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?

[

“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?

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

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