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Review Sheet for Chapter 3 Quizapolooza!NAME: __________________________Important skills/Terms to know:- Explanatory and Response Variables- Differences between association and causation- Describing Bivariate data (form, strength, pattern, outliers)- Outliers and their affects on regression lines- Least Squares Regression lines and interpretations of their parts- Residual plots and interpretations of residuals- Interpret r2 and s about how well a regression line fits a set of data- Calculating least squares regression line from correlation, means and standard deviations of two sets of data.1. Acrylamide is a chemical that appears in fried starchy foods that is thought to increase the risk of some cancers. The following data compared frying time to acrylamide concentration for six samples of French fries.#1#2#3#4#5#6Frying Time (seconds)150240240270300300Acrylamide conc (ug/kg)155120190185140270Sketch the scatterplot for this data and comment on the plot.Find the LSRL equation. Looking at your scatterplot in part (a), is there a potential outlier point? Which point? Determine if this is an influential point and explain your thinking.2. The following data was collected in Texas in 2004 on the ages of drivers vs. the fatality rate (percent of drivers killed in injury crashes). The authors made the following statement: “The correlation coefficient of r = 0.82 shows there is a strong correlation between driver’s age and fatality rates.”AgeFatality RateAgeFatality RateAgeFatality RateAgeFatality Rate201.00400.75601.15802.20250.99450.75651.20853.00300.80500.95701.30903.20350.80551.05751.65Calculate the equation for the LSRL and confirm the authors’ value for the correlation r. Do you think the equation in question a) is a good model for the data? Explain why or why not.3. (Multiple Choice Question) A least squares regression line (LSRL) has been found for a bivariate data set consisting of an explanatory variable x and a response variable y. Which of the following statements is true?a) The least squares regression line must pass through at least one data point (x, y) in the data set.b) If a value for x is given the predicted value for y can be calculated using the LSRL equation.c) Half the data points are above the LSRL line and half are below the LSRL line.d) If the response and explanatory variables are switched the equation for the LSRL remains the same.4. Data has been collected from the teachers at Leland on the gas mileage of the cars they drive and the age of car. A summary of the data is shown below.Age of Car (years)Gas Mileage (miles per gallon)Mean = 5.6Mean = 22.7Standard deviation = 2.4Standard deviation = 3.1Minimum = 0Minimum = 14.9Maximum = 13Maximum = 30.3r = - 0.52a. Calculate the equation of the Least Squares Regression Line (assume age is the explanatory variable and mileage the response variable).b. Interpret the meaning of the slope and the y-intercept (in words).c. Predict the mileage of a car that is 0, 10 and 20 years old.5. The following is output from a statistical software package. The data analyzed was the number of wins for major league baseball teams vs. their average home attendance per game.--------------------------------------------------------------------------------Dependent variable is: Home AttendanceR squared = 48.5%S = 9.277VariableCoefficientSE(Coeff)t-ratiop-valueIntercept-14364.55611-2.560.0082Wins538.91567.038.04<0.0001--------------------------------------------------------------------------------What is the correlation coefficient r?What is the equation for the Least Squares Regression Line? Identify any variables in the equation.Explain the significance of R squared.If a team won 75 of their games what would you predict their attendance to be?Assume the Detroit Tigers won 88 of their games and had an average attendance of 35,500 fans. What is the residual for this data point? ................
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