The following data show the curb weight, horsepower, and 1 4mile speed for 16 popular sports and GT cars. Suppose that the price of each sports and GT car is also available. The complete data set is as follows. Sports & GT Car Price ($1,000s) Curb Weight (lb.) Horsepower Speed at 1 4Mile (mph) Acura Integra Type R 25.035 2577 195 90.7 Acura NSX-T 93.758 3066 290 108.0 BMW Z3 2.8 40.900 2844 189 93.2 Chevrolet Camaro Z28 24.865 3439 305 103.2 Chevrolet Corvette Convertible 50.144 3246 345 102.1 Dodge Viper RT/10 69.742 3319 450 116.2 Ford Mustang GT 23.200 3227 225 91.7 Honda Prelude Type SH 26.382 3042 195 89.7 Mercedes-Benz CLK320 44.988 3240 215 93.0 Mercedes-Benz SLK230 42.762 3025 185 92.3 Mitsubishi 3000GT VR-4 47.518 3737 320 99.0 Nissan 240SX SE 25.066 2862 155 84.6 Pontiac Firebird Trans Am 27.770 3455 305 103.2 Porsche Boxster 45.560 2822 201 93.2 Toyota Supra Turbo 40.989 3505 320 105.0 Volvo C70 41.120 3285 236 97.0 (a) Find the estimated regression equation that uses price (in thousand dollars) and horsepower to predict 1 4mile speed (in mph). (Let x1 represent price, x2 represent horesepower, and y represent speed. Round your numerical values to two decimal places.) ŷ =

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Inverse Normal Distribution

The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.

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It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.

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A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.