Answer based off of the information in the pictures. You created a scatterplot of miles per gallon against weight; check to make sure it was included in your attachment. Does the graph show any trend? If yes, is the trend what you expected? Why or why not? See Step 2 in the Python script.What is the coefficient of correlation between miles per gallon and weight? What is the sign of the correlation coefficient? Does the coefficient of correlation indicate a strong correlation, weak correlation, or no correlation between the two variables? How do you know? See Step 3 in the Python script.Write the simple linear regression equation for miles per gallon as the response variable and weight as the predictor variable. How might the car rental company use this model? See Step 4 in the Python script.What is the slope coefficient? Is this coefficient significant at a 5% level of significance (alpha=0.05)? (Hint: Check the P-value, P>|t| , for weight in the Python output.) See Step 4 in the Python script. Step 1: Generating cars datasetThis block of Python code will generate the sample data for you. You will not be generating the dataset using numpy module this week. Instead, the datasetwill be imported from a CSV file. To make the data unique to you, a random sample of size 30, without replacement, will be drawn from the data in the CSVfile. The data set will be saved into a Python dataframe which you will use in later calculations.Click the block of code below and hit the Run button above.import pandas as pdfrom IPython.display import display, HTML#read data from mtcars.csv data set.cars_df_orig = pd.read_csv("https://s3-us-west-2.amazonaws.com/data-analytics.zybooks.com/ntcars.csv")#randomly pick 30 observations without replacement from mtcars dataset to make the data unique to you.cars_df = cars_df_orig.sample(n=30, replace=False)# print only the first five observations in the data set.print("\nCars data frame (showing only the first five observations)")display (HTML (cars_df.head().to_html()))Cars data frame (showing only the first five observations)Unnamed: 0 mpg cyl diep hp drat wt qeec ve am30110 3.08 3.215 19.44 1113 3.77 1.513 16.9011Hornet 4 Drive 21.4 6 258.027 Lotus Europa 30.4 4 95.127.3 4 79.015.02568 4.081.935 18.901Fiat X1-9Maserati Bora308 301.0 335 3.54 3.570 14.60 06 Duster 380 14.3 8 380.0 245 3.21 3.570 15.84 0#set a title for the plot, x-axis, and y-axis.plt.title('MPG against Weight")plt.xlabel('Weight (1000s lbs)')plt.ylabel('MPG")import matplotlib.pyplot as plt#create scatterplot of variables mpg against wt.plt.plot(cars_df["wt"], cars_df["mpg"], 'o', color="red")#show the plot.plt.show()1MPG against Weight10Step 2: Scatterplot of miles per gallon against weightThe block of code below will create a scatterplot of miles per gallon (coded as mpg in the data set) and weight of the car (coded as wt).Click the block of code below and hit the Run button above.NOTE: If the plot is not created, click the code section and hit the Run button again.30 35 40Weight (1000 lbs)gear carb3545312184 Step 3: Correlation coefficient for miles per gallon and weightNow you will calculate the correlation coefficient between the miles per gallon and weight variables. The corr method of a dataframe returns the correlationmatrix with correlation coefficients between all variables in the dataframe. In this case, you will specify to only return the matrix for the variables "miles pergallon" and "weight".Click the block of code below and hit the Run button above.#create correlation matrix for mpg and wt.#the correlation coefficient between mpg and wt is contained in the cell for mpg row and wt column (or wt row and mpg column)mpg_wt_corr = cars_df[['mpg', 'wt']].corr()print(mpg_wt_corr)mpgmpg 1.000000 -0.871718wt -0.871718 1.000000Step 4: Simple linear regression model to predict miles per gallon using weightThe block of code below produces a simple linear regression model using "miles per gallon" as the response variable and "weight" (of the car) as a predictorvariable. The ols method in statsmodels.formula.api submodule returns all statistics for this simple linear regression model.Click the block of code below and hit the Run button above.from statsmodels.formula.api import ols#create the simple Linear regression model with mpg as the response variable and weight as the predictor variablemodel = ols("mpg ~ wt', data=cars_df).fit()wt#print the model summaryprint(model.summary())Dep. Variable:Model:Method:Date:Time:No. Observations:Df Residuals:Df Model:Covariance Type:======InterceptwtOmnibus:Prob (Onnibus):Skew:Kurtosis:coef37.3171-5.3136OLS Regression Resultsmpg R-squared:OLSLeast SquaresThu, 08 Dec 202203:43:58nonrobuststd err302811.8930.564Adj. R-squared:F-statistic:Prob (F-statistic):Log-Likelihood:AIC:BIC:t19.717-9.414P>|t|2.5710.2760.6362.807 Cond. No.===========0.0000.000Durbin-Watson:Jarque-Bera (JB):Prob(JB):[0.02533.440-6.4700.7600.75188.613.59e-10-75.181154.4157.2=========0.975]41.194-4.1572.0782.0680.35612.2(========Warnings:[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Given that, Dependent variable (y) = miles per gallon Independent variable (x) = weight The Python…

Answered: ou created a scatterplot of miles per…

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P