suppose we have independent random variables Y1,..., Yn with Y; = 0xi + ei, i = 1,...,n, iid where 1,..., xn are fixed, known constants, 0 is a fixed but unknown parameter, and e; Normal(0, o?). a) What is the distribution of Y;? Are the Y;'s independent? Show that Yn/ã is an unbiased estimator of 0 and calculate its variance. Show that (1/n) E(Yi/x;) is an unbiased estimator of 0 and calculate its variance. This is called a linear regression model. It is very important in data analysis because it allows us to estimate the linear relationship between predictors (xi) and outcomes (yi).

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suppose we have independent random variables Y1,...,Yn with
Y; = 0x; + e;,
i = 1, . .. , n,
%3D
iid
where x1,
.., Xn are fixed, known constants, 0 is a fixed but unknown parameter, and e;
Normal(0, o?).
a)
What is the distribution of Y;? Are the Y;'s independent?
b)
Show that Yn/ã is an unbiased estimator of 0 and calculate its variance.
c)
Show that (1/) E1(Yi/x;) is an unbiased estimator of 0 and calculate its variance.
This is called a linear regression model. It is very important in data analysis because it allows us to
estimate the linear relationship between predictors (xi) and outcomes (yi).
Transcribed Image Text:suppose we have independent random variables Y1,...,Yn with Y; = 0x; + e;, i = 1, . .. , n, %3D iid where x1, .., Xn are fixed, known constants, 0 is a fixed but unknown parameter, and e; Normal(0, o?). a) What is the distribution of Y;? Are the Y;'s independent? b) Show that Yn/ã is an unbiased estimator of 0 and calculate its variance. c) Show that (1/) E1(Yi/x;) is an unbiased estimator of 0 and calculate its variance. This is called a linear regression model. It is very important in data analysis because it allows us to estimate the linear relationship between predictors (xi) and outcomes (yi).
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Step 1

               Regression Analysis

           Regression analysis helps to estimate the relationship between one independent variable and two or more dependent variable and also strength of the relationship. It has different types such as

  • Simple linear Regression
  • Multiple linear Regression and
  • Non linear regression.

          The Linear model involves the values of the coefficient should be used in the representation of the data. It includes the statistical properties which are used to estimate those coefficients and it is an amalgamation of all the standard deviations, covariance and correlations. 

 

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