645.19851710.92509577

The regression model shows a relationship between 1 dependent variable and 1 or more independent…

Answered: Big Time Corporation wanted to…

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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3. A regression of infant mortality rates (annual deaths per thousand births) on median annual household income (data entered in thousands of dollars) is performed on data collected from 34 counties. The results are summarized below: Intercept 18.46 Slope -0.14 Sum of Mean squares df square Regression Residual 1.8 Total 3.4 (a) Fill in the blanks in the ANOVA table. (b) What is the predicted infant mortality rate in a county where the median annual household income is $40,000? (c) In words, what is the meaning of the coefficient -0.14? Do not simply say thát this is the slope or the regression coefficient; indicate what it means and how it can be interpreted.
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 1278 230.04 Coefficients Does the sign of the coefficient for the variable safety rating make sense? - 140.58 328.63 Standard Error 129.86 23.23 14.97 397.29 t Stat 9.841 P-value 0.0000 9.903 0.0000 -9.391 0.0000 0.827 0.4117 O No, because it is expected that as the safety rating increases then the cost should decrease. O Yes, because it is expected that as the safety rating increases then the cost should decrease. O No, because it is expected that as the safety rating increases then the cost should also increase. O Yes, because it is expected that as the safety rating increases then the cost should also increase. Tables G Keypad Keyboard Shortcuts
Let's say you then decide to enter the variables age and age-squared. The following are the regression results after adding the age and age-squared (age2) variables. *see image* Question: Based on the output above, is it proven that age has an influence on teaching evaluation scores?
Sky Eyes manufactures drones for security and enforcement organizations. They would like to predict production cost using a regression model. The regression model is based on historic production values. The regression statistics from Excel are provided below. Intercept Machine Hours Labor Hours Production Output Coefficients -477.7647341 44.97381045 -0.08890915 55.06503861 Standard Error 62.63384 -7.627901659 7.518943592 0.083252 -1.067948403 5.957718 9.242639029 t Stat 5.9814 P-value 1.20603E-09 1.74367E-09 0.291239564 5.77595E-12 What can be concluded based on the output of the regression statistics? (Select all that apply.) The regression equation is y 44.97 x (machine hours) - 0.0889 x (labor hours) + 55.065 x (production output). Labor hours is significant variable in the regression model. Machine hours is a significant variable in the regression model. The regression equation is y=-477.76+44.97 x (machine hours) - 0.0889x (labor hours) + 55.065x (production output).
Simple regression was employed to establish the effects of childhood exposure to lead. The effective sample size was about 122 subjects. The independent variable was the level of dentin lead (parts per million). Below are regressions using various dependent variables. (a) Calculate the t statistic for each slope, at significance level = 0.01. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Dependent Variable R2 Estimated Slope Std. Error tcalculated p-value Differ from 0? Highest grade achieved .061 −0.027 0.009 .008 (Click to select) Yes No Reading grade equivalent .121 −0.070 0.018 .000 (Click to select) Yes No Class standing .039 −0.006 0.003 .048 (Click to select) No Yes Absence from school .071 4.8 1.7 .006 (Click to select) No Yes Grammatical reasoning .051 0.159 0.062 .012 (Click to select) No…
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Describe the difference between the visually fitted and the calculated regression lines you placed on the scatterplot. Describe any discrepancy in the scatterplot of the raw data, and how it might explain the difference between your fitted lines. Does the difference between the visual fit and the regression fit reveal a flaw or a strength in the regression prediction, and why?
Please answer these questions carefully to get the correct answers for my Statistics homework problems.
What are the differences between the predicted y values and actual y values called in a regression analysis? Question 19 options: Residuals Constants Error terms Coefficients
Hypertension The investigators for a study collected standardized data on timed 24-hour urinary excretion for 10,079 men and women from 52 population samples in 32 countries. One of the goals of the study was to quantify the relationship between 24-hour urinary Na (y) and estimated 24-hour urinary Na (x) obtained from casual urine specimens at one point in time. The investigators presented a simple linear regression of y on x, separately for men and women. The regression equation for men was: Y = 1.09x - 7.11, with R² = 0.26, n = 1,369 Hint: Assume that a t distribution with > 200 df is the same as a N(0, 1) distribution. You can use the Distribution Calculators page in SALT to find critical values and/or p-values to answer parts of this question. (a) What does the R² of 0.26 mean in words? R²: = 0.26 means that about 74% of the time the estimated 24-hour urinary Na matches the 24-hour urinary Na obtain from casual urine specimens at one point in time. R² = 0.26 means that about 26% of…
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