An amusement park sales director is interested in predicting the number of beverages purchased based on the daily temperature so she can assign staff accordingly. The director already knows that there is a significant relationship between number of beverages purchased and the daily temperature. 1 Should the amusement park sales director use a correlation coefficient or regression to predict the number of beverages purchased from the daily temperature and why?   2 What variable would you assign to X and what variable would you assign to Y? 3 What are the terms for the X and Y variables?

7. The table below shows the round-trip fare for flights from one city on major airlines. These were the lowest prices found online in early 2010. The airlines varied, but the travel dates were all the same. Determine the linear regression for the cost of a round-trip ticket given the distance traveled, use your regression to estimate the cost of a 1500-mi flight, and state the correlation coefficient, r, and describe the trend, shape, and strength for the data contained in this problem. Distance (mi.) Cost ($) 205 183 968 180 3097 598 1939 277 1976 313 1033 318 2977 448 1752 408 1143 335

The equation of a regression line, unlike the correlation, depends on the units we use to measure the explanatory and response variables. Here is the data on percent body fat and preferred amount of salt. Preferred amountof salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 21 30 23 30 39 24 31 In calculating the preferred amount of salt, the weight of the salt was in milligrams. (a) Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in milligrams. (Round your answers to one decimal place.) ŷ =  +  x (b) A mad scientist decides to measure weight in tenths of milligrams. The same data in these units are as follows. Preferred amountof salt x 2 3 4 5 6 8 11 Percent body fat y 21 30 23 30 39 24 31 Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in tenths of milligrams. (Round your intercept to one decimal place and your slope to two…

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.)

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1 2.5 3 4 5 5.5 Overall Grades 98 95 90 79 75 69 66   Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.   Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.   Step 3 of 6: Determine if the statement "Not all points predicted by the linear model fall on the…

I need help phrasing a quantitative research question. The topic is on Health, it is focused on Widowed Americans as the dependent variable, and their quality of life (Excellent - Poor)as the independent variable. An overlapping variable would be sex (male - female).

you are going to  buy some calves. What should a healthy weight be? let  x be the age of the calf (in weeks), and let y be the weight of the calf (in kg) the calves you want to buy are 12 weeks old. Predict the healthy weight you should expect. x=1           3      10         16        26       36 y=42        50     75       100      150      200 state the correlation coefficient and regression line equation.

A study by Judge and Cable (2010) suggests that there is a positive relationship between weight and income for a group of men. The following data is similar to what was collected in the study. To simplify the weight variable, the men are categorized into five categories that measure actual weight relative to height from 1=thinnest to 5=heaviest. Income is recorded as thousands earned annually. Weight (X) Income (Y) 4 151 5 88 3 52 2 73 1 49 3 92 1 56 5 143 a. Calculate the Pearson correlation for these data. b. Is the correlation statistically significant? Use a two-tailed test with a=.05.

. Determine the regression equation using values you create for x and y for at least 10 pairs of data.  Show the regression equation, correlation coefficient, and coefficient of determination.  Then switch the x and y values for each data point.  Based on that, again show the regression equation, correlation coefficient, and coefficient of determination.  Discuss the similarities and differences between the results.

Answer A B C