Would you mind taking a look at my work (highlighted) and see if it is true/accurate?

Step 1: Generating cars dataset Cars data frame (showing only the first five observations) Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear carb 24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 31 Volvo 142E 21.4 4 121.0 10 4.11 2.780 18.60 1 14 30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Step 2: Scatterplot of miles per gallon against weight MPG against Weight 15 15 20 25 30 3.5 45 5.0 5 Weight (1000s lbs) Step 3: Correlation coefficient for miles per gallon and weight wt mpg 1.000cee*-0.866094 Beeeee.T +60998.0- M Step 4: Simple linear regression model to predict miles per gallon using weight OLS Regression Results Dep. Variable: Model : R-squared: 8.758 OLS Adj. R-squared Least Squares F-statistic: 8.743 84.05 Date: lon, 26 Sep 2822 Prob (F-statistic) : .32e-10 Time: 12:48:27 Log-Likelihood: lo. Observations: 30 Of Residuals: of Model: 28 RIC 158 .3 Covariance Type: nonrobust Coef std err [e.825 Intercept 37.2297 1.931 19. 282 33.275 41 180 wt -5.3002 0.578 e.0ee -6.484 -4.116 Omnibus : 2.182 Durbin-Watson: Prob (Omnibus) : 0.336 Jarque-Bera (JB): Skew: Prob(JB) : 0.403 Kurtosis : 2.724 Cond . No . Warnings : [1] Standard Errors assume errors is correctly specified. In this discussion, you will apply the statistical concepts and techniques covered in this week's reading about correlation coef linear regression. A car rental company wants to evaluate the premise that heavier cars are less fuel efficient than lighter cars. In other words, the company expects that fuel efficiency miles per gallon) and weight of the car (often measured in thousands of pounds) are correlated. Performing this analysis will help the company optimize its business model and charge its customers appropriately. In this discussion, you will work with a cars data set that includes two variables: Miles per gallon (coded as mpg in the data set) Weight of the car (coded as wt in the data set) 1) Does the graph show any trend? If yes, is the trend what you expected? Why or why not? The graph shows that the heavier the car the less it travels compared to a lighter car for the same amount of gas. This makes sense as it would take more gas to move a heavier car and will travel a shorter distance compared to a lighter vehicle on the same amount of gas. 2) 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? mpgT mpg 1.000000*-0. 866094 wt -0. 866094 1.000060 The correlation coefficient = -0.866 A positive correlation between two variables means that as one variable increases, the other variable increases as well. A negative correlation between two variables means that as one variable increases. the other variable decreases. The strength of correlation between a predictor variable and a response variable can be measured by the correlation coefficient denoted by p and the sample correlation coefficient is denoted by R. The strength of correlation can be described by the absolute value of R. Strength of correlation: Value of | R Strength of correlation 0 It| is 0. With the p-value being less than the significance level of 0.05, the weight value is important