7.In the table below ratings data on the quality of the speed of execution and y = overall satisfaction with electronic trades provided the estimated regression equation0.1038+0.9468x (AAII website).OSpeed SatisfactionBrokerageScottrade, Inc.3.93.5●Charles Schwab3.13.2Fidelity Brokerage Services2.62.92.83.2TD AmeritradeE*Trade Financial3.63.9Vanguard Brokerage Services3.84.0USAA Brokerage Services2.62.5Thinkorswim2.52.02.52.2Wells Fargo InvestmentsInteractive BrokersZecco.com3.33.13.93.4At the 0.05 level of significance, test whether speed of execution and overall satisfaction are related. Show the ANOVA table. What is your conclusion?p-valueSourceMean SquareDegreesSum of Squaresof Variation (to 4 decimals) of Freedom(to 4 decimals) (to 2 decimals) (to 4 decimals)XXRegression.XError×TotalP-value is less than 0.01related.We canreject Ho B₁ = 0, we conclude that speed of execution and overall satisfaction areDII80FBF6F5F4F3id R::F2#69X%A&8aaF7*(F95сF10

Answered: Image /qna-images/answer/7db5f4af-3873-46ec-a157-751ced934e6c.jpg

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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Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables? (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly. (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly. (d) Suppose a second regression model (Model 2) was generated using only…
Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables? (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly. (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly. (d) Suppose a second regression model (Model 2) was generated using only…
Which statement below is correct? Select one: A. The value of a correlation between X and Y is reported by a researcher to be r = -0.5, it indicates that the X variable explains 25% of the variability in the Y variable in a univariate regression. B. QQ plot can be used to judge whether there is an outlier effect in the regression. C. Capital asset pricing theory asserts that individual stock returns are best explained by multiple factors. D. You are running a regression of Y on X, if you scale Y and X by dividing them by 100, it will have no effect on intercept and coefficient of X.
Based on the dataset provided below, which car feature is most likely to have the highest direct correlation with the car's price? Use Excel to answer this question Car Horsepower (X₁) Doors (X2) Fuel Efficiency (miles/gallon) (X3) Age (years) (X₁) Price (Y) 1 150 3 2 200 5 3 120 1 Number of Doors ($X_2$) Age of the Car ($X_4$) Fuel Efficiency ($X_3$) Horsepower ($X_1$) 4 2 4 25 20 30 $18,000 $25,000 $15,000
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LINEAR CORRELATION PROJECT / Topic: Does per capita income affect birth rates ((* for a sample size of 20, the absolute value of r should be greater than 0.45)) > Create a well-labeled scatterplot of the data. > Add a trendline and include the equation on the chart. Country Per Capita Income Average Birth Rate Afghanistan $ 530 37.9 Austria $ 51,460 9.5 Cambodia $ 1,530 23 Canada $ 46,370 10.3 Denmark $ 63,950 10.5 Ecuador $ 6,090 17.9 Ethiopia $ 850 36.5 France $ 42,450 12.2 Haiti $ 1,330 23 India $ 2,120 19 Kenya $ 1,750 23.9 Monaco $ 186,080 6.6 Nigeria $ 2,030 36.9 Peru $ 6,740 17.8 Philippines $ 3,850 23.7…
Perform correlation and linear regression analysis. 18) A songwriter wants to know if there is a correlation between the length of a number one song in recent years and the number of weeks the song was number one. The following table lists the lengths (in seconds) of randomly selected number one songs and the number of weeks the songs were number one in recent years. Use a significance level of a = 0.05. Lengt hs of numb er one 42186791932650 songs 14693308155561 (in secon ds) Numb er of weeks the song was numb 222111 12121112 er one. 5355 15 311246316
In Multivariate Correlation, explain why it is better and more advantageous to do a Multiple Regression Analysis rather than separate individual correlations and regression analyses
You are interested in whether there are gender differences in voting behaviour using the European Social Survey. Youestimate a regression model with being a woman as the single explanatory variable. Clearly write down the regression model.
You run a regression of homework grades on hours spent studying, and get the following regression equation: Grade = 94 – 6.2 * Hours_Spent_Studying + error What is your takeaway from this regression? Group of answer choices a) Studying is bad, and causes worse grades. b) Students don’t know how to study very well. c) A few outliers are skewing the results. d) Students who are less confident study more, but still do worse. There is an omitted variable skewing the results.
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).

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