Minitab Statistics

Given the following Minitab output, which of the following is false?

• 80% of the variability in y is explained by the linear relationship with x.

• Since r=0.898, the linear relationship between x and y is strong, positive, and linear.

• As x increases by one unit, y decreases, on average, by 1.6914 units.

• The intercept of the least squares regression line is -0.868.

• The equation of the least squares regression line is y=-0.868-1.6914x.

To determine property taxes, Florida reappraises real estate every year, and the county appraiser's website lists the current "fair market value" of each piece of property. Property usually sells for somewhat more than the appraised market value. We collected data on the appraised market values x and the actual selling prices y (in thousands of dollars) of a random sample of 16 condominium units in Florida. We checked that the conditions for inference about the slope of the population regression line are met. Here is part of the Minitab output from a least-squares regression analysis using these data. The slope beta of the population regression line describes

• the exact increase in the selling price of an individual unit when its appraised value increases by $1000.

• the average increase in the appraised value in a population of units when selling prices increases by $1000.

• the average increase in selling prices in a population of units when appraised value increases by $1000.

• the average increase in the appraised value in the sample of units when selling price increases by $1000.

• the average increase in selling price in the sample of units when the appraised value increases by $1000.

The computer output below shows the result of a linear regression analysis for predicting the concentration of zinc, in parts per million (ppm), from the concentration of lead, in ppm, found in fish from a certain river. Which of the following statements is a correct interpretation of the value 19.0 in the output?

• On average there is a predicted increase of 19.0 ppm in concentration of lead for every increase of 1 ppm in concentration of zinc found in the fish.

• On average there is a predicted increase of 19.0 ppm in concentration of zinc for every increase of 1 ppm in concentration of lead found in the fish.

• The predicted concentration of zinc is 19.0 ppm in fish with no concentration of lead.

• The predicted concentration of lead is 19.0 ppm in fish with no concentration of zinc.

• Approximately 19% of the variability in the zinc concentration is predicted by its linear relationship with lead concentration.

The computer output is used to predict helicopter flight time from drop height.  Which is the correct equation for this relationship?

• (Time) = -0.03761+0.0057244(drop height)

• (time) = -0.03761+0.05838(drop height)

• (height)=-0.03761+0.0057244(time)

• (height)=0.0057244-0.03761(time)

Given the image of linear regression computer output, what would the correlation coefficient be?

• .922

• .96

• -0.03761

• 28.37

To determine property taxes, Florida reappraises real estate every year, and the county appraiser's website lists the current "fair market value" of each piece of property. Property usually sells for somewhat more than the appraised market value. We collected data on the appraised market values x and the actual selling prices y (in thousands of dollars) of a random sample of 16 condominium units in Florida. We checked that the conditions for inference about the slope of the population regression line are met. Here is part of the Minitab output from a least-squares regression analysis using these data. The equation of the least-squares regression line for predicting selling price from appraised value is

What is the equation of the LSRL

• y = 0.2 + 0.32x

• y = -0.97 + 175.3x

• y = 0.97 - 175.553x

• y = -175.554 + 0.097x

Is there a relationship between reaction time and age? A statistics student measures the reaction time of 12 subjects by measuring how far a yardstick drops, in inches, before a subject catches it between his or her thumb and forefinger (a high number thus indicates a slow reaction time). The student plans to use linear regression to see if reaction time can be predicted by the age of the subject. Output from a computer regression analysis is given below. The equation of the least-squares regression line is

• 
• 
• 

The following computer output describes the relationship between y = height (in cm) and x = foot length (also in cm) for 12 randomly selected students from the British Census @ Schools database. The scatterplot for this relationship show a roughly linear shape. Which of the following is an equation of least-squares regression line for these data?

• Height = 117.99 + 1.878 (Foot Length)

• Foot Length = 117.99 + 1.878 (Height)

• Height = 1.878 + 117.99 (Foot Length)

• Foot Length = 1.878 + 117.99 (Height)

What is the equation of the LSRL

• y = 3.367 - 0.062x

• y = 3.3 - 0.6x

• y = -0.062 + 3.3x

• y = 3.3 + 3.3x

Tags

CCSS.8.EE.C.8C