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Predictive Modeling作业代写-预测建模代写
Predictive Modeling作业代写

Predictive Modeling作业代写-预测建模代写

Predictive Modeling

Predictive Modeling作业代写 You are asking to accessing the effectiveness of predictor, LOGNUMBED. Also compute the corresponding p-value.

HW 3. Basic Linear Regression Models   Predictive Modeling作业代写

1.Nursing Home Utilization data for the following questions. this exercise involves data filename ”WiscNorsingHome” Frees page 59.

You will need to read a .csv file into R by read.csv(file,header=TRUE) for this questions.

You decide to examine the relationship between total patient years (LOGTPY) and the number of beds (LOGNUMBED), both in logarithmic units, using cost-report year 2001 data.

You are asked to perform below in both excel workbook and verify with R

(a) Estimation of coefficients, β0, β1

  1. Calculate the 2 × 2 matrix, xT x, ( xT x )−1 and x T y
  1. Calculate the 2 × 1 estimate βˆ

(b) Calculate the fitted valueˆy

(c) Calculate the diagonal element of the hat matrix H, hii.

  1. Calculate the inverse of 2 × 2 matrix, x T x
  2. Calculate xTi (xT x)−1x , where xTi is ith row of x, (1, xi).
Predictive Modeling作业代写
Predictive Modeling作业代写

(d) Calculate the standard residual vector r  Predictive Modeling作业代写

  1. Calculate the residuals ei
  2. Calculate the standard residual

(e) Calculate R2 , adjusted R2 , F−stat, p-value, and the mean squared error (MSE).

(f) You are asking to accessing the effectiveness of predictor, LOGNUMBED. Also compute the corresponding p-value.

1.Hypothesis testing: H0 : β1 = 0 versus Ha : β1 ≠ 0 at the 5% levels of significance using a t-statistic.

2.Compute the p-value. what is your assessment of the estimate βˆ 1?

3.Provide a 95% confidence internal (CI) corresponding to the point estimate for β1.  Predictive Modeling作业代写

4.Provide a 99% CI corresponding to the point estimate for β1.

(g) At a specified number of beds estimate x∗ = 100, do these things:

  1. Find the predicted value of LOGTPY.
  2. Obtain the standard error of the prediction.
  3. Obtain a 95% CI for your prediction.
  4. Obtain a prediction interval, corresponding to a 90% level (in lieu of 95%).

(h) (Perform in R) Fit the basic linear model using LOGTPY as response variable and LOGNUMBED as explanatory variable. Compare results with what you calculate above.

 

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Predictive Modeling作业代写
Predictive Modeling作业代写

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