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2. Suppose we are given data on n observations (zi, y), î i, . . ....
2. Suppose we are given data on n observations (zi,Y), i = 1, , n, and...
2. Suppose we are given data on n observations (zi,Y), i = 1, , n, and we have a linear model, so that E(X) = β0+Axi. Let ßi-SXY/SXX and β0 = Y-Ax be the least-square estimates given in lecture. (a) Show that E(5xx) = ẢSXX and E(T) = β0+A2. (b) Use (a) to show that E(A) = and E(%) = A- In other words, these are unbiased estimators (c) The fitted values Y BotBr, are used as estimates of E(Y),...
2. Suppose we are given data on n observations (x,Y), i 1,... , n, and we...
2. Suppose we are given data on n observations (x,Y), i 1,... , n, and we have a linear model, = SXY/SXX and A,-ㄚ-Ax be the least-square estimates so that E(X) = β0 +ATp Let given in lecture. (a) Show that E(5xx)-A5xx and E(Y)-Ao +A2. (b) Use (a) to show that E(A)-A and E(A)-A. În other words, these are unbiased estimators (c) The fitted values Yi = ArtAz; are used as estimates of E(K), and the residuals ei = Y-...
Suppose we are given data on n observations (zi, Y4), i-1, . . . , n,...
Suppose we are given data on n observations (zi, Y4), i-1, . . . , n, and we have a linear model so that E(X)-β0+B1zi. Let A-SXY/SXX and A-Y-Aī be the least-square estimates given in lecture. (a) Show that E(Sxy)-ASxx and E(Y)-A +AF (b) Use (a) to show that E(BB and E(B)In other words, these are unbiased estimators (c) The fitted values Yi-A+Azi are used as estimates of %), and the residuals e,-x-Y; are used as surrogates for the unobservable...
2 2. Suppose we are given data on n observations (i, Y),, and we have a...
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2. Suppose we are given data on n observations (i, Y),, and we have a linear model, so that E(X)-A, + ßiri-Let呙-SXY /SXX and β') = F-β,2 be the least-square estimates given in lecture (a) Show that E(SXY)-ASXX and E (T)-A] + β,7. (b) Use (a) to show that E(角)-βι and E(A) = 3). In other words, these are unbiased estimators. (c) The fitted values Yt = Atari are used as estimates of E(A), and the residuals e.-Yi for...
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) i...
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...
2. Consider a simple linear regression i ion model for a response variable Y, a single...
2. Consider a simple linear regression i ion model for a response variable Y, a single predictor variable ,i1.., n, and having Gaussian (i.e. normally distributed) errors: This model is often called "regression through the origin" since E(X) = 0 if xi = 0 (a) Write down the likelihood function for the parameters β and σ2 (b) Find the MLEs for β and σ2, explicitly showing that they are unique maximizers of the likelihood function Hint: The function g(x)log(x) +1-x...
3. Consider the linear model: Yİ , n where E(Ei)-0. Further α +Ari + Ei for...
3. Consider the linear model: Yİ , n where E(Ei)-0. Further α +Ari + Ei for i 1, assume that Σ.r.-0 and Σ r-n. (a) Show that the least square estimates (LSEs) of α and ß are given by à--Ỹ and (b) Show that the LSEs in (a) are unbiased. (c) Assume that E(e-σ2 Yi and E(49)-0 for all i where σ2 > 0. Show that V(β)--and (d) Use (b) and (c) above to show that the LSEs are consistent...
2) Suppose you have multiple regression set up Ynxi XnxpBpxi Sxl and f ~ N(0nx1, σ21.). P Po X(X,...
linear statistics modeling and regression
2) Suppose you have multiple regression set up Ynxi XnxpBpxi Sxl and f ~ N(0nx1, σ21.). P Po X(X,X)-X, be the projection matrix on the column space of X. a) Show residual vector, e = (1,-P)Y. Here e is the vector of residuals ei S. b) Show that the variance of e, is 1 - Pi, where P is the i, j th entry of the matrix P c) Show that the sample covariance of...
2. R programming 2·The data set prostate in the faraway package is froma study on 97...
2. R programming
2·The data set prostate in the faraway package is froma study on 97 men with prostate cancer who were due to receive a radical prostatectomy We are interest is in predicting lpsa (log prostate specific antigen) with Icavol (log cancer volume). (a) Draw a scatterplot -does a simple linear regression model seem reasonable? (b) Without using the R function Im), compute the values T,Y, Sxx, Syy and Sxy. Com- pute the ordinary least squares estimates of the...
2. The data set prostate in the faraway package is from a study on 97 men...
2. The data set prostate in the faraway package is from a study on 97 men with prostate cancer who were due to receive a radical prostatectomy. We are interest is in predicting lpsa (log prostate specific antigen) with lcavol (log cancer volume). (a) Draw a scatterplot - does a simple linear regression model seem reasonable? (b) Without using the R function Im(0, compute the values , Y,Sxx, Syy and Sxy. Com pute the ordinary least squares estimates of the...
2. Suppose we are given data on n observations (zi,Y), i = 1, , n, and we have a linear model, so that E(X) = β0+Axi. Let ßi-SXY/SXX and β0 = Y-Ax be the least-square estimates given in lecture. (a) Show that E(5xx) = ẢSXX and E(T) = β0+A2. (b) Use (a) to show that E(A) = and E(%) = A- In other words, these are unbiased estimators (c) The fitted values Y BotBr, are used as estimates of E(Y),...
2. Suppose we are given data on n observations (x,Y), i 1,... , n, and we have a linear model, = SXY/SXX and A,-ㄚ-Ax be the least-square estimates so that E(X) = β0 +ATp Let given in lecture. (a) Show that E(5xx)-A5xx and E(Y)-Ao +A2. (b) Use (a) to show that E(A)-A and E(A)-A. În other words, these are unbiased estimators (c) The fitted values Yi = ArtAz; are used as estimates of E(K), and the residuals ei = Y-...
Suppose we are given data on n observations (zi, Y4), i-1, . . . , n, and we have a linear model so that E(X)-β0+B1zi. Let A-SXY/SXX and A-Y-Aī be the least-square estimates given in lecture. (a) Show that E(Sxy)-ASxx and E(Y)-A +AF (b) Use (a) to show that E(BB and E(B)In other words, these are unbiased estimators (c) The fitted values Yi-A+Azi are used as estimates of %), and the residuals e,-x-Y; are used as surrogates for the unobservable...
2
2. Suppose we are given data on n observations (i, Y),, and we have a linear model, so that E(X)-A, + ßiri-Let呙-SXY /SXX and β') = F-β,2 be the least-square estimates given in lecture (a) Show that E(SXY)-ASXX and E (T)-A] + β,7. (b) Use (a) to show that E(角)-βι and E(A) = 3). In other words, these are unbiased estimators. (c) The fitted values Yt = Atari are used as estimates of E(A), and the residuals e.-Yi for...
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...
2. Consider a simple linear regression i ion model for a response variable Y, a single predictor variable ,i1.., n, and having Gaussian (i.e. normally distributed) errors: This model is often called "regression through the origin" since E(X) = 0 if xi = 0 (a) Write down the likelihood function for the parameters β and σ2 (b) Find the MLEs for β and σ2, explicitly showing that they are unique maximizers of the likelihood function Hint: The function g(x)log(x) +1-x...
3. Consider the linear model: Yİ , n where E(Ei)-0. Further α +Ari + Ei for i 1, assume that Σ.r.-0 and Σ r-n. (a) Show that the least square estimates (LSEs) of α and ß are given by à--Ỹ and (b) Show that the LSEs in (a) are unbiased. (c) Assume that E(e-σ2 Yi and E(49)-0 for all i where σ2 > 0. Show that V(β)--and (d) Use (b) and (c) above to show that the LSEs are consistent...
linear statistics modeling and regression
2) Suppose you have multiple regression set up Ynxi XnxpBpxi Sxl and f ~ N(0nx1, σ21.). P Po X(X,X)-X, be the projection matrix on the column space of X. a) Show residual vector, e = (1,-P)Y. Here e is the vector of residuals ei S. b) Show that the variance of e, is 1 - Pi, where P is the i, j th entry of the matrix P c) Show that the sample covariance of...
2. R programming
2·The data set prostate in the faraway package is froma study on 97 men with prostate cancer who were due to receive a radical prostatectomy We are interest is in predicting lpsa (log prostate specific antigen) with Icavol (log cancer volume). (a) Draw a scatterplot -does a simple linear regression model seem reasonable? (b) Without using the R function Im), compute the values T,Y, Sxx, Syy and Sxy. Com- pute the ordinary least squares estimates of the...
2. The data set prostate in the faraway package is from a study on 97 men with prostate cancer who were due to receive a radical prostatectomy. We are interest is in predicting lpsa (log prostate specific antigen) with lcavol (log cancer volume). (a) Draw a scatterplot - does a simple linear regression model seem reasonable? (b) Without using the R function Im(0, compute the values , Y,Sxx, Syy and Sxy. Com pute the ordinary least squares estimates of the...
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