The coefficients a and b of the line y = ax + b for the least squares fit of the n points (xi , yi) satisfy
Exercise 1: Express the cost to compute a and b as O(f(n)), for some function f. Justify your formula for f(n)
is the expression to calculate the mean of x.
The mean is calculate by calculating the sum of all terms in the set divided by the number of elements in the set.
So, the time complexities are:
So,
Time Complexity of a =
Time Complexity of b =
4. We have n statistical units. For unit i, we have (xi; yi), for i-1,2,... ,n. We used the least squares line to obtain the estimated regression line у = bo +biz. (a) Show that the centroid (x, y) is a point on the least squares line, where x = (1/n) and у = (1/n) Σ¡ı yi. (Hint: E ) i-1 valuate the line at x = x. (b) In the suggested exercises, we showed that e,-0 and e-0, where...
4. We have n statistical units. For unit i, we have (x; yi), for i 1,2,...,n. We used the least squares line to obtain the estimated regression line bobi . (a) Show that the centroid (z, y) is a point on the least squares line, where x-(1/n) Σ-Χί and у-(1/ n) Σ|-1 yi. (Hint: Evaluate the line at x x.) (b) In the suggested exercises, we showed that e,-0 and where e is the ith residual, that is e -y...
Projections and Least Squares 3. Consider the points P (0,0), (1,8),(2,8),(3,20)) in R2, For each of the given function types f(x) below, . Find values for A, B, C that give the least squares fit to the set of points P . Graph your solution along with P (feel free to graph all functions on the same graph). . Compute sum of squares error ((O) -0)2((1) 8)2 (f(2) -8)2+ (f(3) - 20)2 for the least squares fit you found (a)...
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the best linear least-squares fit to the following data. x2.4 3.6 3.64 4.7 5.3 y| 33.8 34.7 35.5 36.0 37.5 38.1 Plot both the linear function and the data points on the same axis system Solution We can solve the problem with the following MATLAB commands x[2.4;3.6; 3.6;4.1;4.7;5.3]; y-L33.8;34.7;35.5;36.0;37.5;38.1 X [ones ( size (x)),x); % build the matrix X for linear model %...
Question 4. Least squares solution [6 marks] The ordinary least squares estimate for the slope in simple linear regression gives the following: B = (2=1 Xiyi) – nzy (2=127) - na Show that this is the same as Bi 2=1(ki – 7)(yi — ) i=1(xi – T)2 in where n n 1 = - n Xi, y= Yi n i=1 i=1
Question 4) Suppose that the (univariate) variable y is known to be a quadratic function of the variable x; that is, y = a x2 +bx+c, where the coefficients a, b, c are obtained by conducting an experiment in which values y1, .. , Yn of the variable y are measured for corresponding values 21,.. , Un of the variable x. Find the best least-squares fit of the quadratic polynomial using the data: {(-2,-5),(-1, -1),(0,4), (1,7), (2,6), (3,5), (4, -1)}....
8. (16 points) Suppose you use a quadratic curve y = ax? +b to fit the three (x,y) points (1,3), (0,-1), (-1,1). Use matrix method as described in class to find the least squares estimate of the constants a and b in the above equation. In particular, formulate the relevant normal equation, whose solution leads to the least squares estimates of a and b, and hence obtain the least squares estimate of a and b.
Fitting a Line to Data The method of least squares is a standard approach to the approximate solution of overdeter- mined systems, i.e., sets of equations in which there are more equations than unknowns. The term "least squares" means that the overall solution minimizes the sum of the squares of the errors made in the results of every single equation. In this worksheet you will derive the general for- mula for the slope and y-intercept of a least squares line....
For A and B, a least-squares solution of Ax-b is x. Compute the least-squares error associated with this solution 113 For A= 1 -1 and b= 13 , a least-squares solution of Ax=b is . Compute the least-squares error associated with this solution. The least-squares error is . (Simplify your answer. Type an exact answer, using radicals as needed.)
2. Suppose Y ~ Exp(a), which has pdf f(y)-1 exp(-y/a). (a) Use the following R code to generate data from the model Yi ~ Exp(0.05/Xi), and provide the scatterplot of Y against X set.seed(123) n <- 500 <-rnorm (n, x 3, 1) Y <- rexp(n, X) (b) Fit the model Yi-Ao + Ax, + ε¡ using the lm function in R and provide a plot of the best fit line on the scatterplot of Y vs X, and the residual...