Assignment Instructions

Multiple Linear Regression.

 

 

Use the dataset that you have been using for the previous projects. Use all of your independent variables, your response variable, and the lm() function to build a multiple linear regression model. Print the model with the summary() function. The output will be similar to the bottom of page 141.

Use the pairs() function to look at the scatterplots of the interval/ratio variables. Color your points by the value of a nominal/ordinal variable.

A standard regression model with correlated independent variables will almost always perform poorly. For this project, you will remove independent variables until the model is trustworthy. Use the summary() output

The scatterplots to decide if a variable should be removed. Remove the variable.

Repeat the process of
• build model
• check summary() and scatterplots
• remove variable until you believe all variables in the model should stay in the model.
Use par(mfrow = c(2,2)) and the plot() function to look at diagnostic plots of the reduced model (similar to the plots on page 129).

Create a simple linear regression model with one of your numeric independent variable and your response variable.

Build the scatterplot of the response variable by the independent variable, Include the line of best fit on the first scatterplot.

The scatterplot of the residuals by the independent variable (similar to figure 3.3, page 50).

Plot the residuals by the response variable. Do you the scatterplots indicate that there are any problems with the model?

Use hist() to plot a histogram of the residuals. Do the residuals appear to be normally distributed?

Use qqnorm() and qqline() to plot a QQ-normal plot with the QQ-line of the residuals. Do the residuals appear to be normally distributed?
Use par(mfrow = c(2,2)) and plot(‘linear model’) to build a plot similar to figure 3.14 on page 70.

Record which data points are labeled in the subplots.
Solution:
The data points: 209, 425, 435, and 599.
Print those observations. Investigate each of these points and decide which ones are legitimate data points and which ones are erroneous and polluting your dataset. Use car::powerTransform() to find power transformations for
• y – min(y) + 1, and
• x – min(x) + 1.
Transform the data and call the new data y_new and x_new. Build four scatterplots.
• y ~ x
• y_new ~ x
• y ~ x_new
• y_new ~ x_new
Which of these models appears to be the be fit?
Solution:
The simple linear regression model.

Build the corresponding linear model.

 

 

 

 

 

 

The post Multiple Linear Regression first appeared on Bestchoice Writers.

Multiple Linear Regression

Calculate Price


Price (USD)
$

Why Choose Us For Your Assignment?

Privacy

We value all our customers' privacy. For that reason, all information stays private and confidential and will never be shared with third parties.

Punctuality

With our service you will never miss a deadline. We use strict follow-ups with our writers to ensure that all papers are submitted on time.

Authenticity

We have no tolerance for plagiarism. All papers go through thorough checking to ensure that no assignments contain plagiarism.

Money Back

You feel unsatisfied with your results? No worries. We offer refunds to our customers if any paper is not written according to the instructions.

Clients Love Us

Client #121678
Client #121678
Read More
This is by far the best I have ever scored in a custom essay. I am surprised the writer handled this assignment so well despite the short notice. I will definitely use your service next time.
Client #21702
Client #21702
Read More
When I was recommended to you by my friends, I wasn't sure you could deliver excellent results for Masters research papers until I submitted my first order. I am all yours now.
Client #20730
Client #20730
Read More
Excellent Services! You are the only assignment helper I can rely on. I have worked with many before and your services are exceptional. I have recommended you to my friends and the results are similar.
Client #20387
Client #20387
Read More
I rarely write reviews online but your services are worth promoting. My paper was so urgent I was sure I was gonna miss the deadline but you turned things around. You are awesome!
Client #20189
Client #20189
Read More
I am a satisfied customer. I know I should have given a 5 star because you deserve it but I will give 4.6 because I almost missed a deadline because of a revision. Luckily it was minor and the writer acted promptly.
Client #20187
Client #20187
Read More
Great paper but there is still some room for improvement. I am impressed by your fast responses and how you tacked my concerns professionally. Thank you for being among the few genuine essay writing service providers.
Client #19783
Client #19783
Read More
I can't thank you enough for being a great part of my college life. I recommended you to two more of my friends. I am sure they will be making their orders soon. I love the fact that you offer free pages for referrals. I will be referring a few more and maybe I won't have to pay for my next two paper, LOL.
Previous
Next