Consider simple linear regression with n pairs of numbers xi, yi . Let β0 + gx...
1. If a true model of simple linear regression reads: yi −y ̄ = β0 +β1(xi −x ̄)+εi for i = 1, 2, · · · , n, showβ0 =0andβˆ0 =0. (1pt) (hint: use the formula of estimator βˆ0 = y ̄ − βˆ1x ̄.)
Consider the simple linear regression model: Yi = Bo + Bilitei, i = 1,...,n. with the least squares estimates ỘT = (Bo ß1). We observe a new value of the predictor: x] = (1 xo). Show that the expression for the 100(1 - a)% prediction interval reduces to the following: . (xo – x2 Ēo + @130 Etap 11+ntan (x; – 7)2
Consider the simple linear regression model: HARD1 = β0 + β1*SCORE + є, where є ~ N(0, σ). Note: HARD1 is the Rockwell hardness of 1% copper alloys and SCORE is the abrasion loss score. Assume all regression model assumptions hold. The following incomplete output was obtained from Excel. Consider also that the mean of x is 81.467 and SXX is 81.733. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.450969 Standard Error Observations 15 ANOVA df...
5) Consider the simple linear regression model N(0, o2) i = 1,...,n Let g be the mean of the yi, and let â and ß be the MLES of a and B, respectively. Let yi = â-+ Bxi be the fitted values, and let e; = yi -yi be the residuals a) What is Cov(j, B) b) What is Cov(â, ß) c) Show that 1 ei = 0 d) Show that _1 x;e; = 0 e) Show that 1iei =...
R STUDIO Create a simulated bivariate data set consisting of n 100 (xi, yi) pairs: Generate n random a-coordinates c from N(0, 1) Generate n random errors, e, from N(0, o), using o 4. Set yiBoB1x; + , Where Bo = 2, B1 = 3, and eN(0, 4). (That is, y is a linear function of , plus some random noise.) (Now we have simulated data. We'll pretend that we don't know the true y-intercept Bo 2, the true slope...
In the simple linear regression with zero-constant item for (xi , yi) where i = 1, 2, · · · , n, Yi = βxi + i where {i} n i=1 are i.i.d. N(0, σ2 ). (a) Derive the normal equation that the LS estimator, βˆ, satisfies. (b) Show that the LS estimator of β is given by βˆ = Pn i=1 P xiYi n i=1 x 2 i . (c) Show that E(βˆ) = β, V ar(βˆ) = σ...
The first script is validate.sh. This is a simple form validation script that will be used to verify the inputs given. Normally, this would be done to validate input from a website or another program before entry into a database or other record storage. In this case, we will keep it simple and only focus on the input validation step. In particular, the script should prompt the user for four values: first name, last name, zip code, and email address....