2. BIC, Bayesian information criteria or SBC, Schwarz' Bayesian Criteria, are the same. Give theequations for AIC and BIC and explain the difference in these two equations in terms of the termsin the equations as well as the consequences.3. Give a short description of measuring the actual predictive capabilities of the selected regressionmodel.

Since you have asked multiple questions, we will solve the first question for you. If you want any…

Answered: 2. BIC, Bayesian information criteria…

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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Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. + ŷ = HRT Use Breast Cancer Incidence 46.30 40.60 39.50 36.60 30.00 USE SALT 103.30 105.00 100.00 93.80 83.50 (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women)…
A researcher collected statistics on the sales amount of a product in 120 different markets and the advertising budgets used in TV, radio and newspaper media channels for each of these markets. The sales amount are expressed in 1000 units, and the budgets are expressed in 1000 $. The researcher wants to create a simple linear regression model by choosing one among the TV, radio and newspaper advertising budgets to explain the amount of sales.a) Which variable should this researcher choose as an independent variable to the simple regression model? Explain your decision by providing its statistical basis.(use formula)b) Construct the simple linear regression model using the argument you chose and write the equation of the model. Comment b0 and b1.(not excel use formula please)c) Test whether there is a statistically significant, linear relationship between the independent variable and the dependent variable by establishing the relevant hypotheses at the level of α = 0.05 significance.…
2. In your own words, please describe the difference between the regression equation ŷ = b + b₁x and the regression equation y = P + ₁x (Section 10-3).
What are the different ways to reduce the error in least square regression?
How could we provide evidence for the positive association between two variables through linear regression model and hypothesis testing? What are the steps?
The marketing manager wants to estimate the effect of the MBA program on Salary controlling for the other factors. Which regression model is the MOST appropriate? Oa. Salary = B_0+B_1 MBA + ε Ob. Salary = 3_0+ B_1 MBA + B_2 Work + e c. Salary = B_0+B_1 MBA+B_2 Work + B_3 Age +8 Od. Salary = B_0+ B_1 MBA + B_2 Work + B_3 Age +B_4 Gender + ε
A retail company wants to understand the factors that impact its sales revenue. The company has collected data on the following variables for the past 5 months: Total sales revenue (Y), Average store foot traffic (XI), and Marketing budget (X3). Develop a multiple linear regression model to predict that, what is the impact of average store foot traffic, and marketing budget on total sales revenue for the retail company. The data is summarized in Table 4. Table 4 Average Store foot traffic (X1) Sales Revenue (Y) 50$ 5 130S 45$ 48$ 7 6 120$ 200$ 60$ 708 5 4 Marketing Budget (X2) 130$ 250$ Note: Average Store foot traffic is the average number of people enter in the store per minute
Provide an example of a regression that arguably would have a high value of R² but would produce biased and inconsistent estimators of a causal effect. Explain why the R² is likely to be high. Explain why the OLS estimators would be biased and inconsistent.
Evaluate the predictive appropriateness of the linear model (the linear regression equation). Support your evaluation with analysis values including the coefficient of determination. That is my question given to me above, I just do not understand how to evaluate the predictive appropriateness from the linear regression equation. Can you please help me?
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?
This is my question! All parts please.
Parameter estimates Direct effects Freedom Generosity Family Life Expectancy Note. Delta method Indirect effects Freedom Freedom Generosity Generosity Family Family Total effects Freedom Generosity Estimate Std. Error Freedom Generosity Family Life Expectancy 1.476 0.381 1.118 1.289 GDP per Capita Happiness Score Happiness Score Government Corruption CDP per Capita Government Corruption GDP per Capita Government Corruption GDP per Capita Government Corruption Happiness Score Happiness Score Life Expectancy Happiness Score Happiness Score Life Expectancy Note. Delta method standard errors, normal theory confidence intervals, ML estimator. Happiness Score Happiness Score Happiness Score Happiness Score standard errors, normal theory confidence intervals, ML estimator. 1111 0.335 0.322 0.197 0.314 Estimate Std. Error 1.828 0.253 1.402 2.278 z-value Estimate 0.352 -0.128 0.284 0.989 Р 4.409 <.001 1.183 0.237 5.661 <.001 4.102 <.001 Happiness Score Happiness Score 0.331 0.332 0.194 0.227…

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