## ## Call: ## lm(formula = Ought_Score ~ Inherence_Bias + Ought_Score + educ + ## RavensProgressiveMatrix_sum + conserv + Belief_in_Just_World, ## data = data_reg_key_vars) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.51857 -0.63014 -0.01277 0.59971 2.24534 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.652e-16 8.708e-02 0.000 1.00000 ## Inherence_Bias 3.063e-01 9.366e-02 3.270 0.00142 ** ## educ -7.198e-02 8.994e-02 -0.800 0.42518 ## RavensProgressiveMatrix_sum -2.231e-02 9.125e-02 -0.245 0.80724 ## conserv 1.023e-01 8.993e-02 1.137 0.25773 ## Belief_in_Just_World -8.753e-02 9.462e-02 -0.925 0.35685 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.9619 on 116 degrees of freedom ## Multiple R-squared: 0.113, Adjusted R-squared: 0.07481 ## F-statistic: 2.957 on 5 and 116 DF, p-value: 0.01504
What is the dependent variable in this analysis? What are the independent variables in this analysis?...
Please show work We fit a GARCH (1, 1) model and display the MLE of the fitted model belovw > summary(dax.garch) Call: garch(x- dax) Model: GARCHC1,1) Residuals: 1Q Median Max 12.18398 -0.47968 0.04949 0.65746 4.48048 Min 3Q Coefficient(s): Estimate Std. Error t value Pr>Itl) a0 4.639e-06 7.560e-07 6.137 8.42e-10** a1 6.833e-02 1.125e-02 b1 8.891e-01 1.652e-02 53.817 <2e-16* 1.25e-09 Signif. codes: 0 '***' 0.001 0.010.05 '.' 0.1 ''1 What is the t-value for a1? We fit a GARCH (1, 1) model...
UESTION 7 Fuel efficiency in auto-mobiles can be influences by a number of characteristics. See the linear regression output below and answer the following questions Results of linear regression analysis are shown below: Call: lm (formula = mpg ~ ., data = auto-mpg) Residuals: Min 1Q Median 3Q Max -8.6927-2.3864 -0.0801 2.0291 14.3607 Coefficients: Estimate Std. Error t value Pr>Itl) (Intercept) -1.454e+01 4.764e+00 -3.051 0.00244* cyl disp hp gvw accel year -3.299e-01 3.321e-01 -0.993 0.32122 7.678e-03 7.358e-03 1.044 0.29733 -3.914e-04...
2.-Interpret the following regression model Call: lm(formula = Sale.Price ~ Lot.Size + Square.Feet + Num.Baths + API.2011 + dis_coast + I(dis_fwy * dis_down * dis_coast) + Pool, data = Training) Residuals: Min 1Q Median 3Q Max -920838 -84637 -19943 68311 745239 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.375e+05 7.138e+04 -10.332 < 2e-16 *** Lot.Size -5.217e-01 1.139e-01 -4.581 5.34e-06 *** Square.Feet 1.124e+02 1.086e+01 10.349 < 2e-16 *** Num.Baths 3.063e+04 9.635e+03 3.179 0.00153 ** API.2011 1.246e+03 8.650e+01 14.405 < 2e-16...
Consider the dataset in the proj2-3.txt file on BlackBoard. In this problem, focus is on high systolic blood pressure (sbp) and possible explanatory variables Body Mass Index (bmi), and scale (scl). Consider the linear regression model with response high SBP and scale as explana- tory variables. Explain the coefficients in the model? Explain the null hypotheses that the estimated slope equals 0? Write a summary of your findings. What is your conclusion? Residuals: Min 1Q Median 3Q Max -72.64 -27.55...
Q) The CO2 dataset in R has data on plants from Quebec and Mississippi (denoted by the variable name ‘Type’) that were subjected to two different treatments (denoted by the variable name “Treatment”), chilled or nonchilled. I ran two regression models to see what variables best describe CO2 uptake of plants, given different conditions, with the output below: What are the regression equations for models 1 and 2? What kind of variable is “Treatment”? What does the sign of the...
The data shown below for the dependent variable, y, and the independent variable, x, have been collected using simple random sampling. X 10 15 11 19 18 17 5 17 18 y 9070 30 8020 30 5060 40 40 a. Develop a simple linear regression equation for these data. b. Calculate the sum of squared residuals, the total sum of squares, and the coefficient of determination c. Calculate the standard error of the estimate. d. Calculate the standard error for...
The R code will help to answer the question. 8. DeGroot&Shervish (2002) consider an experiment to study the combined effects of taking a stimulant and a tranquilizer. In this experiment three types of stimulant and four types of tranquilizer are administered to a group of rabbits. Each rabbit received one of the stimulants, then 20 minutes later, one of the tranquilizers. One hour later their response time (in microseconds) to a stimulus was measured. The results were: Tranquilizer Stimulant 1...
3. [25 marks] Some female psychology students were investigating whether intelligence depends on brain size. They each took a standard test that measured verbal IQ and also underwent an MRI scan to measure their brain size. The resulting data is below, file named IQBrain.csv. IQ BrainV 132 816.932 132 951.545 90 928.799 136 991.305 90 854.258 129 833.868 120 856.472 100 878.897 71 865.363 132 852.244 112 808.02 129 790.619 86 831.772 90 798.612 83 793.549 126 866.662 126 857.782...
please answer the following using the r code provided . The data set below contains information about the gasoline mileage performance for 32 au- tomobiles. We are interested in developing a model to predict the miles per gallon () using related predictor variables. The variables in the study are Dependent variable: Miles per gallon (v) Independent variables: ri horsepower (ft-lb) ra: torque (ft-lb) r: horsepower+torque (ft-lb) rs: carburetor (barrels) (a) We first start by fitting a model using y and...
You are an analyst working on a Medical Civic Action Program (MEDCAP) mission in the Philippines. The mission is very limited in scope and the primary objective is to provide acute care for recent transplant patients who otherwise would not receive adequate care. Your job is to assess how well the patients’ kidneys are working post-operation (which determines when they can be transferred out of the intensive care unit (ICU) and into rehabilitation). A measure of kidney function is creatinine...