Statistics

Statistics
Col 1 Col 2 Col 3 Col 4 Col 5 Col 6 Col 7 Col 8 Col 9

Check
It Off! Expectations for the Week 3 Application Project:
Correlation and Regression 60 Points
Possible
1 _______
If you have not already done so, please take Walden’s academic integrity and TurnItIn tutorial to clarify your understanding of academic integrity issues and to learn how to avoid plagiarism. This online tutorial is available here: http://academicguides.waldenu.edu/writingcenter/undergraduate/academicintegrity

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Part 1: Analyze Data

• How many observations are in the file?
• Classify variables using chart provided
• Is a population or sample represented? Why?
0 – 10
points possible
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Part II: Scatter Plots

• Two graphs created for each pair of variables
• Visual relationship explained for graphs, including the direction (positive/negative) and the magnitude (strong, moderate, weak or none).
0 – 10 points
possible
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2 _______

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Part III: Correlation

• Correlation Coefficients found for both relationships as well as explanation provided in terms of magnitude and direction
and sample size and degrees of freedom are listed for computations.
• Mathematical relationship explained for correlation coefficients, including the direction (positive/negative) and the magnitude (strong, moderate, weak or none) of the relationship.
• Compare and Contrast these relationships (similarities and differences).
0 – 15 points
possible
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2 _______
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Part IV: Simple Regression

• Stat Disk Output given.
• Equation determined for best-fit line
• Determine the slope and intercept (with units) and explain what the slope and y-intercept indicate in the practical sense
• Prediction made for budget given as well as change in budget
• Determine and Explain R2 (What does it tell you about the relationship).

0 – 15 points
possible

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Part IV: Multiple Regression

• Stat Disk Output given.
• Equation determined for best-fit line (round coefficients to 2 decimals).
• Prediction made for budgets for constraints given
• Determine and Explain R2 (What does it tell you about the relationship).

0 – 10 points
possible

Academic Honesty
Review the class announcements and resources related to academic honesty and avoiding plagiary. Make sure that your application does not plagiarize from other authors. Recognize that whenever passages written by other authors are used in your work they must be enclosed in quotation marks and the original source must be cited. Applications demonstrating plagiary are worth zero points.
Comments Projects submitted late may be penalized at the instructor’s discretion. Submit your final draft using the naming convention “WK3ProejctI+first initial+last name” as the Submission Title. Total out of 60

Project – STAT 3001
please provide short answers.

Part I. Analyze Data
Instructions Answers
Open the fileCARS using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about different cars.How many observations are there in this file?

Analyze the data in this file and complete the following table, indicating for each variable what type of data it represents.
Variable Qualitative/ Quantitative Discrete/ Continuous/ Neither Level of Measurement
Car
Length
Cylinders
Size
Braking
Would you consider this data to represent a sample or a population?

Part II. ScatterPlots
. Create a scatterplot for the data in the Weight and Braking columns.Paste it here.You may need to resize the plot once it is in this file.
. Explain the visual relationship between Weight and Braking distance of the cars.
Create a scatterplot for the data in the Weight and the City MPG columns. Paste it here.You may need to resize the plot once it is in this file.
Explain the visual relationship between Weight and City MPG.

Part III. Correlation
Using Stat Disk, calculate the linear correlation between the data in the Weight and Braking columns.List the steps used for the calculation and give the resulting correlation coefficient.
Explain the mathematical relationship between Weight and Braking based on the linear correlation coefficient. Be certain to include comments about the magnitude and the direction of the correlation.

List the sample size and the degrees of freedom for this computation.
Using Stat Disk, calculate the linear correlation between the data in the Weight and City MPGcolumns.
Compare and contrast these two relationships:

Weight and Braking distance
Weight and City MPG

How are they similar? How are they different?

[Hint: “Types of Correlation”]

Part IV. Simple Regression
Let’s say that we wanted to be able to predict the Braking distancein feet for a car based on its weight in pounds. Using this sample data, perform a simple-linear regression to determine the line-of-best fit. Use the Weight as your x (independent) variable and braking distance as your y (response) variable.Use 4 places after the decimal in your answer.

Paste your results here:
Answer the following questions related to this simple regression

What is the equation of the line-of-best fit? Insert the values for bo and b1from above into y = bo + b1x.
What is the slope of the line? What does it tell you about the relationship between the Weight (Pounds)and Braking distance(Feet) data? Be sure to specify the proper units.

What is the y-intercept of the line? What does it tell you about the relationship between the Weight and Braking distance?

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 effect would you predict this would have on the braking distance of the car? Relate this to the Braking distance you found for a car weighing 2650 pounds in the previous question.
Find the coefficient of determination (R2 value) for this data. What does this tell you about this relationship?

Part V. Multiple Regression
Let’s say that we wanted to be able to predict the city miles per gallon for a car using

• Weight in pounds
• Length in inches
• Cylinders

Using this sample data, perform a multiple-regression using Weight, Length, Cylinder, City. Select City (Column 8) as your dependent variable.

Paste your results here:

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 3 decimal places.]
What would you predict for the City MPG earnings of a car whose

• Weight is 3410 pounds
• LENGTH is 130 inches
• Cylinders is 6
What is the R2 value for this regression? What does it tell you about the regression?