Find the y-intercept of the equation of the regression line for the following data: Consumer Price Index (X) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of Pizza(Y) 0.15 0.35 1.00 1.25 1.75 2.00

The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers):   A.What would be the typical linear regression equation for the number of sales?    B.Make the sales forecast for period 15 of next year.    C. Make the sales forecast for period 17 of next year.      D. What is the standard error for the data?

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 273.3 mm. How does the result compare to the actual height of 1776 ​mm?   Foot Length 281.9 278.3 253.2 258.7 278.7 257.8 274.2 262.2   Height 1784.8 1771.0 1675.6 1645.9 1858.7 1710.1 1789.2 1737.4   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 273.3 mm is enter your response here mm. ​(Round to the nearest integer as​ needed.)

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 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (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 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.

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

Perform a linear regression analysis on the following data and determine the "a" coefficient (i.e., slope): Y 4.99 22.19 1.96 9.89 2.98 11 9 40.46 4.04 18.93 6.06 25 0.88 0.19 8.02 34.02 6.97 28.03

Which of the following statements best describes linear regression? A measure of the degree of dependence between two variables. A statistical technique using pairs of data and identifying a line which ‘best fits’ all of the pairs of observations. A measure of the proportion of changes in the dependent variable which can be explained by a change in the independent variable. Shows individual costs against their associated activity levels but makes no attempt to identify a linear relationship between them.

What is the explanatory variable,   what is the response variable. What is the regression equation for this data.   Plot the regression line in your calculator and draw it on your graph.   Interpret in context the slope.   Interpret in context the y-intercept.   What is the residual at 45 years of age.   Plot the residual plot on the axis to the right