I believe that cars with more mileage are worth less money. I decide to run a statistical analysis at the a = 0.01 significance level to testmy claim. From a large website specializing in selling used cars, I sample 6 used 2012 sedans at random. For each sedan, I recordIts mileage and the price that the car is listed at Data are as follows:Mileage14572083100744305043036500108940Price391011550101009170153409070Assume that responses follow the modelPrice = Bo - BiIMileage) + EWhere the error terms e satisfy the standard regression assumptionsCompute the correlation between price and mileage. (Round answers to 4 decimal points)a.Correlation: b.Which of the following is NOT an assumption made on the standard regression assumptions?O They all have the same standard deviationO They all have mean (expected value) OO They are all independent of each otherO They all follow a binomial distributionWhich of the following is true about the regression line?O The regression line minimizes the MSE of forecast errorsO The regression line is the line with the largest prediction accuracy.O The regression line minimizes the sum of the squares of the residuals.O Regression may only be performed if the standard regression assumptions hold.O All of the above.d.Is the slope of the regression line positive or negative?O PositiveO NegativeO NeitherO There is not enough information to tell.е.What is the null hypothesis for your hypothesis test?O Ho: Bo>0O Ho: Bo <0O Ho: Bo = 0O Ho: Bo #0O Ho: B1>0O Ho: B1<0O Ho: B1= 0O Ho: B1#0

Answered: Image /qna-images/answer/a42367ab-61c4-4716-9873-c60fb1af0eac.jpg

Answered: O They all have the same standard…

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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True/false (please explain). Suppose that a regression of Y on X₁ is unbiased, and the true slope coefficient is 2. Another variable X2 is correlated with Y, but it is uncorrelated with X₁. In expectation, the default t-statistic on B₁ will be larger in the multivariate regression that includes X₂ than in the bivariate regression that omits X₂.
(d) Predict the value of moisture content when frying time is 20, in a way that conveys information about reliability and precision. (Calculate a 95% PI. Round your answers to two decimal places.) %, (e) Analyze the residuals from fitting the simple linear regression model to the transformed data and comment. (Select all that apply.) O The patterns in the residual plot and the normal probability plot are marginally acceptable. O There are four positive standardized residuals. O The patterns in the residuals plot and the normal probability plot are unacceptable. O Looking at the residual versus fits, one standardized residual is a bit large. O Looking at the residual versus fits, no standardized residuals look large. O There are only two positive standardized residuals, but two others are essentially 0.
Chapter 9, Section 2, Exercise 030 Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 304.5 304.5 2.01 0.158 Residual Error 174 26361.0 151.5 Total 175 26665.5 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is=The p-value is=
Data on 12 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. ski 85 2.5. treadmill = Coefficients Estimate (Intercept) Treadmill 85 - 2.5 Std. Error What is the test statistic? -2.5 0.45 1 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? Round your answers to three decimal places. Using your answer from the previous question, find the p-value. Part 2 o
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Jadual 2 : Ringkasan analisis korelasi kajian kemurungan dan kebimbangan Table 2 : The summary of correlational analysis of depression and anxiety . Correlations Depression Anxiety Pearson Correlation Depression Anxiety Sig (2-tailed) Depression Anxiety N Depression Anxiety 1.00 .282 - .003 112 112 .282 1.00 .003 - 112 112 Based on this position, write the likely regression equation. Write down statistics representation of the correlation in APA format.
Sarah has some data and wants to run a linear regression model on it. Before she runs the test, she knows she needs to check to make sure all conditions are met. Based only on the plots below, what condition is not met? Data Scatterplot Normal Probability Plot Residual Scatterplot 25 100 Regression Standardoed Predcted Vale Observed Cum Prob Linearity O Normality Equal Variances O Independence O The plots do not show a problem with any of the listed conditions.
What is the slope of the regression line (slope and intercept) Please put the answer in bold.
Match the regression (linear model) assumptions with the corresponding graphical procedure for testing that assumption. ( Choose ] Std. residuals vs fitted values plot Normality v QQ plot Time Series plot (of responses or residuals) Independence Std. residuals vs fitted values Homogeneity of variance Time Series plot (of responses v Model fit Std. residuals vs fitted values v
Public health researchers would like to evaluate whether information on age (in years) and weight (in kg) could be used to predict shoe size of students. The output of the linear regression analysis is given below. 95% confidence interval Unstandardized Beta t Lower bound Upper bound (Constant) 6.493 1.121 -5.191 18.177 Age (years) -0.462 -1.617 -1.037 0.114 Weight (kg) 0.155 7.568 0.114 0.197 Dependent variable: shoe size A) Write null hypotheses for the linear regression analysis. B) Interpret the results of the linear regression in not more than 100 words. C) Write a complete equation for the results of the linear regression analysis
A researcher is interested in examining the relationship between spousal abuse and child abuse. Specifically, they are interested in determining whether there is a predictive relationship between spousal abuse and child abuse in 5 county social services offices. Calculate the linear regression line for the following data. Note you have already calculated the first step to this analysis (Pearson's Correlation)
Which of the following are true? (Select all that apply.) 1 An assumption of a linear regression is a consistent dispersion of the observations from the regression line. 2 An assumption of a linear regression is that the residuals form a normal distribution. 3 Omitted variable bias is when you don’t include the dependent variable.

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