Statistics: Correlation The table shows the relationship between the mass of a car and the amount of fuel consumed (km/liter) for some cars sold in the USA.  What is the correlation coefficient between mass and fuel consumed? Using linear regression, estimate the fuel consumption for a 2000 kg car. From the above information, what can you conclude about the relationship between the mass of a car and fuel consumption?

What is the dollar amount of change in sales for each new year according to the regression (slope) model?  Do not use commas or $ in your answer.

x = number of traffic tickets y = Cost of auto insurance A test of linear correlation between traffic tickets and insurance cost produces a linear correlation coefficient of r = 0.705.  Determine weather the regression line has a positive slope, or a negative slope

Prices and mileage of 48 Dodge 2500 Pickup trucks with Cummins diesel engines.  Prices ranged from $28,925 to $68,650.  Mileage ranged from 315 to 186,466.   While I did not share the trucks were 2013 to 2020 models. Please show all work.  Interpret b0 in the context of this problem.  Interpret b1 in the context of this problem.  What percent of total variation in prices is explained by the regression model?  Provide evidence to support your answer. Price Mileage 52293 34450 50987 23372 62988 21738 37500 81176 66000 23118 47601 71143 47750 18214 42000 32303 50987 23372 28925 143737 42050 58561 51738 9691 51500 23969 44988 48781 29500 114809 46975 16918 63999 15507 46975 16918 43222 50459 38386 87192 42000 68722 62250 14042 62998 21738 32130 103644 52293 34450 52900 68883 53172 3656 53494 315 39594 88441 63246 23631 58250 34974 59391 18383 50650 42737 54976 84200 68650 14401 51114 47433 43900 74106 37450…

Determine if correlation between the two given variables is likely to be positive or negative, or if they are not likely to display a linear relationship.  Your daily calorie intake and weight. (a) Positive (b) Negative (c) No correlation

Demand for Smartphones The following table shows worldwide sales of a type of phone and their average selling prices in 2012, 2013, and 2017. Year 2012 2013 2017 Selling Price p ($100) 4 Sales q (billions) 0.5 3 1 2 2 Find the regression line (round coefficients to one decimal place). q(p) = 1.0166 X Use the regression line to estimate the demand (in millions of units sold) when the selling price was $320. 1016.6 million

Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion).  Use the horizontal axis for the customer age to graph.  Find the linear regression line that models the data by each type of customer.  Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales.  What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y  / Change in x

In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67  the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?