# Walden University Positive Correlation Statistics Discussion and Presentation

Please answer the following questions: Add your answer for these 2 questions ina Microsoft word, please. No specific format needed.
One correlation you believe has a positive correlation and fully explain your
reasoning.
One correlation you believe has a negative correlation and fully explain your
reasoning
Overview (power point presentation)
For this Performance Task, you will perform a comparison of different cars, develop a
linear regression equation and a multiple regression equation, and use these things
to make a decision about buying a car.
Professional Skills: Written Communication, Oral
Communication, Technology and Quantitative Fluency are assessed in this
Competency.
Your response to this Assessment should:

Reflect the criteria provided in the Rubric.
Adhere to the required assignment length.
This Assessment requires submission of one (1) file, a report containing calculations
and analysis of statistics relating to different cars.
Instructions
Before submitting your Assessment, carefully review the rubric. This is the same
rubric the assessor will use to evaluate your submission and it provides detailed
criteria describing how to achieve or master the Competency. Many students find
that understanding the requirements of the Assessment and the rubric criteria help
them direct their focus and use their time most productively.
Rubric
Choosing the Right Car for You
You are considering buying a new car and want to explore the relationship between
different characteristics of certain cars. Namely, you are concerned about braking
distance and gas mileage in the city. In addition, you want to make some predictions
about some of the characteristics of the cars. Perform the following calculations in
Statdisk and include it in a report that you can take to your car dealer.
To prepare for this Assessment:
Open the file CAR Measurements using menu option Datasets and then Elementary
Stats, 13th Edition in Statdisk. This file contains information, such as size, weight,
length, braking distance, cylinders, displacement, city miles per gallon (MPG),
highway MPG, and GHG (greenhouse gas emissions), for 21 cars.
Perform the following tasks to help you determine which car is right for you:
1. Scatterplots, Correlations, and the Correlation Coefficient
o Weight vs. Braking Columns
▪ Create a scatterplot for the data in the Weight and Braking
columns. Paste it in your report.
▪ Using Statdisk, calculate the linear correlation between the data in
the Weight and Braking columns. Paste your results in your Word
document.
▪ Explain the mathematical relationship between weight and braking
based on the linear correlation coefficient. Be certain to include
comments about the magnitude (strength) and the direction
(positive or negative) of the correlation. As weight increases, what
happens to the braking distance?
o Weight vs. City MPG
▪ Create a scatterplot for the data in the Weight and the City MPG
columns. Paste it in your report.
▪ Using Statdisk, calculate the linear correlation between the data in
the Weight and City MPG columns. Paste your results in your Word
document.
▪ Explain the mathematical relationship between weight and city
MPG based on the linear correlation coefficient. Be certain to
include comments about the magnitude and the direction of the
correlation. As weight increases, what happens to the city MPG?
o Compare the correlations for weight and braking distance with that of
weight and city MPG. How are they similar? How are they different?
2. Linear Regression and Prediction
o Let’s say that we wanted to be able to predict the braking distance in feet
for a car based on its weight in pounds.
▪ Using this sample data, perform a linear regression to determine
the line-of-best fit. Use weight as your x (independent) variable and
braking distance as your y (response) variable. Use four (4) places
after the decimal in your answer. Paste it in your report.
▪ What is the equation of the line-of-best fit (linear regression
equation)? Present your answer in y = bo + b1x form.
▪ What would you predict the braking distance would be for a car
that weighs 2650 pounds? Show your calculation.
▪ Let’s say you want to buy a muscle car that weighs 4250 pounds.
What would you predict the braking distance would be for a muscle
car that weighs 4250 pounds? Show your calculation.
▪ What effect would you predict weight would have on the braking
distance of the car? Compare the breaking distance of the 2650pound car to the 4250-pound car.
▪ Calculate the coefficient of determination (R2 value) for this data.
3. Multiple Regression
o Let’s say that we wanted to be able to predict the city MPG for a car using
weight in pounds, length in inches, and cylinders. Using this sample data,
perform a multiple-regression line-of-best-fit using weight, length,
cylinder, and city MPG.
o Select City MPG (Column 8) as your dependent variable. Paste it in your
report.
▪ What is the equation of the line-of-best fit? The form of the
equation is: Y = bo + b1X1 + b2X2 + b3X3 (fill in values for bo, b1,
b2, and b3). Round coefficients to three (3) decimal places.
▪ What would you predict for the city MPG of a car whose (1) Weight
is 3410 pounds, (2) LENGTH is 130 inches, and (3) Cylinders is 6?
o What is the R2 value for this regression? What does it tell you about the
regression?
4. Making Decisions Based on Data
o Based on the information gathered in this task on the relationship
between weight and braking distance and weight and city MPG, which of
the 21 cars listed would you choose to buy, and why?

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