By linear regression (f(r) a + bx) we have obtained from some data a 1.170476, b...
A simple linear regression (linear regression with only one predictor) analysis was carried out using a sample of 23 observations From the sample data, the following information was obtained: SST = [(y - 3)² = 220.12, SSE= L = [(yi - ġ) = 83.18, Answer the following: EEEEEEEE Complete the Analysis of VAriance (ANOVA) table below. df SS MS F Source Regression (Model) Residual Error Total Regression standard error (root MSE) = 8 = The % of variation in the...
3) Consider the following linear regression: y =a + Bx + Show that minimizing the sum of squared residuals ( - ) to obtain OLS estimators of the slope and the intercept results in the following algebraic properties a) b) Ex = 0 = 0 4) You run the following regression: TestScore = a + (Female) + where TestScore is measured on a scale from 400 to 1000, and female is an indicator for the gender of the student. You...
5. So far in our linear modeling, we have assumed that Ylz ~ NA,+Az,σ2); that is, there is a normal distribution of common variance around the regression line. Here, we change this up! Suppose that X~Unif(0, 1) and that for a given r, we know YlN(,22). (Here, the regression lne is 01z and the variance around the regression grows as r grows.) a. In R, figure out how to generate 1000 data points that follow this model and plot them....
Problem 2: Given a collection of data { zNJS R" we define 1. The sample mean of the points is given by 2. The sample variance of the points is given by N 2 3. The covariance matrix of the points is given by Suppose that (N) S R is a collection of data points. Using Lagrange Multipliers, show that the unit vector w for which the set (i.N), where wy, has maximum variance is the normalized eigenvector of Cov(ia)...
Econometrics
13) Consider the classical linear regression model y = XB + E, EN(0,021) The data are collected in such a way that the X matrix is orthogonal, that is X'X = 1. We want to test the null hypothesis that Ho: B1 + B2 + ... + Bx = 0. For this particular hypothesis, the standard t-test for a single linear restriction r' B = q reduces to ki bi a) t= i=1 b) t = svk Ek=1b c)t...
Suppose that the data (X1, Y), ... (Xn, Yn is generated by the following ("true") model: a+ bX; + сX; +ei, where a, b, c are some parameters and ei are independent errors with zero mean and variance a2. Suppose that we fit the simple linear regression model to the data (i.e. we ignore the quadratic term cX2) using the OLS method. Find the expectation of the residual from the fit.
Suppose that the data (X1, Y), ... (Xn, Yn...
Simple Linear Regression Problem
Simple Linear Regression
Problem
QUESTION 4 SUMMARY OUTPUT Regression Statistics Multiple R Squared Adjusted Rsq Standard Error Observations 0.90 0.80 0.79 82.06 19.00 ANOVA MS 467247.5 6733.3 df Regression Residual Total 467247.5 114466.2 581713.7 17 Intercept Age Coefficients St Error 756.26 10.27 30.41 1.23 t Stat 24.87 -8.33 This output was obtained from data on the age of houses (in years) and the associated amount paid in rates (S). Predict the rates paid (in dollars correct...
Weighted least squares is a modification of standard regression analysis that may be used for a set of data when the assumption of variance homogeneity does not hold. (Assume the responses are independent.) If the ith response is an average of mi equally variable observations, then Var(Vi) ynx1-Xnxp ßpx1 + En x1, where E(c) _ 0, Cov(c)-σ2V, and In this case, we have the model 1112 0 The fixed and known positive definite matrix Vnxn has rank n. The weighted...
2.4 We have defined the simple linear regression model to be y =B1 + B2x+e. Suppose however that we knew, for a fact, that ßı = 0. (a) What does the linear regression model look like, algebraically, if ßı = 0? (b) What does the linear regression model look like, graphically, if ßı = 0? (c) If Bi=0 the least squares "sum of squares" function becomes S(R2) = Gyi - B2x;)?. Using the data, x 1 2 3 4 5...
2. (a) We want to find the root x of the function f(x); that is, we need f(r) = 0 . This can be done using Newton's method, making use of the iterative formula f(xn) Show that the sequence ofiterates (%) converges quadratically if f'(x) 0 in some appropriate interval of x-values near the root χ 9 point b) We can get Newton's method to find the k-th root of some number a by making it solve the non-linear cquation...