The following regression output can be used to predict the weight of a Shetland pony based on the animal's age:Regression StatisticsMultiple R0.97224608R Square0.94526245Adjusted R Square0.9270166Standard Error14.0700917ObservationsInterceptAgeCoefficients Standard Error55.7tStat12.08558531P-value4.61142 0.019187.19771 0.005535.9 0.818914857True or false? The pony's weight is related to its age at the .01 level of significance.A TrueB) False

Step 1: State the hypothesesThe null hypothesis shall always signify that there is no difference or…

Answered: The following regression output can be…

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 manufacturing company that produces laminate for countertops is interested in studying the relationship between the number of hours of training that an employee receives and the number of defects per countertop produced. Ten employees are randomly selected. The number of hours of training each employee has received is recorded and the number of defects on the most recent countertop produced is determined. The results are as follows. Hours of Training 1 4 7 3 2 2 5 5 1 6 Defects per Countertop 5 1 0 3 5 4 1 2 8 2
7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…
I need help with this question 1. What type of analysis is this ( give the most descriptive name possible)?
Table 4.1 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.99794806 Missing 0.99513164 Standard Error 1.64839211 Observations 20 ANOVA Significance F MS F Regression Missing 10561.07486 16 43.47514498 19 10604.55 fissing 2.717197 1295.585 2.66E-19 Residual Total t Stat Coefficients 0.562 Standard Error P-Value Intercept X1 1.327 0.424 0.677 0.959 0.038 25.245 0.000 X2 1.117 0.125 8.916 0.000 X3 1.460 0.066 22185 0.000 Consider the output shown in Table 4.1. The Mean Square value is missing. What is its actual value? 3520 660 10561 31683
Does Job stress influence overall life satisfaction? A sample of 7,814 people were surveyed, and this is the result of the regression analysis: Descriptive Statistics Mean Std. Deviation N Life satisfaction 7.97 1.194 7817 Job stress 3.16 1.667 7817 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .228a .052 .052 1.162 .052 427.062 1 7815 .000 a. Predictors: (Constant), Job stress b. Dependent Variable: Life satisfaction ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 577.130 1 577.130 427.062 .000b Residual 10561.161 7815 1.351 Total 11138.291 7816 a. Dependent Variable: Life satisfaction b. Predictors: (Constant), Job stress Coefficientsa Model Unstandardized…
The ANOVA summary table to the right is for a multiple regression model with six independent variables. Complete parts (a) through (e). a. Determine the regression mean square (MSR) and the mean square error (MSE). (Round to four decimal places as needed.) (Round to four decimal places as needed.) MSR= MSE = b. Compute the overall FSTAT test statistic. FSTAT (Round to two decimal places as needed.) C Source Regression Error Total Degrees of Sum of Freedom Squares 240 190 430 6 26 32
A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 Using α = 0.01 to test the…

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