# Engineering Statistics: Linear Regression Model and Correlation Coefficient

**Topics:**Statistics, Regression analysis, Errors and residuals in statistics

**Pages:**3 (499 words)

**Published:**May 5, 2013

UNIVERSITI MALAYSIA PERLIS GROUP ASSIGNMENT EQT 271 ENGINEERING STATISTICS SEMESTER 2 SESSION 2012/2013

INSTRUCTIONS: 1. 2. 3. 4. Maximum of 5 persons in a group (should be in the same program). Due date: 28 MAY 2013. Report must be typewritten using A4 paper. The front cover for the report is as in Appendix 1.

In this assignment, you will apply concepts of data approximation and fitting to some real data generated from your surveys. Each modeling tool gives you another way to represent, simplify and make decisions about the real system you are dealing with. You will compute basic statistics of the data and make some statements about basic relationships between variables. For example; could height be related to weight? The report must include: 1. 2. 3. 4. Introduction Methodology Data Analysis Result and Conclusion

1.0 Introduction In your introduction section, you should have a briefly introduction about the background of your research. 2.0 Methodology 2.1 Collecting Data Collecting data can be in two ways; get data from your experiment in the lab and do survey! So what you should have in your data? Your variable must be more than one and your data must be in sample greater than 30. 2.2 Methodology and Data Analysis 2.2.1 Basic Statistics Your calculation for basic statistics must be in Excel and compute the mean, median, standard deviation. Also, you must develop a histogram using Excel.

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EQT 271 Engineering Statistics

2.2.2 Linear regression and correlation In linear regression, you should follows those instructions: 1. Choose one pair variables, first create the scatterplot (using Excel). Do this by simply plotting one variable as the x –axis and the other y-axis. Based on the scatterplot, comment on the relationship after fitting a simple curve, so you can be creative in pairing the variables. 2. Find the linear regression model by computing either manually or using Excel. 3. Compute the correlation...

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