Using 1% & 5% alphas, test the overall significance of the model. ASUMMARY1 OUTPUTWN234 Multiple R5 R SquareRegression Statistics7Adjusted R6 SquareStandard ErrorObservations8910 ANOVA1112 Regression13 Residual14 Total151617 Intercept18 fixed acidity;volatile acidity1920 citric acidresidual sugar2122 chloridesfree sulfur23 dioxidetotal sulfurB24 dioxide25 density26 pH27 sulphates28 alcohol000.6004595770.3605517030.3561194840.648011208df1599сCoefficientsSSD11 375.7544028 34.159491161587 666.4107004 0.4199185261598 1042.165103StandardError21.96520845 21.1945750.024990553 0.025948502-1.083590259 0.12110128MSt StatEFP-valueF81.3479022 1.7914E-145Significance F-1.874225158 0.419283205 -4.470069717 8.37395E-060.004361333 0.002171292 2.008635263 0.044744951Lower 95%GUpper 95%HLower 95.0%|Upper 95.0%1.036359939 0.300192136 -19.60710094 63.53751785 -19.60710094 63.537517850.963082682 0.335652752 -0.025906394 0.075887499 -0.025906394 0.075887499-8.947801896 9.87236E-19 -1.321125565 -0.846054953 -1.321125565 -0.846054953-0.182563948 0.147176188 -1.240444878 0.214994246 -0.471244142 0.106116245 -0.471244142 0.1061162450.01633127 0.015002096 1.088599183 0.276495961 -0.013094741 0.04575728 -0.013094741 0.04575728-2.69663236 -1.051817956 -2.69663236 -1.0518179560.000102431 0.008620235 0.000102431 0.008620235-0.00326458 0.000728729 -4.47982984 8.00461E-06 -0.004693951 -0.001835208 -0.004693951 -0.001835208-17.88116384 21.63309988 -0.826565029 0.408607897 -60.31362221 24.55129454 -60.31362221 24.55129454-0.413653144 0.191597361 -2.158970991 0.031001886 -0.789463688 -0.0378426 -0.7894636880.916334413 0.114337465 8.014297061 2.12723E-15 0.692066057 1.140602768 0.6920660570.276197699 0.026483586 10.42901431 1.12303E-24 0.224251206 0.328144192 0.224251206-0.03784261.1406027680.328144192

There are 11 independent variables and one dependent variable. The problem is multiple regression.…

Answered: Using 1% & 5% alphas, test the overall…

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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Need help writing a null hypothesis for the bivariate relationship shown in the SPSS output.
A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?
Ride services are on the rise, such as Uber and Lyft. You wondered if the recent news reports of crime occurring with these ride services impacted the usage of them, especially with women. You decide to conduct a survey about ride service usage among male and female college students. The results showed that 19 males use ride services and 11 do not. While 12 females use ride services and 18 do not. Calculate the chi-square statistic:
Please see attached
Provide an example of a regression that arguably would have a high value of R² but would produce biased and inconsistent estimators of a causal effect. Explain why the R² is likely to be high. Explain why the OLS estimators would be biased and inconsistent.
Exercise: Suppose α = 1%. Find Z0.01 and the rejection region. Answer:
You run a regression analysis on a bivariate set of data (n = 22). With ☎ = 22.3 and y = 58.5, you obtain the regression equation y = 0.842x + 37.664 with a correlation coefficient of r = 0.897. You want to predict what value (on average) for the response variable will be obtained from a value of 180 as the explanatory variable. What is the predicted response value? y = (Report answer accurate to one decimal place.)
You run a regression analysis on a bivariate set of data (n = 120). With i = 66.3 and y = 50.6, you obtain the regression equation y = 4.097x – 11.636 with a correlation coefficient of r = 0.56. You want to predict what value (on average) for the response variable will be obtained from a value of 120 as the explanatory variable. What is the predicted response value? y = (Report answer accurate to one decimal place.)
The value of PRESS for five different regression models are A = 42.3, B = 47.4, C = 51.5, D =40.6, and E = 54.6. Based only on this statistic, which model is preferred?
In order to explain the U.S. defense budget, the following variables are considered. Data from 1962-1981 is collected. Variable Definition Y Defense budget-outlay for year t, $ billions X2 GNP for year t, $ billions X3 U.S. military sales/assistance in year t, $ billions X4 Aerospace industry sales, $ billions Dependent Variable: Y Method: Least Squares Date: 11/22/21 Time: 11:20 Sample: 1962 1981 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. X2 0.016703 0.007017 2.380261 0.0301 X3 -0.696174 0.453978 -1.533497 0.1447 X4 1.467729 0.277608 5.287047 0.0001 22.77514 3.311695 6.877186 0.0000 R-squared 0.971088 Mean dependent var 83.86000 Adjusted R-squared 0.965667 S.D. dependent var 28.97771 S.E. of regression Sum squared resid 5.369339 Akaike info criterion 6.376143 461.2769 Schwarz criterion 6.575290 Log likelihood -59.76143 Hannan-Quinn criter. 6.415019 F-statistic 179.1337 Durbin-Watson stat 0.676777 Prob(F-statistic) 0.000000
When comparing different multiple linear regression models to determine which is better for predicting a response variable, Y, why should we use the adjusted-R2 values instead of the plain R2 values?

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