Answered: a) Var (e) = b) Var (e.) = ²√x, ²x²

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Can you help me solve sub-part C
In the simple linear regression model , the Gauss Markov ( classical ) assumptions guarantee that the OLS estimator of the unknown parameters is BLUE. Among those assumptions , in order to have consistency of the OLS estimator we need ( this is a question on the necessary condition ): Question 4Select one: a. we need that the errors are not correlated with each others and that they have zero mean b. we need that the errors are homoscedastic ( all have the same variance) c. we need that the residuals are not correlated with the explanatory variables and that the residuals have zero mean d. we need that the errors have zero mean and that they are not correlated with the regressors ( no endogeneity) e. we need that the errors are homoscedastic ( all have the same variance) and not correlated with each others ( no serial correlation)
The market researcher in a certain company conducted a study to assess the effectiveness of a new computerized system of filling orders. He selected a random sample of 100 customers served using the old system and 100 served using the new system. He contacts each customer to find out whether or not the order was filled satisfactorily within 2 weeks. Results were recorded in the accompanying table: System Satisfied Not Satisfied Total OLD 68 32 100 NEW 85 15 100 Total 153 47 200 Test at alpha equal to 5% the null hypothesis that the proportion of satisfied customers among those served by the new system is the same as that among those served by the old system.
I only need the part ii-iii-iv
If the Cumulative Gain at a depth of 15% for the Neural Network model is converted to number of primary/positive event cases, what will be the number of cases? Show your calculation.
D2
For least-squares to work well, we need: ) the relationship between x and y to be non-linear. residuals to be Uniformly distributed. ) the residuals to have a mean of zero. the residuals to be correlated with the explanatory variable.
A youtuber is interested about the participation of its viewers in the games they watch him play on his channel. The youtuber wants to compare the views he gets from them watching fortnight vs the views he gets from them watching the game Among Us. Use the data given and show in an Excel work sheet to test at an α=0.01 level that there is a difference in the proportion in the number of views he gets from fortnight compared to the views from Among Us. Fortnight Among Us Total Viewers 35 23 58 Non-Viewers 69 53 122 Total 104 76 180 What is the Null and alternative hypothesis? what would be the Point estimate? P-value? Decision? Interpretation including whether there is evidence in a difference in proportion views fortnight game vs Among us game
a production line is producing light bulbs. 90% of the bulbs are good and .10% are defective. Suppose each bulbls condition is dependent of the other bulbs condition Select three buls at randon: 1. P(all 3 are good)=? 2. P( 2 of 3 are good)= 3. P(1 of 3 are good)=
1. Derive the least squares estimators (LSES) of the parameters in the simple linear regression model. 2. Derive the estimators of 30 and ß1 using maximum likelihood estimation procedures.
Phoebe gathers data and estimates many di¤erent regression models. All of them suggest that children who have more books at home have fewer cavities. Phoebe doesnt think this result is important because: a.) There is clearly selection bias, kids who have lots of books at come from higher income and better educated families.b.) The data on cavities is certainly heteroskedasticc.) Phoebe only used ordinary least squares in her model, should have used weighted least squares d.) Phoebe didnt use a high enough condence level
What are the difficulties in estimating the following model? Use as much detail as possible in answering this question while considering the Gauss-Markov assumptions and OLS estimator. Economic productivity = β0 + β1Unemployment + β2Innovation + θiControls + ei Where unemployment is the average unemployment rate of a country and innovation is an index of R&D performance.

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