# Statistics Exam 1 Study Guide

**Topics:**Linear regression, Regression analysis, Errors and residuals in statistics, Normal distribution, Econometrics /

**Pages:**5 (1174 words) /

**Published:**Nov 7th, 2013

Part One (approximately 70 - 80% weight) Overview:

Covers Chapters 1 – 5

Part One will begin promptly at 2:10 and end at 3:25. Students arriving late will not be given additional time. If you have a diagnosed learning disability which requires extra time, you must make arrangements with me at least one week in advance so arrangements can be made for you to take the exam at the ARC.

Consists of multiple-choice, true-false, short answer and short essay questions. There are no computational problems on Part One.

Closed book, closed notes.

Use of a cellphone, computer or other electronic device is not permitted. Please turn your cellphone completely OFF (including vibrate) for the duration of the entire exam.

Bring at least two #2 pencils. I will bring paper.

You may not wear a baseball cap during the exam. Please do not bring water bottles or food to the exam.

Once you have begun Part One, you may not leave the room for any reason. Content:

Emphasis is on application and interpretation. Assuming you have read the chapter and competed all assigned homework for Chapters 1 through 5, the best way to prepare for this portion of the exam is to review: the end of chapter summaries and all terms and concepts your quizzes the lecture notes and other class materials I have distributed the powerpoint slides posted on ANGEL how to interpret graphs and findings, such as r, r2

You may be asked to draw a histogram, bar graph, stem plot and/or scatterplot.

Know the meaning of the terms discussed throughout each chapter. (see attached list of most important terms

Understand the facts about least-squares regression (pgs. 132 – 134)

Understand the cautions about correlation and regression (pgs. 142 – 146)

Important terms to understand:

Preface and Chapter 1

Definition of statistics

Individuals

Categorical vs. quantitative variables

Distribution

The appropriate use of pie charts, bar charts, histograms,