Math 075 Activities and Worksheets Book 2:
|
|
- Shanon Kelly
- 5 years ago
- Views:
Transcription
1 Math 075 Activities and Worksheets Book 2: Linear Regression Name: 1
2 Scatterplots Intro to Correlation Represent two numerical variables on a scatterplot and informally describe how the data points are distributed and any apparent relationship that exists between the two variables (e.g., between time spent on homework and grade level). Write positive correlation, negative correlation, or no correlation to describe each relationship
3 7. Use the given data to make a (year, units of CD s) scatter plot. 8. What kind of correlation is there between the year and the number of CD s sold? 9. Use the given data to make a (year, units of cassettes) scatter plot. 10. What kind of correlation is there between the year and the number of cassettes sold? 11. The scatter plot to the right shows the average traffic volume and average vehicle speed on a I-80 for 50 days in Which statement best describes the relationship between average traffic volume and average vehicle speed shown on the scatter plot? A As traffic volume increases, vehicle speed increases. B As traffic volume increases, vehicle speed decreases. C As traffic volume increases, vehicle speed increases at first, then decreases. D As traffic volume increases, vehicle speed decreases at first, then increases. 3
4 Understanding Scatterplots Match each description for a set of measurements (A and B) to a scatterplot, and briefly explain your reasoning. Each graph in this packet can only be used once. Scatterplot 1: Scatterplot 2: 1. If x = city miles per gallons and y = highway miles per gallon for 10 cars, describe which scatter plot is likely the correct graph. Explain your reasoning. a. What does a dot represent? 2. If x = sodium (milligrams/serving) and y = Consumer Reports quality rating for 10 salted peanut butters, describe which scatterplot is likely the correct graph. Explain your reasoning. a. What does a dot represent? 4
5 These scatterplots show body measurements for 34 adults who are physically active. Some measurements are a girth, which is a measure of length around a body part. Match each description (A, B, and C) to a scatterplot. Briefly explain your reasoning. A. B. C. 3. x = forearm girth (centimeters), y = bicep girth (cm). The bicep is above the elbow. a. What does a dot represent? 4. x = calf girth (cm), y = bicep girth (cm). The calf is below the knee. a. What does a dot represent? 5. x = age (years), y = bicep girth (cm) a. What does a dot represent? 5
6 Match each description of a set of measurements to a scatterplot. Then describe what a dot represents in each graph. 6. x = average outdoor temperature and y = heating costs of a residence during the winter 7. x = height (inches) and y = shoe size for a random sample of adults 8. x = height (inches) and y = score on an intelligence test for a random sample of teenagers (15-17) Lines have been added to some of the scatterplots used in the Lesson to summarize the relationship between the ingredient and the Consumer Reports rating for breakfast cereals. You will learn more about summary lines in future lessons. 9. Which ingredients (sugar, protein, and/or fat) are negatively associated with ratings? 10. Which of the negatively associated ratings is the strongest? 6
7 House Prices: Correlation Your lab report should include a well written response to each of the following questions and all relevant supporting graphs and analyses performed using StatCrunch. Submit your assignment through CANVAS by uploading it as a document (either in word format, or in pdf). Remember to put your name on the document itself. Regression: Find the data set house prices.txt from the class StatCrunch group and load it into StatCrunch. The data set house prices contains information collected on characteristics of houses that were sold in a suburban community. House prices (the price at which the house sold in thousands of dollars), its size (in square feet), and other characteristics of the house that are usually recorded when a house is on the market. In task #1 we want to investigate the relationship between the price of a house and its size. 1. Which variable is the explanatory variable and which variable is the response variable when investigating the relationship between the price of a house and its size? 2. Construct a scatterplot for the data. Graph -> Scatter Plot -> For x variable, select the explanatory variable. For y variable, select the response variable. Click Compute. Copy the graph below: 3. How can we describe the nature of the relationship between house price and size from the scatterplot? (Think form, direction, strength.) Do you notice any outliers or deviations from the general pattern? 4. Compute the correlation for house price and size. Stat -> Summary Stats -> Correlation. Choose the two variable names for which you want to calculate the correlation. Click Compute. 5. What does the correlation you found say about the nature of their relationship? (Think about what the correlation measures.) 7
8 Diamond Linear Regression Worksheet An article in the Journal of Statistics Education reported the price of diamonds of different sizes in Singapore dollars (SGD). The following data set contains a data set that is consistent with this data, adjusted to US dollars in 2004 Open the Diamond Data set in our StatCrunch Group, and answer the questions. 1. What is the response variable and what is the explanatory variable in this model? - Explanatory variable: - Response variable: 2. Explain why you chose the way you did for Number 1 3. Construct a Scatterplot. How do you describe the scatterplot relationship that you observe? form: direction: strength: 4. Find the least square s regression line that describes the price of a diamond in relation to it s carat size. 5. What is the slope, in units? Interpret the slope using a complete sentence. 8
9 6. What is the y intercept with units? Interpret the y intercept using a complete sentence. 7. Using the regression equation estimate the cost of a diamond that is 0.32 carats big. 8. Nick bought a diamond that is 0.32 and was included in the data set given. What is his residual? What does that mean? Did Nick overpay or underpay? 9. Calculate the correlation. What does the correlation say about the nature of the relationship between diamond size and price? 10. How much variability in price does the carat size explain? What number are you using for your answer? 9
10 Residual Plots: 11. Construct a residual plot. a. Go to Stat > Regression > Simple Linear. Select the appropriate explanatory and response variables. b. On the editing page: scroll down to graphs. Under graphs, scroll down and highlight Residual vs X- values. Click compute. c. Which graph from (a) (f) from above does your residual plot look like? Notice that the red line must be at y = 0 d. If a linear model is a good fit, the graph will look like (a), scattered everywhere! Do you think that a linear model is a good fit for our data? Why or why not? 10
11 Now construct a histogram of the residuals under the same menu. 12. If a linear model is a good fit the histogram of the residuals should be normal, centered around zero, like below. Do you think a linear model is a good fit for our data? Why or why not? 13. What is the standard error Se? This is found by going to Stat > Regression > Simple Linear. Select the appropriate explanatory and response variables, and the output should have a Estimate of error standard deviation. This is the Se. 14. Se represents the average distance that the observed values fall from the regression line. Write a statement in context of the data we are writing about. 11
12 USPS Postal Linear Regression Classwork In an effort to decide if there is an association between the year of a postal increase and the new postal rate for first class mail, the data were gathered from the United States Postal Service. In 1981, the United States Postal Service changed their rates on March 22 and November 1. This information is shown in the data set below. Find it on StatCrunch, and load it. 1. Choose an appropriate year representation for t = 0. We do not want to use such big numbers for our model. Make a new column that has the title Years since Put in the appropriate numbers. 2. What is the response variable and what is the explanatory variable in this model? - Response variable: - Explanatory variable: 3. Explain why you chose the way you did for Number 1 4. Construct a Scatterplot. How do you describe the scatterplot relationship that you observe? form: direction: strength: 5. Find the least square s regression line that describes relationship between the year and postal rate. 12
13 6. What is the slope, in units? Interpret the slope using a complete sentence. 7. What is the y intercept with units? Interpret the y intercept using a complete sentence. 8. Using the regression equation estimate the cost of a postage stamp in (Hint: you re not plugging in 1977!) 9. The actual postage stamp cost $0.13. What is the residual? (Remember the residual is the actual value minus the predicted value). 10. Calculate the correlation. What does the correlation say about the nature of the relationship between years since 1970 and the postage rate? 11. How much variability in postage rate does the year explain? What number are you using for your answer? 13
14 Residual Plots: 12. Construct a residual plot. a. Go to Stat > Regression > Simple Linear. Select the appropriate explanatory and response variables. b. On the editing page: scroll down to graphs. Under graphs, scroll down and highlight Residual vs X- values. Click compute. c. Which graph from (a) (f) from above does your residual plot look like? Notice that the red line must be at y = 0 d. If a linear model is a good fit, the graph will look like (a), scattered everywhere! Do you think that a linear model is a good fit for our data? Why or why not? 14
15 Now construct a histogram of the residuals under the same menu. 13. If a linear model is a good fit the histogram of the residuals should be normal, centered around zero, like below. Do you think a linear model is a good fit for our data? Why or why not? 14. What is the standard error Se? This is found by going to Stat > Regression > Simple Linear. Select the appropriate explanatory and response variables, and the output should have a Estimate of error standard deviation. This is the Se. 15. Se represents the average distance that the observed values fall from the regression line. Write a statement in context of the data we are writing about. 15
16 Olympics Long Jump The following data set contains a data set with the winning jump lengths (in meters) for the Olympics Men s Long Jump Winners. Open the data set in our StatCrunch Group, and answer the questions. 1. What is the response variable and what is the explanatory variable in this model? - Explanatory variable: - Response variable: 2. Explain why you chose the way you did for Number 1 3. Construct a Scatterplot. How do you describe the scatterplot relationship that you observe? form: direction: strength: 4. Find the least square s regression line that describes the length of a winning jump in relation to the year 5. What is the slope, in units? Interpret the slope using a complete sentence. 16
17 6. There was no data for 1940? Google search if there was Olympics in 1940 and explain why there s no data. 7. If there had been Olympics in 1940, predict what the winning long jump would have been using your regression model. Does this number seem reasonable? 8. Is it okay to predict the future? Predict what the winning long jump will be in Does this number seem reasonable? Why or why not? 9. Calculate the correlation. What does the correlation say about the nature of the relationship between the winning long jump distance and the year? 10. What is the standard error Se? This is found by going to Stat > Regression > Simple Linear. Select the appropriate explanatory and response variables, and the output should have a Estimate of error standard deviation. This is the Se. What does this number tell us? 17
18 Fatalities Worksheet: Linear Regression We are going to analyze the association between the number of drunk driving fatalities and the years after What would be your explanatory and response variables in this analysis? Explanatory Variable: Response Variable: 2. Create a fitted line plot that describes the linear association between the years after 1980 and the number of drunk driving fatalities. Be sure to choose the appropriate explanatory and response variables. Be sure to label the axes correctly including the appropriate units being measured. Minitab: Drunk Driving Fatal Accidents Fitted Line Plot Drunk Driving Fatal Accidents = Yr Since 80 S R-Sq 81.6% R-Sq(adj) 80.8% Yr Since What is the equation of the least squares regression line? 4. What is the slope of your model including units? Write a sentence that interprets this slope. 5. What is the intercept of your model? Write a sentence to interpret your intercept in context. 18
19 6. What is the correlation coefficient of your linear model? What does this value tell you about the strength of the linear relationship? 7. Find the predicted number of drunk driving fatalities in Show work. 8. Find the estimated residual (error) for the number of drunk driving fatalities in (Remember the residual is the actual value minus the predicted value). 9. What is the Coefficient of Determination (r 2 )? Write a sentence to interpret it. 10. What is the Standard Error of Regression (S e)? Write a sentence to interpret it. 19
20 Play Ball!! And Do Statistics!!! Objective: You will be playing soccer today, represent the data on a scatterplot, and analyze the data. Materials: Soccer board and spinner 1 large paper clip per group A ball such as a penny Directions: 1. Flip your penny to decide who goes first 2. Put the penny on the dark line in the center of the game board. 3. Players take turns (one goes, then the other person goes) by spinning the paper clip on the spinner and moving that many yards toward his/her opponent s goal (each line on the game board represents ten yards). a. Keep track of the total number of yards for both players in the table below. b. This means, for instance if the first person who goes spins 10, then the first data point would be (1,10) If the second person scores 20, the next data point would be (2,30) since it s the TOTAL yards. 4. Each time a player gets to his/her opponent s goal, s/he scores one point Collecting Data: Collect the following data, stop after 20 turns (each person will have 10 kicks of the ball/spins) Turn Total yards traveled Keep Track of your scores! 20
21 Now let s do some statistics: 1. Enter the Turn and Total yards traveled into StatCrunch 2. Which variable is the explanatory variable and which variable is the response variable when investigating the relationship between turns and total yards? Why did you choose this way? 3. Construct a scatterplot for the data. Graph -> Scatter Plot -> For x variable, select the explanatory variable. For y variable, select the response variable. Click Compute. Copy the graph below: 4. How can we describe the nature of the relationship between Turn and Total yards traveled from the scatterplot? (Think form, direction, strength.) Do you notice any outliers or deviations from the general pattern? 5. Compute the correlation for Turn and Total yards traveled. Stat -> Summary Stats -> Correlation. Choose the two variable names for which you want to calculate the correlation. Click Compute. 6. What does the correlation you found say about the nature of their relationship? (Think about what the correlation measures.) Is it a strong or weak correlation? 7. Find the LSRL Least Squares Regression Line by going to Stat -> Regression -> Simple Linear For x variable, select the explanatory variable. For y variable, select the response variable. Click Compute. Report the Least Squares regression line. (You don t need to give me the rest of the output, just the estimated regression equation.) 8. What is the slope, including the units? Then write a statement interpreting the slope. 9. What is the y intercept, including the units? Then write a statement interpreting the y intercept. 21
22 22
23 StatCrunch CW/HW: Linear Regression Refresher Your lab report should include a well written response to each of the following questions and all relevant supporting graphs and analyses performed using StatCrunch. Submit your assignment through CANVAS by uploading it as a document (either in word format, or in pdf). Remember to put your name on the document itself. Linear Regression: Open the Amount in Savings ($) in the StatCrunch group. The savings account was opened in The following ordered pairs give the number of years since 1990 and the amount of money in a savings account. 1. What is the response variable and what is the explanatory variable in this model? - Response variable: - Explanatory variable: 2. Explain why you chose the way you did for Number 1 3. Construct a Scatterplot. Post the scatterplot below 4. How do you describe the scatterplot relationship that you observe? form: direction: strength: 23
24 5. Find the least square s regression line that describes the linear relationship 6. What is the slope, in units? Interpret the slope using a complete sentence. 7. What is the y intercept with units? Interpret the y intercept using a complete sentence. 8. Calculate the correlation. What does the correlation say about the nature of the relationship between the two variables we are looking at? 9. How much variability in cost does the size explain? What number are you using for your answer? 24
25 Residual Plots: 10. Construct a residual plot. a. Go to Stat > Regression > Simple Linear. Select the appropriate explanatory and response variables. b. On the editing page: scroll down to graphs. Under graphs, scroll down and highlight Residual vs X-values. Click compute. c. Which graph from (a) (f) from above does your residual plot look like? Notice that the red line must be at y = 0. Post the graph below: d. If a linear model is a good fit, the graph will look like (a), scattered everywhere! Do you think that a linear model is a good fit for our data? Why or why not? 25
26 11. Now construct a histogram of the residuals under the same menu. Post the graph below 12. If a linear model is a good fit the histogram of the residuals should be normal, centered around zero. Do you think a linear model is a good fit for our data? Why or why not? 13. What is the standard error Se? This is found by going to Stat > Regression > Simple Linear. Select the appropriate explanatory and response variables, and the output should have a Estimate of error standard deviation. This is the Se. 14. Se represents the average distance that the observed values fall from the regression line. Write a statement in context of the data we are writing about. 26
(a) 50% of the shows have a rating greater than: impossible to tell
KEY 1. Here is a histogram of the Distribution of grades on a quiz. How many students took the quiz? 15 What percentage of students scored below a 60 on the quiz? (Assume left-hand endpoints are included
More informationHow Faithful is the Old Faithful? The Practice of Statistics, 5 th Edition 1
How Faithful is the Old Faithful? The Practice of Statistics, 5 th Edition 1 Who Has Been Eating My Cookies????????? Someone has been steeling the cookie I bought for your class A teacher from the highschool
More informationCHAPTER 3 Describing Relationships
CHAPTER 3 Describing Relationships 3.1 Scatterplots and Correlation The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers Reading Quiz 3.1 True/False 1.
More informationHW 3.2: page 193 #35-51 odd, 55, odd, 69, 71-78
35. What s My Line? You use the same bar of soap to shower each morning. The bar weighs 80 grams when it is new. Its weight goes down by 6 grams per day on average. What is the equation of the regression
More information(a) 50% of the shows have a rating greater than: impossible to tell
q 1. Here is a histogram of the Distribution of grades on a quiz. How many students took the quiz? What percentage of students scored below a 60 on the quiz? (Assume left-hand endpoints are included in
More informationLecture 12: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression
Lecture 12: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression Equation of Regression Line; Residuals Effect of Explanatory/Response Roles Unusual Observations Sample
More informationHomework #3. SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.
Homework #3 Name Due Due on on February Tuesday, Due on February 17th, Sept Friday 28th 17th, Friday SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Fill
More informationLecture 6B: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression
Lecture 6B: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression! Equation of Regression Line; Residuals! Effect of Explanatory/Response Roles! Unusual Observations! Sample
More informationAP Statistics Practice Test Ch. 3 and Previous
AP Statistics Practice Test Ch. 3 and Previous Name Date Use the following to answer questions 1 and 2: A researcher measures the height (in feet) and volume of usable lumber (in cubic feet) of 32 cherry
More informationc. Construct a boxplot for the data. Write a one sentence interpretation of your graph.
STAT 280 Sample Test Problems Page 1 of 1 1. An English survey of 3000 medical records showed that smokers are more inclined to get depressed than non-smokers. Does this imply that smoking causes depression?
More information7) Briefly explain why a large value of r 2 is desirable in a regression setting.
Directions: Complete each problem. A complete problem has not only the answer, but the solution and reasoning behind that answer. All work must be submitted on separate pieces of paper. 1) Manatees are
More informationChapter 3: Examining Relationships
Name Date Per Key Vocabulary: response variable explanatory variable independent variable dependent variable scatterplot positive association negative association linear correlation r-value regression
More informationLesson 1: Distributions and Their Shapes
Lesson 1 Name Date Lesson 1: Distributions and Their Shapes 1. Sam said that a typical flight delay for the sixty BigAir flights was approximately one hour. Do you agree? Why or why not? 2. Sam said that
More informationChapter 3 Review. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.
Name: Class: Date: Chapter 3 Review Multiple Choice Identify the choice that best completes the statement or answers the question. Scenario 3-1 The height (in feet) and volume (in cubic feet) of usable
More informationMidterm STAT-UB.0003 Regression and Forecasting Models. I will not lie, cheat or steal to gain an academic advantage, or tolerate those who do.
Midterm STAT-UB.0003 Regression and Forecasting Models The exam is closed book and notes, with the following exception: you are allowed to bring one letter-sized page of notes into the exam (front and
More informationLab 5a Exploring Correlation
Lab 5a Exploring Correlation The correlation coefficient measures how tightly the points on a scatterplot cluster around a line. In this lab we will examine scatterplots and correlation coefficients for
More informationScatter Plots and Association
? LESSON 1.1 ESSENTIAL QUESTION Scatter Plots and Association How can you construct and interpret scatter plots? Measurement and data 8.11.A Construct a scatterplot and describe the observed data to address
More informationLAB ASSIGNMENT 4 INFERENCES FOR NUMERICAL DATA. Comparison of Cancer Survival*
LAB ASSIGNMENT 4 1 INFERENCES FOR NUMERICAL DATA In this lab assignment, you will analyze the data from a study to compare survival times of patients of both genders with different primary cancers. First,
More information3.2A Least-Squares Regression
3.2A Least-Squares Regression Linear (straight-line) relationships between two quantitative variables are pretty common and easy to understand. Our instinct when looking at a scatterplot of data is to
More informationChapter 3 CORRELATION AND REGRESSION
CORRELATION AND REGRESSION TOPIC SLIDE Linear Regression Defined 2 Regression Equation 3 The Slope or b 4 The Y-Intercept or a 5 What Value of the Y-Variable Should be Predicted When r = 0? 7 The Regression
More informationq3_2 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
q3_2 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) The relationship between the number of games won by a minor
More informationSTAT 201 Chapter 3. Association and Regression
STAT 201 Chapter 3 Association and Regression 1 Association of Variables Two Categorical Variables Response Variable (dependent variable): the outcome variable whose variation is being studied Explanatory
More informationHomework Linear Regression Problems should be worked out in your notebook
Homework Linear Regression Problems should be worked out in your notebook 1. Following are the mean heights of Kalama children: Age (months) 18 19 20 21 22 23 24 25 26 27 28 29 Height (cm) 76.1 77.0 78.1
More informationA response variable is a variable that. An explanatory variable is a variable that.
Name:!!!! Date: Scatterplots The most common way to display the relation between two quantitative variable is a scatterplot. Statistical studies often try to show through scatterplots, that changing one
More informationChapter 14. Inference for Regression Inference about the Model 14.1 Testing the Relationship Signi!cance Test Practice
Chapter 14 Inference for Regression Our!nal topic of the year involves inference for the regression model. In Chapter 3 we learned how to!nd the Least Squares Regression Line for a set of bivariate data.
More informationANALYZING BIVARIATE DATA
Analyzing bivariate data 1 ANALYZING BIVARIATE DATA Lesson 1: Creating frequency tables LESSON 1: OPENER There are two types of data: categorical and numerical. Numerical data provide numeric measures
More informationSTOR 155 Section 2 Midterm Exam 1 (9/29/09)
STOR 155 Section 2 Midterm Exam 1 (9/29/09) Name: PID: Instructions: Both the exam and the bubble sheet will be collected. On the bubble sheet, print your name and ID number, sign the honor pledge, also
More information14.1: Inference about the Model
14.1: Inference about the Model! When a scatterplot shows a linear relationship between an explanatory x and a response y, we can use the LSRL fitted to the data to predict a y for a given x. However,
More informationRegression Equation. November 29, S10.3_3 Regression. Key Concept. Chapter 10 Correlation and Regression. Definitions
MAT 155 Statistical Analysis Dr. Claude Moore Cape Fear Community College Chapter 10 Correlation and Regression 10 1 Review and Preview 10 2 Correlation 10 3 Regression 10 4 Variation and Prediction Intervals
More informationINTERPRET SCATTERPLOTS
Chapter2 MODELING A BUSINESS 2.1: Interpret Scatterplots 2.2: Linear Regression 2.3: Supply and Demand 2.4: Fixed and Variable Expenses 2.5: Graphs of Expense and Revenue Functions 2.6: Breakeven Analysis
More informationUF#Stats#Club#STA#2023#Exam#1#Review#Packet# #Fall#2013#
UF#Stats#Club#STA##Exam##Review#Packet# #Fall## The following data consists of the scores the Gators basketball team scored during the 8 games played in the - season. 84 74 66 58 79 8 7 64 8 6 78 79 77
More informationSection 1.2 Displaying Quantitative Data with Graphs. Dotplots
Section 1.2 Displaying Quantitative Data with Graphs Dotplots One of the simplest graphs to construct and interpret is a dotplot. Each data value is shown as a dot above its location on a number line.
More informationLesson 2: Describing the Center of a Distribution
In previous work with data distributions, you learned how to derive the mean and the median of a data distribution. This lesson builds on your previous work with a center. Exploratory Challenge You will
More informationAnswer all three questions. All questions carry equal marks.
UNIVERSITY OF DUBLIN TRINITY COLLEGE Faculty of Engineering, Mathematics and Science School of Computer Science and Statistics Postgraduate Diploma in Statistics Trinity Term 2 Introduction to Regression
More information3. For a $5 lunch with a 55 cent ($0.55) tip, what is the value of the residual?
STATISTICS 216, SPRING 2006 Name: EXAM 1; February 21, 2006; 100 points. Instructions: Closed book. Closed notes. Calculator allowed. Double-sided exam. NO CELL PHONES. Multiple Choice (3pts each). Circle
More informationChapter 3: Describing Relationships
Chapter 3: Describing Relationships Objectives: Students will: Construct and interpret a scatterplot for a set of bivariate data. Compute and interpret the correlation, r, between two variables. Demonstrate
More informationLab 4 (M13) Objective: This lab will give you more practice exploring the shape of data, and in particular in breaking the data into two groups.
Lab 4 (M13) Objective: This lab will give you more practice exploring the shape of data, and in particular in breaking the data into two groups. Activity 1 Examining Data From Class Background Download
More informationUnit 8 Bivariate Data/ Scatterplots
Unit 8 Bivariate Data/ Scatterplots Oct 20 9:19 PM Scatterplots are used to determine if there is a relationship between two variables. /Correlation /Correlation /Correlation Line of best fit cuts the
More informationSemester 1 Final Scientific calculators are allowed, NO GRAPHING CALCULATORS. You must show all your work to receive full credit.
Algebra 1 Name: Semester 1 Final Scientific calculators are allowed, NO GRAPHING CALCULATORS. You must show all your work to receive full credit. (F.IF.2 DOK 1) (1 point) 1. Evaluate the function when
More informationBusiness Statistics Probability
Business Statistics The following was provided by Dr. Suzanne Delaney, and is a comprehensive review of Business Statistics. The workshop instructor will provide relevant examples during the Skills Assessment
More informationStat 13, Lab 11-12, Correlation and Regression Analysis
Stat 13, Lab 11-12, Correlation and Regression Analysis Part I: Before Class Objective: This lab will give you practice exploring the relationship between two variables by using correlation, linear regression
More informationLevel 3 AS Credits Internal Investigate Bivariate Measurement Data Written by Jake Wills MathsNZ
Level 3 AS91581 4 Credits Internal Investigate Bivariate Measurement Data Written by Jake Wills MathsNZ jwills@mathsnz.com NCEA Level 3-3.9 Bivariate Data Achievement Achievement with Merit Achievement
More informationThe North Carolina Health Data Explorer
The North Carolina Health Data Explorer The Health Data Explorer provides access to health data for North Carolina counties in an interactive, user-friendly atlas of maps, tables, and charts. It allows
More information5 To Invest or not to Invest? That is the Question.
5 To Invest or not to Invest? That is the Question. Before starting this lab, you should be familiar with these terms: response y (or dependent) and explanatory x (or independent) variables; slope and
More informationLesson Using Lines to Make Predictions
STTWY STUDENT HNDOUT STUDENT NME DTE INTRODUCTION Statistical methods are used in forensics to identify human remains based on the measurements of bones. In the 1950s, Dr. Mildred Trotter and Dr. Goldine
More informationPre-Test Unit 9: Descriptive Statistics
Pre-Test Unit 9: Descriptive Statistics You may use a calculator. The following table shows how many text messages different students sent this week. Answer the following questions using the table. 20
More informationREVIEW PROBLEMS FOR FIRST EXAM
M358K Sp 6 REVIEW PROBLEMS FOR FIRST EXAM Please Note: This review sheet is not intended to tell you what will or what will not be on the exam. However, most of these problems have appeared on or are very
More informationCHAPTER ONE CORRELATION
CHAPTER ONE CORRELATION 1.0 Introduction The first chapter focuses on the nature of statistical data of correlation. The aim of the series of exercises is to ensure the students are able to use SPSS to
More informationMiSP Solubility Lab L3
MiSP Solubility Lab L3 Name Date In today s lab you will be working in groups to determine whether sugar or salt dissolves more quickly in water. The rate at which different substances dissolve depends
More informationMEASURES OF ASSOCIATION AND REGRESSION
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 816 MEASURES OF ASSOCIATION AND REGRESSION I. AGENDA: A. Measures of association B. Two variable regression C. Reading: 1. Start Agresti
More informationProblem Set 3 ECN Econometrics Professor Oscar Jorda. Name. ESSAY. Write your answer in the space provided.
Problem Set 3 ECN 140 - Econometrics Professor Oscar Jorda Name ESSAY. Write your answer in the space provided. 1) Sir Francis Galton, a cousin of James Darwin, examined the relationship between the height
More informationThe Jumping Dog Quadratic Activity
Standards: The Jumping Dog Quadratic Activity A2.4.1 Identify the family of function best suited for modeling a given real-world situation. A2.4.3 Using the adapted general symbolic form, draw reasonable
More informationFurther Mathematics 2018 CORE: Data analysis Chapter 3 Investigating associations between two variables
Chapter 3: Investigating associations between two variables Further Mathematics 2018 CORE: Data analysis Chapter 3 Investigating associations between two variables Extract from Study Design Key knowledge
More informationChapter 4. Navigating. Analysis. Data. through. Exploring Bivariate Data. Navigations Series. Grades 6 8. Important Mathematical Ideas.
Navigations Series Navigating through Analysis Data Grades 6 8 Chapter 4 Exploring Bivariate Data Important Mathematical Ideas Copyright 2009 by the National Council of Teachers of Mathematics, Inc. www.nctm.org.
More informationPearson Education Limited Edinburgh Gate Harlow Essex CM20 2JE England and Associated Companies throughout the world
Pearson Education Limited Edinburgh Gate Harlow Essex CM20 2JE England and Associated Companies throughout the world Visit us on the World Wide Web at: www.pearsoned.co.uk Pearson Education Limited 2014
More information10/4/2007 MATH 171 Name: Dr. Lunsford Test Points Possible
Pledge: 10/4/2007 MATH 171 Name: Dr. Lunsford Test 1 100 Points Possible I. Short Answer and Multiple Choice. (36 points total) 1. Circle all of the items below that are measures of center of a distribution:
More informationSTATISTICS 201. Survey: Provide this Info. How familiar are you with these? Survey, continued IMPORTANT NOTE. Regression and ANOVA 9/29/2013
STATISTICS 201 Survey: Provide this Info Outline for today: Go over syllabus Provide requested information on survey (handed out in class) Brief introduction and hands-on activity Name Major/Program Year
More informationBIVARIATE DATA ANALYSIS
BIVARIATE DATA ANALYSIS Sometimes, statistical studies are done where data is collected on two variables instead of one in order to establish whether there is a relationship between the two variables.
More informationHomework 2 Math 11, UCSD, Winter 2018 Due on Tuesday, 23rd January
PID: Last Name, First Name: Section: Approximate time spent to complete this assignment: hour(s) Readings: Chapters 7, 8 and 9. Homework 2 Math 11, UCSD, Winter 2018 Due on Tuesday, 23rd January Exercise
More information1. To review research methods and the principles of experimental design that are typically used in an experiment.
Your Name: Section: 36-201 INTRODUCTION TO STATISTICAL REASONING Computer Lab Exercise Lab #7 (there was no Lab #6) Treatment for Depression: A Randomized Controlled Clinical Trial Objectives: 1. To review
More information3.4 What are some cautions in analyzing association?
3.4 What are some cautions in analyzing association? Objectives Extrapolation Outliers and Influential Observations Correlation does not imply causation Lurking variables and confounding Simpson s Paradox
More informationLesson 1: Distributions and Their Shapes
Lesson 1 Lesson 1: Distributions and Their Shapes Classwork Statistics is all about data Without data to talk about or to analyze or to question, statistics would not exist There is a story to be uncovered
More informationCorrelation & Regression Exercises Chapters 14-15
Correlation & Regression Exercises Chapters 14-15 1. Which of these are true and which are false? Explain why the false statements are wrong. a. If the slope of the line is 1, then the correlation must
More informationUSING STATCRUNCH TO CONSTRUCT CONFIDENCE INTERVALS and CALCULATE SAMPLE SIZE
USING STATCRUNCH TO CONSTRUCT CONFIDENCE INTERVALS and CALCULATE SAMPLE SIZE Using StatCrunch for confidence intervals (CI s) is super easy. As you can see in the assignments, I cover 9.2 before 9.1 because
More informationIAPT: Regression. Regression analyses
Regression analyses IAPT: Regression Regression is the rather strange name given to a set of methods for predicting one variable from another. The data shown in Table 1 and come from a student project
More informationSection 3.2 Least-Squares Regression
Section 3.2 Least-Squares Regression Linear relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these relationships.
More informationEating and Sleeping Habits of Different Countries
9.2 Analyzing Scatter Plots Now that we know how to draw scatter plots, we need to know how to interpret them. A scatter plot graph can give us lots of important information about how data sets are related
More informationMath 124: Module 2, Part II
, Part II David Meredith Department of Mathematics San Francisco State University September 15, 2009 What we will do today 1 Explanatory and Response Variables When you study the relationship between two
More informationStatistics and Probability
Statistics and a single count or measurement variable. S.ID.1: Represent data with plots on the real number line (dot plots, histograms, and box plots). S.ID.2: Use statistics appropriate to the shape
More informationbivariate analysis: The statistical analysis of the relationship between two variables.
bivariate analysis: The statistical analysis of the relationship between two variables. cell frequency: The number of cases in a cell of a cross-tabulation (contingency table). chi-square (χ 2 ) test for
More informationBiology 345: Biometry Fall 2005 SONOMA STATE UNIVERSITY Lab Exercise 5 Residuals and multiple regression Introduction
Biology 345: Biometry Fall 2005 SONOMA STATE UNIVERSITY Lab Exercise 5 Residuals and multiple regression Introduction In this exercise, we will gain experience assessing scatterplots in regression and
More informationINTERMEDIATE ALGEBRA Review for Exam 3
INTERMEDIATE ALGEBRA Review for Eam 3 Consider the polnomials below. Answer the questions. 1) a) -163 + 6 + 34-2 - 82 b) u + 9u9v2 + u4v3 + 7u + 4v6 i) Determine the degree of each term of the polnomial.
More informationMULTIPLE LINEAR REGRESSION 24.1 INTRODUCTION AND OBJECTIVES OBJECTIVES
24 MULTIPLE LINEAR REGRESSION 24.1 INTRODUCTION AND OBJECTIVES In the previous chapter, simple linear regression was used when you have one independent variable and one dependent variable. This chapter
More informationPart I: Alcohol Metabolization Explore and Explain
Name Date Part I: Alcohol Metabolization Explore and Explain Just like any other type of food or beverage, alcohol is digested and then metabolized by the body. When a substance is metabolized by the body,
More informationMean Absolute Deviation (MAD) Statistics 7.SP.3, 7.SP.4
Mean Absolute Deviation (MAD) Statistics 7.SP.3, 7.SP.4 Review Let s Begin The Mean Absolute Deviation (MAD) of a set of data. is the average distance between each data value and the mean. 1. Find the
More informationM 140 Test 1 A Name (1 point) SHOW YOUR WORK FOR FULL CREDIT! Problem Max. Points Your Points Total 75
M 140 est 1 A Name (1 point) SHOW YOUR WORK FOR FULL CREDI! Problem Max. Points Your Points 1-10 10 11 10 12 3 13 4 14 18 15 8 16 7 17 14 otal 75 Multiple choice questions (1 point each) For questions
More informationVitruvian Man Meets the Scientific Method Writing and Testing Appropriate Hypotheses
Vitruvian Man Meets the Scientific Method Writing and Testing Appropriate Hypotheses Leonardo da Vinci s drawing Vitruvian Man shows how the proportions of the human body fit perfectly into a circle or
More informationBouncing Ball Lab. Name
Bouncing Ball Lab Name Scientists use an organized set of steps when they solve problems or perform investigations. This organized set of steps is called the Scientific Method. There are many versions
More information2.75: 84% 2.5: 80% 2.25: 78% 2: 74% 1.75: 70% 1.5: 66% 1.25: 64% 1.0: 60% 0.5: 50% 0.25: 25% 0: 0%
Capstone Test (will consist of FOUR quizzes and the FINAL test grade will be an average of the four quizzes). Capstone #1: Review of Chapters 1-3 Capstone #2: Review of Chapter 4 Capstone #3: Review of
More information7. Bivariate Graphing
1 7. Bivariate Graphing Video Link: https://www.youtube.com/watch?v=shzvkwwyguk&index=7&list=pl2fqhgedk7yyl1w9tgio8w pyftdumgc_j Section 7.1: Converting a Quantitative Explanatory Variable to Categorical
More information3.2 Least- Squares Regression
3.2 Least- Squares Regression Linear (straight- line) relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Statistics Final Review Semeter I Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) The Centers for Disease
More informationChoosing a Significance Test. Student Resource Sheet
Choosing a Significance Test Student Resource Sheet Choosing Your Test Choosing an appropriate type of significance test is a very important consideration in analyzing data. If an inappropriate test is
More informationPractice First Midterm Exam
Practice First Midterm Exam Statistics 200 (Pfenning) This is a closed book exam worth 150 points. You are allowed to use a calculator and a two-sided sheet of notes. There are 9 problems, with point values
More informationWhat Do You Think? For You To Do GOALS. The men s high jump record is over 8 feet.
Activity 5 Run and Jump GOALS In this activity you will: Understand the definition of acceleration. Understand meters per second per second as the unit of acceleration. Use an accelerometer to detect acceleration.
More informationIntroduction to regression
Introduction to regression Regression describes how one variable (response) depends on another variable (explanatory variable). Response variable: variable of interest, measures the outcome of a study
More informationOrdinary Least Squares Regression
Ordinary Least Squares Regression March 2013 Nancy Burns (nburns@isr.umich.edu) - University of Michigan From description to cause Group Sample Size Mean Health Status Standard Error Hospital 7,774 3.21.014
More informationExemplar for Internal Assessment Resource Mathematics Level 3. Resource title: Sport Science. Investigate bivariate measurement data
Exemplar for internal assessment resource Mathematics 3.9A for Achievement Standard 91581 Exemplar for Internal Assessment Resource Mathematics Level 3 Resource title: Sport Science This exemplar supports
More informationRegression CHAPTER SIXTEEN NOTE TO INSTRUCTORS OUTLINE OF RESOURCES
CHAPTER SIXTEEN Regression NOTE TO INSTRUCTORS This chapter includes a number of complex concepts that may seem intimidating to students. Encourage students to focus on the big picture through some of
More informationMultiple Choice Questions
ACTM State Statistics Work the multiple choice questions first, selecting the single best response from those provided and entering it on your scantron form. You may write on this test and keep the portion
More informationSCATTER PLOTS AND TREND LINES
1 SCATTER PLOTS AND TREND LINES LEARNING MAP INFORMATION STANDARDS 8.SP.1 Construct and interpret scatter s for measurement to investigate patterns of between two quantities. Describe patterns such as
More information1. The figure below shows the lengths in centimetres of fish found in the net of a small trawler.
Bivariate Data 1 IB MATHEMATICS SL Topic: Bivariate Data NAME: DATE: 1. The figure below shows the lengths in centimetres of fish found in the net of a small trawler. Number of fish 11 10 9 8 7 6 5 4 3
More informationSTAT 135 Introduction to Statistics via Modeling: Midterm II Thursday November 16th, Name:
STAT 135 Introduction to Statistics via Modeling: Midterm II Thursday November 16th, 2017 Name: 1 1 Short Answer a) For each of these five regression scenarios, name an appropriate visualization (along
More informationSection I: Multiple Choice Select the best answer for each question.
Chapter 1 AP Statistics Practice Test (TPS- 4 p78) Section I: Multiple Choice Select the best answer for each question. 1. You record the age, marital status, and earned income of a sample of 1463 women.
More informationSTAT445 Midterm Project1
STAT445 Midterm Project1 Executive Summary This report works on the dataset of Part of This Nutritious Breakfast! In this dataset, 77 different breakfast cereals were collected. The dataset also explores
More informationSection 6: Analysing Relationships Between Variables
6. 1 Analysing Relationships Between Variables Section 6: Analysing Relationships Between Variables Choosing a Technique The Crosstabs Procedure The Chi Square Test The Means Procedure The Correlations
More informationPreliminary Report on Simple Statistical Tests (t-tests and bivariate correlations)
Preliminary Report on Simple Statistical Tests (t-tests and bivariate correlations) After receiving my comments on the preliminary reports of your datasets, the next step for the groups is to complete
More informationSection 3 Correlation and Regression - Teachers Notes
The data are from the paper: Exploring Relationships in Body Dimensions Grete Heinz and Louis J. Peterson San José State University Roger W. Johnson and Carter J. Kerk South Dakota School of Mines and
More informationChapter 3, Section 1 - Describing Relationships (Scatterplots and Correlation)
Chapter 3, Section 1 - Describing Relationships (Scatterplots and Correlation) Investigating relationships between variables is central to what we do in statistics. Why is it important to investigate and
More information12.1 Inference for Linear Regression. Introduction
12.1 Inference for Linear Regression vocab examples Introduction Many people believe that students learn better if they sit closer to the front of the classroom. Does sitting closer cause higher achievement,
More information