A linear model was fit to predict weekly Sales of frozenpizza (in pounds) from the average Price ($/unit) chargedby a sample of stores in the city of Dallas in 39 recentweeks. The average Sales was 56,442 pounds(SD = 10,803 pounds), and the correlation between Priceand Sales was - 0.534. If the Price in a particular weekwas one SD lower than the mean Price, how much pizzawould you predict was sold that week?The predicted sales would beweek.(Round to the nearest integer as needed.)Apounds of pizza that

Solution-:Given: Average sales poundsSD=10803 poundsCorrelation (r) =-0.534We want to find if the…

Answered: A linear model was fit to predict…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx11.68.46.73.52.72.62.30.7yy1410.99.976.26.35.74.3 xx = thousands of automatic weaponsyy = murders per 100,000 residents This data can be modeled by the equation y=0.87x+3.83.y=0.87x+3.83. Use this equation to answer the following;A) How many murders per 100,000 residents can be expected in a state with 8.6 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 6.6 thousand automatic weapons?Answer = Round to 3 decimal places.
Below, we see a snippet of a dataset that consists of 128 observations and 15 variables. We present two models describing the linear relationship between the BodyFat and other variables. Density BodyFat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle 1 1.0708 12.3 23 154.25 85.2 94.5 59.0 37.3 21.9 2 1.0853 6.1 22 173.25 83.0 98.7 58.7 37.3 3 1.0414 87.9 99.2 59.6 38.9 4 1.0751 86.4 101.2 5 1.0340 100.0 101.9 63.2 42.2 6 1.0502 94.4 107.8 66.0 42.0 23.4 24.0 67.75 36.2 93.1 72.25 38.5 93.6 25.3 22 154.00 66.25 34.0 95.8 10.4 26 184.75 72.25 37.4 101.8 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 60.1 37.3 22.8 24.0 25.6 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 28.8 25.2 16.6 32.4 29.4 18.2 5 32.2 27.7 17.7 6 35.7 30.6 18.8 Analysis of Variance Table Model 1: x$BodyFat ~ x$Neck Model 2: x$BodyFat x$Neck + xSWeight + x$Height F Pr (>F) Res. Df RSS Df Sum of Sq 1 250 13348.1 2 248 9461.4 2 3886.7 50.939 < 2.2e-16 *** --- Signif. codes: 0***…
Suppose an oceanographer monitors the daily salinity of a particular ocean in relation to the temperature of the water. The oceanographer plots the data with temperature, in degrees Celsius (°C), along the horizontal axis and salinity, in parts per thousand (ppt), along the vertical axis. Select the true statement about the data point identified by the arrow. An ocean temperature of 21 °C causes the salinity to be 29 ppt. The predicted salinity is 29 ppt when the ocean temperature for the day is 21 °C. An ocean temperature of 29 °C corresponds to a salinity level of 21 ppt. The observed salinity is 29 ppt when the ocean temperature for the day is 21 °C. There is no relationship between the temperature of the ocean on a given day and salinity.
A linear model fit to predict weekly Sales of frozen pizza (in kilograms) from the average Price (in dollars per unit) charged by a sample of stores in 41 recent weeks is Sales = 141,870.25-24,445.32 Price. For the data used to create the model, the average Sales was 52,750 kilograms (SD = 10,282 kilograms) and the correlation between Price and Sales was r=-0.554. If the Price in a particular week was 2 SD higher than the mean Price, how much pizza would the model predict was sold that week? The model would predict that kilograms of frozen pizza were sold. (Round to the nearest integer as needed.)
A correlation of 0.95 for two variables X and Y implies that if X increases, there is a 95% chance that Y will also increase. Is this statement true or false?
ologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? Suppose the correlation coefficient was −0.12. How would you classify the correlation between temperature and amount of glucose?
An economist conducted a study of the possible association between weekly income and weekly grocery expenditures. The particular interest was whether higher income would result in shoppers spending more on groceries. A random sample of shoppers at a local supermarket was obtained, and a questionnaire was administered asking about the weekly income of the shopper’s family and the grocery bill for that week. The correlation was found to be r=0.675. The percentage (to two decimal places) of variation in grocery bills explained by the family's weekly income regression line is:
Q13What is the correlation between average monthly rainfall and crop yield in a given region over the past 10 years, and how does this relationship vary across different types of crops grown in the area?
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
For a sample of 41 California cities, a sociologist studies the crime rate in each city (crimes per 100,000 residents) as a function of its poverty rate (in %) and its median income (in $1,000s). The sociologist finds that SSE = 4,182,663 and SST= 7,732,451. Round all answers to 4 decimal places. What is the standard error of the estimate? What proportion of the sample variation in crime rate is explained by variability in the predictor variables? What proportion is unexplained?
Which of the following statements is sensible? A 102.4% of the total variation in a football player's weight is accounted for, or explained by its linear regression with the time spent practicing football. B. The correlation coefficient between a car's length and its fuel efficiency is7 es per gallon. C. There is a very strong direct linear correlation (0.95) between amount of d od consumption and the brand of dog food. D. The correlation coefficient between the amounts of fertilizer used and quantity of beans harvested is 0.42. OD O A OB
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.8 277.9 253.3 259.2 279.0 258.0 274.0 262.4 Height 1784.7 1771.2 1676.0 1645.9 1858.9 1710.1 1788.9 1737.0 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.8 mm is enter your response heremm. (Round to the nearest integer as needed.)

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