Question

2. The algorithm used to estimate logistics regression is A. Least Square B. Maximum Likelihood Estimation...

2. The algorithm used to estimate logistics regression is

A. Least Square

B. Maximum Likelihood Estimation

C. Exponential Smoothing

D. None of the above

Homework Answers

Answer #1

option B

Least squares are generally used for linear regressions, and exponential smoothing are commonly used in time series models.

Please upvote.

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Probability and Statistical Inference (10th Edition) Chapter 6: Point Estimation; Section 6.4: Maximum Likelihood and Moment...
Probability and Statistical Inference (10th Edition) Chapter 6: Point Estimation; Section 6.4: Maximum Likelihood and Moment of Methods Estimation Exercise 6.4-10 6.4-10. Let X1,X2,...,Xn be a random sample of size n from a geometric distribution for which p is the probability of success. (a) Use the method of moments to find a point estimate for p. (b) Explain intuitively why your estimate makes good sense. (c) Use the following data to give a point estimate of p: 3, 34, 7,...
1.One-way ANOVA can be applied to: a)Regression model with several dummy variables (created for a qualitative...
1.One-way ANOVA can be applied to: a)Regression model with several dummy variables (created for a qualitative independent variable) to test the overall usefulness of the model b)Regression model with several quantitative independent variables to test the overall usefulness of the model c)Both of the above D) none of the above 2. You need to decide whether you should invest in a particular stock.  You would like to invest if the price is likely to rise in the long run.  Assuming the past...
Use the table below to find a least-square regression line, and then compute the sum of...
Use the table below to find a least-square regression line, and then compute the sum of the squared residuals for the least-squares regression line. You can use calculator or formula. x 3 5 7 9 11 y 0 2 3 6 9 A Linear regression: y ̂ = -3.7x + 1.1 ; Sum of squared residual: 1.6 B Linear regression: y ̂ = 1.1x – 3.7 ; Sum of squared residual: 1.6 C Linear regression: y ̂ = 1.1x +...
Instructions Estimation of the parameters for the Exponential and Weibull distributions. DATA SET A: 2, 14,...
Instructions Estimation of the parameters for the Exponential and Weibull distributions. DATA SET A: 2, 14, 23, 45, 67, 75, 89, 99, 101, 123, 138, 159, 188, 201, 203 DATA SET B: 13, 24, 35+, 65, 86, 99, 109, 118+, 131, 159, 189, 207 DATA SET C: 5, 13, 34+, 46+, 55, 74, 89, 93, 104, 112+, 126, 134, 145, 159, 167+, 173, 198, 203, 226, 241 DATA SET D: 9, 14, 85, 99, 126, 155, 169+, 199, 201+, 224+,...
1) Given the following information, what is the least squares estimate of the y-intercept? x y...
1) Given the following information, what is the least squares estimate of the y-intercept? x y 2 50 5 70 4 75 3 80 6 94 a)3.8 b)5 c) 7.8 d) 42.6 2) A least squares regression line a) can only be determined if a good linear relationship exists between x and y. b) ensures that the predictions of y outside the range of the values of x are valid. c) implies a cause-and-effect relationship between x and y. d)...
An important application of regression analysis in accounting is in the estimation of cost. By collecting...
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,100 450 5,100 550 5,300 600 5,800 700 6,300...
An important application of regression analysis in accounting is in the estimation of cost. By collecting...
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,500 450 5,500 550 5,900 600 6,400 700 6,900...
The least squares method requires that the variance ? 2/? of the error variable ? is...
The least squares method requires that the variance ? 2/? of the error variable ? is a constant no matter what the value of x is. When this requirement is violated, the condition is called: A. heteroscedasticity B. non-independence of ?ϵ C. homoscedasticity D. influential observation In regression analysis, the coefficient of determination ?2 measures the amount of variation in y that is: A. unexplained by variation in x B. explained by variation in x C. caused by variation in...
An important application of regression analysis in accounting is in the estimation of cost. By collecting...
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,000 450 5,000 550 5,400 600 5,900 700 6,400...
Model Summary Model R R Square Adjusted R Square      Std. Error of the Estimate 1...
Model Summary Model R R Square Adjusted R Square      Std. Error of the Estimate 1 .816 .666 .629 1.23721 a. Predictors: (Constant),x         ANOVA     Model Sum of Squares df Mean Square F                       Sig Regression Residual Total 27.500 13.776 41.276 1 9 10 27.500 1.531 17.966                 .002b                    a. Dependent Variable: Y                    b. Predictors: (Constant), X Coefficients Model Understand Coefficients B               Std Error Standardized Coefficients      Beta t Sig 1 (Constant)        x 3.001             1.125 .500                 .118 .816 2.667...