X ev An investigator has reported the data tabulated below. It is known that such data can be modeled by the following equation where a and b are parameters. Use a transformation to lineanze this equation and then employ linear regression to determine a and b Based on your analysis predict y at x X y 1 0.5 ~ ~ 2.6 Answer: ERSNEN

***PLEASE INCLUDE EXCEL OUTPUT WITH YOUR RESPONSE

please help me answer this for my data analysis class

Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 1.5.

Data was collected for a regression analysis comparing car weight and fuel consumption. b0 was found to be 32.7, b1 was found to be -7.6, and R2 was found to be 0.86. Interpret the y-intercept of the line. On average, each one unit increase in the weight of a car decreases its ful consumption by 7.6 units. On average, when x=0, a car gets -7.6 miles per gallon. On average, when x=0, a car gets 32.7 miles per gallon. On average, each one unit increase in the weight of a car increases its fuel comsumption by 32.7 units. We should not interpret the y-intercept in this problem.

Use linear regression to find the equation for the linear function that best fits this data. Click Read Button For online regression calculator.  Round both numbers to two decimal places. Write your final answer in a form of an equation   y=mx+by=mx+b  x 1 2 3 4 5 6 y 74 90 109 123 143 155

Give an equation for a linear regression model for these data to predict the weight from the fuel capacity, Identify the vanables in your equation. • Justify how well your linear equation fits the model by discussing a scatter plot of the date and the correlation coetlicient in assessing the fit of your linear model. • Use your model to predict the total weight of a small motorcycle that has a tuel capacity of 6 gallons. and discuss the usefulness of this prediction.

The data point height=175 cm, height =80 cm was misrecorded as height=145 cm, height =80 cm. If we use the simple linear regression and do not correct the mistake, what is the impact of this point on the model?

Need help finishing the regression equation with the following:  Y=Residents' Preference B=Size of sector budget, sector budget, and individual trust as Independent Variables.  I'm using Jamovi software, but the need to create the equation with information above.  Thanks,