Answer the given question with a proper explanation and step-by-step solution.Use ExcelThis is a regression model. Using excel estimate the values of the unknowns ie A,B,C,D. Q.3Consider the following data where D = 1 means female and 0 means maleSalary Experiei GenderSXD1330.24991385.26781565.81741458.52571353.81571408 29341431.9261565.58091443.79591483.02041599.4721470.52741617.92471718,72041703.35031630.73061667.95921498.6111)1768-18021599.18911504,11081634.19511783.11231787.7381603.20991708.28121609,99851829.9171657 34611616.35881796.87191628.07311670.10831765.60291818.5151719.08781797.11361940.72971716.17361842 88861730.24991785.26781965.81741858.52571753.81571808.29341831.9261965.58091843.79591883.02041999.4721870 52742017.92472118,72042103.35031234567X9101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354551D1000110011106101D110001100011100110001101111O0000110a. Estimate the model Sa+b*D+c *X and test the hypothesis that b=0.b. Estmate the model S = a + b*X+c*DX and test the hypothesis that c = 0c. Estimate the model Sa+b*D+c*X+d*DX and test the hypothesis that b-d=0

answers :- a. Estimate the model S = a + bD + cX and test the hypothesis that b = 0. To estimate the…

Answered: Use Excel This is a regression model.…

Engineering

Computer Science

Use Excel This is a regression model. Using excel estimate the values of the unknowns ie A,B,C,D.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Related questions

Q: All data analytical models have the tendency to overfit to some degree. In which ways a liner…

A: The following solutions are

Q: ry (https://archive.ics.uci.edu/ml/datasets.php) and identify two data sets, one for…

A: as per question the solution is an given below :

Q: In Poisson regression, the natural link

A: Poisson regression is a statistical technique used for modeling count data. It's particularly useful…

Q: When do we utilize the proportional, binomial, and poisson regression models, and why do we employ…

A: Degrees of Freedom: Degrees of freedom relate to the maximum number of logically independent, or…

Q: What are some applications of linear regression?

A: - We need to have some of the applications of the linear regression.

Q: Logistic Regression is useful to supplement and improve on the linear regression algorithm (within a…

A: True

Q: Assume you have a regression problem of predicting the weather temperature for the coming week.…

A: Regression modeling means, it is a task of approximating a mapping function from the input variables…

Q: When to use the proportional, binomial, and poisson regression models, and what are their respective…

A: Proportional Regression Model:Use: Proportional regression models, such as linear regression, are…

Q: Question 1 Which of the following answer choices is correct? • (A) The estimated model shown in the…

A: select correct choice ? a) The estimate model shown in the regression above does not have an…

Q: Question: Need to choose one answer from below or attached one. A-Ordinary Least Square B-Tobit…

A: Question: Need to choose one answer from below or attached one. A-Ordinary Least Square B-Tobit…

Q: How and when should we use the Proportional, Binomial, and Poisson regression models? Please explain…

A: The correct answer for the above mentioned question is given in the following steps for your…

Q: When and why do we utilize Poisson, Proportional, and Binomial regression models? How and why are…

A: Proportional model: It shows the precise size using ratio models, making it easy to grasp. Binomial…

Q: Write the objective function that can be used to determine the regression model parameters. How is…

A: The solution to the given question is: The objective function is the sum of squared errors (SSE).…

Q: Computer Science Consider the following data: X 2 4 1 5 0 8 8.5 2 4.5 10 Y 3 4 2.5 6 1 6 7 2 5.5 10…

A: Answer: Our instruction is answer the first three part from the first part and I have given…

Q: When assessing the accuracy of a logistic regression model, the percent of incorrectly classified…

A: Given that, When assessing the accuracy of a logistic regression model, the percent of incorrectly…

Q: Explain why all-subsets regression is preferable than stepwise regression.

A: Stepwise regression chooses a model by automatically adding or eliminating individual predictors…

Q: You are expected to train a Linear Regression model on any given data. After you have trained a…

A: Linear Regression Model: Linear regression models are used to reveal or are expecting the connection…

Q: Give an example of a dependent variable in logistic regression.

A: Dependent variable: Dichotomous in nature, for the binary logistic regression dependent variables…

Q: (b) Fit the logistic regression for the training data, and report the training and testing errors.

A: Note: Due to company policies I am compelled to solve only one question and that is the first…

Q: Proportional, Binomial, and Poisson regression models: when and why? How and why do these models…

A: Proportional Model When we use a proportional model, we are trying to estimate the relationship…

Q: The table summarizes the variables collected in a study of ICU outcomes. The primary outcome…

A: which characteristic of the STA leads to a consideration of a logistic model as opposed to a linear…

Q: What precisely is meant by the phrase "sum of squares regression," and how does this statistical…

A: The total of the differences that exist between the predicted value and the mean of the dependent…

Q: Give a few examples of applications of regression

A: 1.It helps us in finding relation between multiple dependent variables and one or more independent…

Q: e mean temperature of the period 1951-1980, for t irst column indicates the year and the second co e…

A: We need to create a file called GlobalTempData.txt and fill with data.

Q: Explain the difference between the sharp regression discontinuity design and the fuzzy regression…

A: A Regression Discontinuity Design (RDD) is a quasi-experimental pre-test-post test aimed at…

Q: what situations would you utilize the Proportional, Binomial, or Poisson regression models, and why?…

A: Degrees of freedom: The maximum number of logically independent, or variable, values in a data…

Q: Please provide the values of the standard-linear-regression function evaluated at the given Question…

A: Regressiоn аnаlysis mаkes use оf mаthemаtiсаl mоdels tо desсribe relаtiоnshiрs. Fоr…

Q: When and why should you utilise the proportional, binomial, and poisson regression models? To what…

A: Regression analysis is a statistical method utilized to determine the association between two or…

Q: Describe the benefits of all-subsets regression over stepwise regression in terms of data analysis.

A: Introduction: Regression Across All Subsets: It's also known as "all potential regressions" or "all…

Q: Explain why you should use all-subsets regression instead of stepwise regression.

A: Summary Best Subsets evaluates all potential models and recommends the best candidates. So…

Q: Give an explanation of the benefits that all-subsets regression offers over stepwise regression in…

A: A regression is a statistical technique that relates a dependent variable to one or more independent…

Q: This is a coding question. Try to program a Ridge regression. Please complete the coding. Note that…

A: Ridge Regression Algorithm:1. Import necessary libraries: numpy, pandas, sklearn, matplotlib.2.…

Q: You will find a comprehensive analysis of the benefits of using all-subsets regression as opposed to…

A: INTRODUCTION: All-subsets regression outperforms stepwise regression.

Q: python

A: Given :- A Linear Regression model on any given data. After you have trained a linear regressor…

Q: When and why are proportional, binomial, and Poisson regression models used?

A: Proportional, binomial, and Poisson regression models are all types of generalized linear models…

Q: Assume you have a regression problem of predicting the weather temperature for the coming week.…

A: A regression problem requires the prediction of a quantity. A regression can have real valued or…

Q: est model?

A: Simply expressed, regression analysis and linear regression are often used interchangeably.Linear…

Q: Define regression testing. Also define, (a)Baseline version (b)Delta version (c) Delta build.

A: Define:

Q: When and why do we employ the Binomial, Poisson, and Proportional models of regression?

A: roportional model: Ratio models show the accurate size in an easy-to-understand way. Binomial…

Q: What statement about the Gradient Descent algorithm to optimize the loss function in the logistic…

A: In Gradient descent as iterations increases, error decreases, hence we can say that accuracy…

Q: Why is RMSE generally the preferred performance measure for regression tasks? A. Because it…

A: 1) RMSE stands for Root Mean Squared Error. RMSE is calculated by taking the square root of the…

Q: 1.) When and Why do we use Proportional model, Binomial model, and Poisson regression model? 2.)…

A: 1. Proportional model: It demonstrates the exact size by using the ratio models which will be easier…

Q: You will find a comprehensive analysis of the benefits of using all-subsets regression as opposed to…

A: In this article, we went through the advantages of using all-subsets regression rather than stepwise…

Concept explainers

System Model Approaches

The systems model approach is a process-oriented representation that emphasizes the impact or flow of information between modules. The multidisciplinary study of the use of models to design and create systems in business and IT development is known as systems model approach. It models how processes interact and what these processes do, but it does not go into detail about how these processes are implemented. Functional decomposition may be utilized to enhance the models employed in this technique, and they can be coupled to requirements models for additional system partitioning.

Question

Answer the given question with a proper explanation and step-by-step solution.

Use Excel

This is a regression model. Using excel estimate the values of the unknowns ie A,B,C,D.

Q.3
Consider the following data where D = 1 means female and 0 means male
Salary Experiei Gender
S
X
D
1330.2499
1385.2678
1565.8174
1458.5257
1353.8157
1408 2934
1431.926
1565.5809
1443.7959
1483.0204
1599.472
1470.5274
1617.9247
1718,7204
1703.3503
1630.7306
1667.9592
1498.6111)
1768-1802
1599.1891
1504,1108
1634.1951
1783.1123
1787.738
1603.2099
1708.2812
1609,9985
1829.917
1657 3461
1616.3588
1796.8719
1628.0731
1670.1083
1765.6029
1818.515
1719.0878
1797.1136
1940.7297
1716.1736
1842 8886
1730.2499
1785.2678
1965.8174
1858.5257
1753.8157
1808.2934
1831.926
1965.5809
1843.7959
1883.0204
1999.472
1870 5274
2017.9247
2118,7204
2103.3503
1
2
3
4
5
6
7
X
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
1
D
1
0
0
0
1
1
0
0
1
1
1
0
6
1
0
1
D
1
1
0
0
0
1
1
0
0
0
1
1
1
0
0
1
1
0
0
0
1
1
0
1
1
1
1
O
0
0
0
0
1
1
0
a. Estimate the model Sa+b*D+c *X and test the hypothesis that b=0.
b. Estmate the model S = a + b*X+c*DX and test the hypothesis that c = 0
c. Estimate the model Sa+b*D+c*X+d*DX and test the hypothesis that b-d=0

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

SEE SOLUTION Check out a sample Q&A here

Step 1

VIEW

Step 2

VIEW

Step 3

VIEW

Step by step

Solved in 3 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions

Question 4 If the output of a logistic regression is a prediction of h(x)=0.7 ,then this means that: A The prediction for y=1 is 0.7 B The prediction for y=0 is 0.7 C The prediction for y=0 is 0.3 D The prediction for y=1 is 0.3
Why and why do we use the Binomial, Poisson, and Proportional models of regression? Explain how and why degrees of freedom are included into each of these models.
what transport layer protocol is used by the point-to-point tunneling protocol?
This is a coding question. Now that you have worked out the gradient descent and the update rules. "Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showease how well your model predicts against the observations. Use spatter plot for observations, line plot for your model predictions. Observations are in color red. and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot You may also nood to fine tune hyperparameters such as leurning rate and the number of'aterations.
Explain the difference between the sharp regression discontinuity design and the fuzzy regression discontinuity design. Give an example of each and illustrate them graphically.
For finding the best fitting between model and data we have to set the best model. (a)variable (b) constant (C)error (d)parameters.
Logistic Regression practice question Note: not an exam question, please help thanks
If you are implementing regularized linear regression, and when you tested your hypothesis in a new dataset, you found that it suffer from high variance, how can you rectify the model? Give 3solutions.
When are the proportional, binomial, and poisson regression models used, and why are they utilized? How and why are these degrees of freedom included into every model?
USE AND APPLY YOUR OWN DATASET (YOU CAN FIND FROM INTERNET OR CREATE ONE) 1. Apply linear regression model only
When and why do we employ the Binomial, Poisson, and Proportional models of regression?