The following data were collected during a study of consumer buying patterns:ObservationObservationy.69y.1819812268191475418510107643577112583569512218646931329903386Click here for the Excel Data Fileb. Obtain a linear regression line for the data (Round your intermediote calculations and final answers to 3 decimal places.)y3=c. What percentage of the variation is explained by the regression line? (Do not round intermediate calculations. Round your answerto the nearest whole percent.)Approximatelyof the varlation in the dependent varlable is explainud by the independent variabled. Use the equation determined in part b to predict the expected value of y for x 44. (Round your intermediate calculations andfinal answers to 3 decimal places.)

Linear regression: Linear regression attempts to model the relationship between two variables by…

The following data were collected during a study of consumer buying patterns: Observation Observation 1. 18 69 19 81 2. 26 81 14 75 41 85 10 10 76 4. 35 77 11 25 83 56 95 12 21 86 46 93 13 29 90 33 86 Click here for the Excel Data File b. Obtain a linear regression line for the data (Round your intermediete calculations and final answers to 3 decimal pleces.) y3=

The following data were collected during a study of consumer buying patterns: Observation Observation 1. 18 69 19 81 2. 26 81 14 75 41 85 10 10 76 4. 35 77 11 25 83 56 95 12 21 86 46 93 13 29 90 33 86 Click here for the Excel Data File b. Obtain a linear regression line for the data (Round your intermediete calculations and final answers to 3 decimal pleces.) y3=

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

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Dex Research Limited conducted a research to investigate consumer characteristics that can be used topredict the amount charged by credit card users. The following multiple regression output is based on adata collected by this research company on annual income, household size and annual credit cardcharges for a sample if 50 consumers.Regression StatisticsMultiple R 0.9086R Square AAdjusted R Square 0.8181Standard Error 398.0910Observations BANOVAdf SS MS F Significance FRegression 2 D E G 1.50876E-18Residual C 7448393.148 FTotal 49 42699148.82Coefficients Standard Error t Stat P-valueIntercept 1304.9048 197.6548 6.6019 3.28664E-08Income ($1000s) 33.1330 3.9679 H 7.68206E-11Household Size 356.2959 33.2009 10.7315 3.12342E-14a. Complete the missing entries from A to H in this output (4 marks)b. Estimate the annual credit card charges for a three-person household with an annual incomeof $40,000. (2 marks)c. Did the estimated regression equation provide a good fit to the data? Explain
5.1.1 complete answer and solution only no need explanation The following data is regressed with least squares regression to y = a0 + a1x. what is the value of a1? x 1 20 30 40 y 1 400 800 1300
Run a regression predicting bill from temperature, pizzas, and got_wine. Assign the standardized regression coefficients to Q4 by using the lm.beta() function. You should not round these values. The output should look something like this: -0.30580803 0.25976246 0.08161478 Note: CodeGrade will be grading you based on the last line (just the values). Add operator to the regression from Q4. Which is the better model? Assign the better model’s AIC to Q5. Use the classical AIC (k=2). Answe should look like this:9373.33
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…
Please help with the ai question below, thanks Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6
population_df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/AnalyseProject/world_population.csv', index_col='Country Code') Question 1: Population Growth The world population data spans from 1960 to 2017. We'd like to build a predictive model that can give us the best guess at what the population growth rate in a given year might be. We will calculate the population growth rate as follows:- The formula to use in calculating the growth rate is as below:- Growth_rate =( Current_year_ population - Previous_year_population) / Previous_year_population As such, we can only calculate the growth rate for the year 1961 onwards. Write a function that takes the population_df and a country_code as input and computes the population growth rate for a given country starting from the year 1961. This function must return a return a 2-d numpy array that contains the year and corresponding growth rate for the country. Function Specifications: Should take…
Please help with the artificial intelligence question below thanks! Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6
Answer the given question with a proper explanation and step-by-step solution. Create a scatter plot of the following data of minutes driven and miles driven using Excel Driver Number Minutes driven Miles driven 1 30 15 2 22 8 3 62 32 4 45 18 5 15 10 6 24 22 7 37 12 8 14 3 9 48 15 10 36 52 11 45 40 12 24 16 13 50 48 14 32 21 15 8 2 Is there a relationship between minutes driving and miles driven?
How to solve this Use the fastfood.csv file to complete the following assignment. Create a file, fastfood.py, that loads the .csv file and runs a regression predicting calories from total_fat, sat_fat, cholesterol, and sodium, in that order. Add a constant using sm.add_constant(data). Then, print the following to two decimals print(model.mse_total.round(2)) print(model.rsquared.round(2)) print(model.params.round(2)) print(model.pvalues.round(2))
Python Regression Model 1: train MSE = 0.423, test MSE = 0.978 Model 2: train MSE = 0.572, test MSE = 0.644 Model 3: train MSE = 0.218, test MSE = 1.103 Based on this information, which of these models generalises the best to unseen data?
Python(multiple linear regression using Ordinary Least Square method) Question: Implement multiple linear regression using Ordinary Least Square method from scratch in Python. ****You are required to at least write your own the fit(), predict(), r2_score() method. Use the following dataset: titanic_train.csv, titanic_test.csv (both can be found online ,files are large so can't attach) Target: Predict whether or not the passenger survived the sinking of the Titanic. Remove unnecessary features if any. Note: do not copy paste any answer available online or in archive I want a personalised answer from an expert do display the output of an running program add comments for better understanding of solution
Please help with artificial intelligence questuon below If given a number of data samples from pairs of values (x, y) namely {(0, 12), (1, 19), (2, 29), (3, 37), (4, 45)} (1) Make a data plot from the given sample where the vertical axis is the y value and the horizontal axis is the x value. (2) Given a simple regression model: y = a x + b where a, b are constant numbers. Use the given data sample and least square method to predict constants a and b from the regression model. (3) Using the predicted regression model in question (2) above, calculate the estimated value of y if the value of x = 7.