If in the following dataframe, "target' is our dependent variable; can we perform the Simple linear regression assuming the other fourcolumns as independent variables?sepal_length sepal_width petal_length petal_widthtarget5.13.51.402 Iris-setosa4.93.002 Iris-setosa1.424.73.21.302 Iris-setosa463.11.502 Irs-setosa4503.81.402 Iris-setosa543.91.704 Irs-setosa03 Iris-setosa02 Iris-setosa463.41.4503.41.544291.402 Iris-setosa4.93.11.50.1 Iris-setosaO a. YesO b. Cannot sayO . Cannot work for four input/independent variablesO d. No as the 'target' has non numeric values

Answered: Image /qna-images/answer/4d10a1cc-db4b-4e68-aee4-29058373e53e.jpg

If in the following dataframe, target' is our dependent variable; can we perform the Simple linear regression assuming the other four columns as independent variables? sepal_length sepal_vidth petal_Jength petal_vith target 51 35 1.4 02 Isetosa 49 3.0 14 02 setosa 47 32 13 02 Issetosa 46 3.1 1.5 02 setosa 50 14 02 Iis-setosa 54 39 1.7 04 I-setosa 46 3.4 14 03 Iris-setosa 50 15 02 I-setosa 34 44 29 02 Iris-setosa 14 01 Ire-setosa 49 3.1 1.5 O a. Yes O b. Cannot say O . Cannot work for four input/independent variables O d. No as the 'target' has non numeric values

If in the following dataframe, target' is our dependent variable; can we perform the Simple linear regression assuming the other four columns as independent variables? sepal_length sepal_vidth petal_Jength petal_vith target 51 35 1.4 02 Isetosa 49 3.0 14 02 setosa 47 32 13 02 Issetosa 46 3.1 1.5 02 setosa 50 14 02 Iis-setosa 54 39 1.7 04 I-setosa 46 3.4 14 03 Iris-setosa 50 15 02 I-setosa 34 44 29 02 Iris-setosa 14 01 Ire-setosa 49 3.1 1.5 O a. Yes O b. Cannot say O . Cannot work for four input/independent variables O d. No as the 'target' has non numeric values

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

Similar questions

4.4 You estimated a regression with the following output. Source | SS df MS Number of obs = 422 -------------+---------------------------------- F(1, 420) = 65.26 Model | 835000.255 1 835000.255 Prob > F = 0.0000 Residual | 5374272.98 420 12795.888 R-squared = 0.1345 -------------+---------------------------------- Adj R-squared = 0.1324 Total | 6209273.24 421 14748.8675 Root MSE = 113.12 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 20.01443 2.477622 8.08 0.000 15.14434 24.88451 _cons | -20.23479 242.2109 -0.08 0.933 -496.3314 455.8619…
In Big Data, suppose we wanted to retrieve records of shifts worked on a weekend in 2020 or at all during 2021. Which of the following WHERE conditions would be correct? 1 WHERE s.dayofweek IN ('SA', 'SU') AND (DATE_FORMAT(es.wdate,'%Y') = '2020' OR DATE_FORMAT(es.wdate,'%Y') = '2021' ) 2 WHERE es.wdate IN ('2020', '2021') AND s.dayofweek IN ('SA', 'SU') 3 WHERE (s.dayofweek IN ('SA', 'SU') AND DATE_FORMAT(es.wdate,'%Y') = '2020') OR es.wdate LIKE '2021%' 4 WHERE s.dayofweek = SA OR SU AND DATE_FORMAT(es.wdate,'%Y') = '2020' OR DATE_FORMAT(es.wdate,'%Y') = '2021'
Question 7 options: What value of k minimizes leave-one-out cross-validation error for this dataset? The k is What is the resulting ACCURACY? (Round to 3 decimals)
Discuss very Big Data an normality and then very small samples of less than 35.
For this question, create a new column in the dataset where total rainfall is just the sum of our three separate rainfall variables. (a) plot crop yield vs. time. Does yield appear to be stationary? Why or why not? (b) plot total rainfall vs. time. Does total rainfall appear to be stationary? Why or why not? (c) plot the first-difference of crop yield vs. time. Does this series appear to be stationary? Why or why not? (d) formally test whether crop yield, rainfall and the first-difference of crop yield are stationary using the appropriate test. Be sure to do all parts of the hypothesis tests. After these tests, what can you say the order of integration is for each of the variables?(e) estimate a model where yield is a function of rainfall and time. You do not have to worry about the time variable being stationary or not, but the other two must be stationary (you might need to difference one or both of them to make it stationary). Fully report your results.(f) test your model for…
What are the anomalies present on the given data below? Provide basic steps in solving those anomalies.
Which activation function is appropriate to use in the final layer of a regression ANN? Group of answer choices softmax No activation sigmoind Tanh
Consider each of the 40 delivery time observations. Delete 10% (4) of the observations at random. Predict the four values removed and calculate R2. Recalculate 100 times. Forecast R2. What effect does this have on model prediction? How does the mean of 100R2 prediction values compare to the R2 derived from PRESS for all 40 observations?
Consider the following data generating process (dgp): yi = 10 + 2xi + ui , ui/x ∼ i.i.N(0, σ2 ) where observations for x are generated by a uniform distribution [0, 30]. (a) Write a R script that, using seed 123, generates a sample of 80 observations from the dgp above setting σ 2 = 144. (b) Run the script. Plot the 80 observations in a scatter plot without including the fitted line. Is the scatter plot as you expected? Comment. (c) Now, repeat the parts (a) and (b) setting σ 2 = 16. Compare the graphs and comment.
Please answer I'll Upvote you, Regarding the COVID Dataset ----------------------------- The dataset has been collected from the Directorate General of Health Services, Bangladesh. The dataset has 626 rows and 4 columns. The dataset contains the number of COVID tests performed, number of persons tested positive and number of death cases for each day. In the Regression model, you must implement the following algorithms.1. Linear Regression2. Polynomial Regression3. Lasso Regression4. Ridge Regression5. Elasticnet Regression
You’ve just finished fitting a logistic regression model for email spam detection, and it is getting abnormally bad performance on both your training and validation sets (AUC of 0.55 on Train and 0.53 on Validation dataset). You know that your implementation has no bugs, so what could be causing the problem? a. You are underfitting b. You are overfitting.
This is a Machine Learning question : Given a dataset, (1,+), (7, - ), (2, +), (6, -), (5, +), (9, -), (11, +) You are supposed to find a threshold function that minimizes the error in the given dataset. Threshold functions look like this: f( x | a,b ) = sign(a-x).b where a is a real number and b is in {1,-1}. How many possible values should you consider to solve this problem? What is the value of a and b in the case that minimizes the error? What is the minimum error? Show how you found it.