If you use α = .001, using p-value for X1, is the coefficient of X1 is significantly different from 0. Yes No Regression StatisticsMultiple R0.9101558R Square0.8283837Adjusted RSquare0.7971807Standard Error1.6929902Observations14ANOVAdfSSMSFSignificanceFRegression2152.185976.0929626.548236.167E-05Residual1131.528372.866216Total13183.7143Coefficientst StatP-Lower 95% UpperStandardvalue95%ErrorIntercept87.8823673.06924128.633261.11E-1181.127012794.63772X10.37987660.0901314.2147020.0014490.181498950.578254X20.22160510.0384425.764660.0001260.136994830.306215

Introduction: It is required to determine if the coefficient of the predictor X1 is significantly…

Answered: If you use α = .001, using p-value…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 5PPS

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Which of the following is FALSE? * The regression model assumes the errors (residuals) are normally distributed. Data point below the regression line, the residual is negative. The errors (residuals) in a regression model are assumed to have a zero mean. The errors (residuals) in a regression model are assumed to have increasing mean. In regression analysis, the regression line Minimize the sum of distances between the actual value x and the predicted x values. Minimize the perpendicular distance between the regression line and each data point. Minimize the sum of the squared residuals between the actual x and the predicted x values. Minimize the sum of the squared residuals between the actual y and the predicted y values. The smaller the distance between observed data points and the regression line, the smaller the sum of squares regression (SSR). TRUE FALSE
Which of the following is FALSE? * The residuals in a regression model are assumed to have a zero mean. Data point below the regression line, the residual is negative. The residuals in a regression model are assumed to have increasing mean. The regression model assumes the residuals are normally distributed.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y) The results of the regression were: учах+b a-1.25 b-35.644 r2 .541696 r=-0.736 Use this to predict the number of situps a person who watches 10 hours of TV can do (to one decimal place)
Why this type of plot is used in analysing regression models
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7193267530.719326753 R Square 0.5174309770.517430977 Adjusted R Square 0.4991055710.499105571 Standard Error 8.6995238488.699523848 Observations 165165 ANOVA dfdf SSSS MSMS F� Significance F� Regression 66 12,821.56512,821.565 2136.9282136.928 28.235728.2357 8.9037E-238.9037E-23 Residual 158158 11,957.71111,957.711 75.68275.682 Total 164164 24,779.27624,779.276 Step 1 of 2 : How many independent variables are included in the regression model
Write a report on your findings. Predictor Constant X_MoneySupply Y_lagIncome S = 2328 Coef 12,718 0.4405 0.7805 R-Sq= 99.4 % Use the report to estimate the model. SE Coef 5392 0.3393 0.1616 T 2.36 1.30 4.83 R-Sq(adj) = 99.4 % P 0.031 0.213 0.000 Y₁ = + x₁ +t-1 Yt (Round the constant to the nearest integer as needed. Round the coefficients to four decimal places as needed.)
Write the estimated regression equation from the coefficient output and interpret the results.
We wish to determine a regression equation that relates the hourly wage in pesos to age (in years), gender and education (in years). Below is portion of the regression analysis derived using a software. *Note: under the gender variable: male and female categories are assigned a value of 0 and 1, respectively. Degrees of freedom Coefficients Standard Error Regression 5270141 3 Intercept -313.958 102.3523 Residual 6956380 121 age 8.527 1.8308 Total 12226521 124 education 46.3419 6.4502 gender -98.0447 43.1113 a) Provide the F-statistics b) Provide the F-critical value c) Are the variables jointly statistically significant? Justify your answer.
A regression was run to determine if there is a relationship between hours of study per week (z) and the test scores (y)- The results of the regression were: y=ax+b a=5.701 b=31.89 r2=0.660969 r=0.813 Use this to predict the final exam score of a student who studies 3.5 hours per week, and please round your answer to a whole number. Question Help: Message instructor Submit Question rse here to search R
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I have no idea how to fill in the blank in this regression output... please help
Simple linear regression results: Dependent Variable: Grade (Marks)Independent Variable: AbsencesGrade (Marks) = 89.100971 - 3.1906331 AbsencesSample size: 30R (correlation coefficient) = -0.93164951R-sq = 0.86797081Estimate of error standard deviation: 3.2600128 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 89.100971 1.1464745 ≠ 0 28 77.717357 <0.0001 Slope -3.1906331 0.23516911 ≠ 0 28 -13.567399 <0.0001 Analysis of variance table for regression model: Source DF SS MS F-stat P-value Model 1 1956.2835 1956.2835 184.07431 <0.0001 Error 28 297.57514 10.627684 Total 29 2253.8587 a) Interpret the slope in terms of abscences and grade. b) Does y-intercept have meaning? If so, what is it?

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