What percentage of the variability in the number of delivery days for a car is NOT explained byknowing the number of factory options?SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsInterceptFactory Options22.31%72.16%88.14%77.69%O 4.64%0.88140.77690.72164.640010Coefficients29.733333.284848Standard Error2.9914355870.482113498

Solution From the data we finds R^2 = 0.7769

Answered: What percentage of the variability in…

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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Extending the Concepts and SkillsMargin of Error in Regression. In Exercises and, you will examine the magnitude of the margin of error of confidence intervals and prediction intervals in regression as a function of how far the specified value of the predictor variable is from the mean of the observed values of the predictor variable.Age and Price of Orions. Refer to the data on age and price of a sample of 11 Orions given in Table on page 679.a. For each age between 2 and 7 years, obtain a 95% confidence interval for the mean price of all Orions of that age. Plot the confidence intervals against age and discuss your results.b. Determine the margin of error for each confidence interval that you obtained in part (a). Plot the margins of error against age and discuss your results.c. Repeat parts (a) and (b) for prediction intervals.TABLE Age and price data for a sample of 11 Orions Age(yr) x Price($100) y 5 85 4 103 6 70 5 82 5 89 5 98 6 66 6 95 2 169 7 70 7 48
Use your knowledge about ANOVA tables and adjusted R-squared values to answer this question: As the sample size goes up, the average amount of variability explained (non-adjusted R-squared value) by a random (zero true predictive power) explanatory variable in regression: Group of answer choices goes down stays the same goes up
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What should be used as Statistical Test to analyze data? Choices: Pearson's R, Spearman's rho, ANNOVA, Multipple regression, T-tests
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Table Physical Health Complaints and Burnout among Social Workers: One-Way Analysis of Variance Results Physical Health Complaints Low Burnout (n = 92) Middle Burnout (n = 93) High Burnout (n = 91) F(dfs) Respiratory infections M SD 5.7 2.3 7.0 3.0 8.8 3.7 23.8(2,273)* Overall Physical Health M SD 31.7 9.4 39.7 10.6 47.8 14.1 44.1(2,272)* * p < .05 Written Assignment Based on the information provided in the article excerpts and table, write a paragraph or two that describes and interprets the findings related to the one-way ANOVA presented in the table. Be sure to include sample size, relevant descriptive statistics, patterns in the data and an interpretation of the findings presented with supportive statistical information included. You do not need to talk about every number in the table, but you do need to include relevant statistical information to support your statements.
Estimating Length Given below is a Minitab display resulting from two-way analysis of variance with sample data consisting of 18 different student visual estimates of the length of a classroom. The values are categorized according to sex and major (math, business, liberal arts). What do you conclude about an interaction between sex and major? Minitab
Fill in the blanks.a. Positive correlations between observations of the response variable result in an underestimate of_______b. Data collected from a designed experiment is generally preferable to data obtained from_______c. Adata point whose removal has a substantial effect on one or more sample regression coefficients is called an_______
Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.9086 A 0.8181 398.0910 Observations ANOVA df SS MS Significance F Regression 2 1.50876E-18 Residual C 7448393.148 F Total 49 42699148.82 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household Size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this output b. Estimate the annual credit card charges for a three-person household with an annual income of $40,000. C. Did the estimated regression equation provide a good fit to the data? Explain
I need help with this question 1. What type of analysis is this ( give the most descriptive name possible)?

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