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
- Calculate the 2 × 2 matrix, xT x, ( xT x )−1 and x T y
- Calculate the 2 × 1 estimate βˆ
(b) Calculate the fitted valueˆy
(c) Calculate the diagonal element of the hat matrix H, hii.
- Calculate the inverse of 2 × 2 matrix, x T x
- Calculate xTi (xT x)−1x , where xTi is ith row of x, (1, xi).
(d) Calculate the standard residual vector r Predictive Modeling作业代写
- Calculate the residuals ei
- 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:
- Find the predicted value of LOGTPY.
- Obtain the standard error of the prediction.
- Obtain a 95% CI for your prediction.
- 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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