Which of the following statements about a least-squares regression analysis is true? I. A point with a large residual is an outlier. II. A point with high leverage has a -value that is not consistent with the other -values in the set. III. The removal of an influential point from a data set could change the value of the correlation coefficient.

The rental  of an apartment (?R) near campus is a function of the square footage (??Sq). A random sample of apartments near campus yielded the following summary statistics: ?¯R¯ = $340, ??¯Sq¯   = 93, ??sR = $ 29.1, and ???sSq= 10.5. Suppose also that the correlation between price and weight is ?r = 0.83.   (a) Write the implied least squares linear regression equation. (b) Suppose an apartment has 75 sqft. Predict its price based on the above model. (c) Suppose the true rental of the apartment in part (b) is $ 345. What is the value of the residual?

Suppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers:  Area – area of the kitchen in square feet  Height – ceiling height in the kitchen (from floor to ceiling) in inches  Cabinets – number of cabinets in the kitchen  Suppose that a multiple linear regression model was fit to the data and that the following output resulted: Coefficients: (Intercept)HeightCabinets Estimate-57.98771.2760.3393 Std. Error8.63820.26430.1302 t value -6.7134.8282.607 Pr(>|t|)2.75e-074.44e-050.0145 What is the predicted area of a kitchen with a height of 96 inches and 10 cabinets? Report your answer to 1 decimal place.    square feet

The options for part b are: head circumference or height

Which of the following will help to mitigate confounding? A. Multiple regression modelB. RandomizationC. StratificationD. All of the above

Sir Francis Galton, in the late 1800s, was the first to introduce the statistical concepts of regression and correlation. He studied the relationships between pairs of varlables such as the size of parents and the size of their affspring. Data similar to that which he studied are given below, with the variable x denoting the height (in centimeters) of a human father and the variable y denoting the height at maturity (in centimeters) of the father's oldest son. The data are given in tabular form and also displayed in the Figure 1 scatter plot. Also given is the product of the father's height and the son's helght for each of the fifteen pairs. (These products, written in the column labelled "xy", may aid in calculations.) Height of Height of son, father, x y (in centimeters) centimeters) xy (in 192.7 190.6 36,728.62 174.8 179.2 31,324.16 210 158.5 175.3 27,785.05 200 160.9 167.7 26,982.93 180- 171.6 182.0 31,231.2 172.2 171.1 29,463.42 170 179.9 187.9 33,803.21 181.5 177.5 32,216.25 130_…

could tou write with pen and paper?

3. In some data sets, there are values that are far from the linear regression line. What are the data points called?

Seven North American Green Frogs (Rana clamitans) had their jumping distance recorded (in mm) multiple times in a laboratory. The mean jumping distance for these frogs along with their length (measured from snout to vent in miMillimeters) are presented in the table below. Length of Frog 52 68 37 65 77 81 59 Mean Jumping Distance  546 673 415 659 793 814 563   (a) Determine the linear regression model that will best predit the mean jumping distance of a North American Green Frog based on the frog's length. (b) How well does the linear regression model fit this sample data? (c) Use the linear regression model to predict the mean jumping distance of a North American Green Frog that is 48 mm in length. No excel, please.

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