3. (a) A group of 30 patients are being observed for certain diseases. The entry and exit age of the patient are reported in Table 2. Use this data to calculate the quantity: dk, yk, k, Ak(tk), and S(tk). (b) Another group of six patients are also under observation. Table 3 reports the age the patient exits, the status of exits (1 if death or 0 if left the observation) and the patient gender (one for female, and zero for male). • Based on this data, derive the corresponding partial likelihood function for given gender covariate. • Find the partial likelihood maximizer estimate of the covariate and the corre- sponding Likelihood Ratio test. 2 2-2345678DDENBID152222222222222 1 71 0 1 1 0 1 9 0 10 0 11 0 0 1 1 0 71 1 0 1 19 0 20 0 1 1 1 0 1 1 1 0 29 0 30 1 238220222222222ELOUANAA293622 76 Sex Entry Age Exit Age Censor 10 72 76 1 78 1 66 67 73 70 70 73 70 66 66 223222222222222222222332~22: 74 1 74 1 71 0 72 0 0 70 0 70 1 78 1 68 1 0 73 0 73 1 75 1 70 1 0 76 1 1 74 1 70 1 0 68 0 72 1 73 1 1 70 1 92 1 74 1 68 0 Table 2: Data for Q3(a) ID Sex Age Censor 1 0 67 1 2 1 54 0 3 1 53 1 4 0 51 1 5 0 66 0 6 0 69 0 Table 3: Data for Q3(b)

3. (a) A group of 30 patients are being observed for certain diseases. The entry and exit age of the patient are reported in Table 2. Use this data to calculate the quantity: dk, yk, k, Ak(tk), and S(tk). (b) Another group of six patients are also under observation. Table 3 reports the age the patient exits, the status of exits (1 if death or 0 if left the observation) and the patient gender (one for female, and zero for male). • Based on this data, derive the corresponding partial likelihood function for given gender covariate. • Find the partial likelihood maximizer estimate of the covariate and the corre- sponding Likelihood Ratio test. 2 2-2345678DDENBID152222222222222 1 71 0 1 1 0 1 9 0 10 0 11 0 0 1 1 0 71 1 0 1 19 0 20 0 1 1 1 0 1 1 1 0 29 0 30 1 238220222222222ELOUANAA293622 76 Sex Entry Age Exit Age Censor 10 72 76 1 78 1 66 67 73 70 70 73 70 66 66 223222222222222222222332~22: 74 1 74 1 71 0 72 0 0 70 0 70 1 78 1 68 1 0 73 0 73 1 75 1 70 1 0 76 1 1 74 1 70 1 0 68 0 72 1 73 1 1 70 1 92 1 74 1 68 0 Table 2: Data for Q3(a) ID Sex Age Censor 1 0 67 1 2 1 54 0 3 1 53 1 4 0 51 1 5 0 66 0 6 0 69 0 Table 3: Data for Q3(b)

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...

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You work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much a home's distance from the water affects its selling price. You are going to collect a random sample of 7 recently sold homes in your town. You will note the distance each home is from the water (denoted by x, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also note the product x.y of the distance from the water and selling price for each home. (These products are written in the row labeled "xy"). (a) Click on "Take Sample" to see the results for your random sample. Distance from the water, .x (in km) Take Sample Selling price, y (in hundreds of thousands of dollars) xy Send data to calculator Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table…