Zecco.com2.8At the 0.05 level of significance, test whether speed of execution and overall satisfaction are related. Show the ANOVA table. What is your conSourceMean SquareFp-valueSum of Squares Degreesof Variation (to 4 decimals) of Freedom (to 4 decimals) (to 2 decimals) (to 4 decimals)RegressionErrorTotal2.4 In the table below ratings data on the quality of the speed of execution and y = overall satisfaction with electronic trades provided the estimatedregression equation = 0.2140 +0.9099z (AAII website).BrokerageScottrade, Inc.Charles SchwabFidelity Brokerage ServicesSourceTD AmeritradeE*Trade FinancialVanguard Brokerage ServicesUSAA Brokerage ServicesSpeed Satisfaction3.83.83.13,73.92.82.93.32.72.63,6ThinkorswimWells Fargo Investments3.6Interactive Brokers3.53.2Zecco.com2.82.4At the 0.05 level of significance, test whether speed of execution and overall satisfaction are related. Show the ANOVA table. What is your conclusion?Sum of Squares DegreesMean Squarep-value3.72.63.03.83.1

Speed (x)Satistifaction (y)3.83.83.12.63.73.63.93.72.82.62.933.33.82.73.13.63.43.53.22.82.4

Answered: In the table below ratings data on the…

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. Develop an estimated regression equation that can be used to predict annual sales given the years of experience. Use the excel regression tool. b. Use the estimated regression equation to predict annual sales for a salesperson with 9 years of experience. Provide an approximate 95% confidence interval.
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).
Plzzz explain
The Equal Opportunity Commission is investigating questions around unequal pay rates and discriminatory remuneration in various industries. The Pay Equity tab in the excel workbook contains information on 100 employees from a particular industry. Information includes: Salary ($) Gender Years of Education Years of Experience Division of employment Age Run a multiple regression analysis looking at the relationship between salary and years of education and years of experience. What proportion of variation in salary can be explained by these two variables? Conduct a test of the overall significance of the model. Test both the Education and Experience variables separately. Do both contribute to explaining the variation in salaries? Write out the estimated equation and interpret all coefficients. Salary (in $) Education (years) Experience (years) 20860 11 4 30200 16 1 31240 12 1 36860 12 8 44760 14 4 46690 19 3 47400 15 1 47880 14 3 50620 13 6 50690 14 4…
You want to check if there's a linear relationship between Washington State's minimum wage and the median rental price in Wenatchee. Here is the data from 2005-2015. Complete a hypothesis test on the line of best fit to determine if there is a significant relationship. You can use either a t-test or a test the critical rho value. Include your scatter plot with the line of best fit graphed. a = 0.05 Min wage, x Rent, y 9.47 9.32 9.19 9.04 8.67 8.55 8.55 8.07 7.93 7.63 7.35 1080 1027 982 957 947 956 979 976 904 902 873
Applying the Concepts and SkillsIn each of Exercises,a. find the regression equation for the data points.b. graph the regression equation and the data points.c. describe the apparent relationship between the two variables under consideration.d. interpret the slope of the regression line.e. identify the predictor and response variables.f. identify outliers and potential influential observations.g. predict the values of the response variable for the specified values of the predictor variable, and interpret your results.Plant Emissions. Plants emit gases that trigger the ripening of fruit, attract pollinators, and cue other physiological responses. N. Agelopolous et al. examined factors that affect the emission of volatile compounds by the potato plant Solanum tuberosum and published their findings in the paper “Factors Affecting Volatile Emissions of Intact Potato Plants, Solanum tuberosum: Variability of Quantities and Stability of Ratios” (Journal of Chemical Ecology, Vol. 26, No. 2,…
Use the Financial database from “Excel Databases.xls” on Blackboard. Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company. Use Excel to perform a forward selection regression analysis. Assume a 5% level of significance. Based on your final model, what is the p-value from the test of the overall model? Write your answer as a number and round to 3 decimal places. Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287 32489 16.7 2.76 3.1 1.4 16.1 Arco Chemical 2 3995 4116 6.2 1.14 6.1 2.8 40.4…
** if there is not enough evidence to fill in the formula please clarify.
You do a survey where you ask people both their age and their income. You want to see if there is a relationship between these two variables, and you want to create an equation you can use to predict someone’s age from his/her income. What kind of hypothesis test or analysis should you do?a. One-Factor, Independent-Measures ANOVAb. Two-Factor ANOVAc. Correlation/Regressiond. Chi-Square Goodness of Fit
When discussing regression analysis for cost estimation the y-variable is referred to as the O explanatory variable dependent variable. O independent variable. O predictor variable.
The scatterplot below shows the relationship between poverty rate in the 51 states in the US (including DC) and high school graduation rate. The linear for predicting poverty is as follows: poverty = 64.68 -0.62 HS-grad-rate High school graduation rate for North Carolina is 81.4% and the poverty rate is 13.1%. What is the residual for this observation? Choose the closest answer. % in poverty 18 16- 14 12 10 8 6 1.1 O-24.8 24.8 -1.1 80 85 % HS grad 90
Chap 14.5 Q4

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